Performance improvements: Token reduction and enhanced summaries (#101)
* refactor: Reduce continuation prompt token usage by 95 lines Removed redundant instructions from continuation prompt that were originally added to mitigate a session continuity issue. That issue has since been resolved, making these detailed instructions unnecessary on every continuation. Changes: - Reduced continuation prompt from ~106 lines to ~11 lines (~95 line reduction) - Changed "User's Goal:" to "Next Prompt in Session:" (more accurate framing) - Removed redundant WHAT TO RECORD, WHEN TO SKIP, and OUTPUT FORMAT sections - Kept concise reminder: "Continue generating observations and progress summaries..." - Initial prompt still contains all detailed instructions Impact: - Significant token savings on every continuation prompt - Faster context injection with no loss of functionality - Instructions remain comprehensive in initial prompt Files modified: - src/sdk/prompts.ts (buildContinuationPrompt function) - plugin/scripts/worker-service.cjs (compiled output) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * refactor: Enhance observation and summary prompts for clarity and token efficiency * Enhance prompt clarity and instructions in prompts.ts - Added a reminder to think about instructions before starting work. - Simplified the continuation prompt instruction by removing "for this ongoing session." * feat: Enhance settings.json with permissions and deny access to sensitive files refactor: Remove PLAN-full-observation-display.md and PR_SUMMARY.md as they are no longer needed chore: Delete SECURITY_SUMMARY.md since it is redundant after recent changes fix: Update worker-service.cjs to streamline observation generation instructions cleanup: Remove src-analysis.md and src-tree.md for a cleaner codebase refactor: Modify prompts.ts to clarify instructions for memory processing * refactor: Remove legacy worker service implementation * feat: Enhance summary hook to extract last assistant message and improve logging - Added function to extract the last assistant message from the transcript. - Updated summary hook to include last assistant message in the summary request. - Modified SDKSession interface to store last assistant message. - Adjusted buildSummaryPrompt to utilize last assistant message for generating summaries. - Updated worker service and session manager to handle last assistant message in summarize requests. - Introduced silentDebug utility for improved logging and diagnostics throughout the summary process. * docs: Add comprehensive implementation plan for ROI metrics feature Added detailed implementation plan covering: - Token usage capture from Agent SDK - Database schema changes (migration #8) - Discovery cost tracking per observation - Context hook display with ROI metrics - Testing and rollout strategy Timeline: ~20 hours over 4 days Goal: Empirical data for YC application amendment 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: Add transcript processing scripts for analysis and formatting - Implemented `dump-transcript-readable.ts` to generate a readable markdown dump of transcripts, excluding certain entry types. - Created `extract-rich-context-examples.ts` to extract and showcase rich context examples from transcripts, highlighting user requests and assistant reasoning. - Developed `format-transcript-context.ts` to format transcript context into a structured markdown format for improved observation generation. - Added `test-transcript-parser.ts` for validating data extraction from transcript JSONL files, including statistics and error reporting. - Introduced `transcript-to-markdown.ts` for a complete representation of transcript data in markdown format, showing all context data. - Enhanced type definitions in `transcript.ts` to support new features and ensure type safety. - Built `transcript-parser.ts` to handle parsing of transcript JSONL files, including error handling and data extraction methods. * Refactor hooks and SDKAgent for improved observation handling - Updated `new-hook.ts` to clean user prompts by stripping leading slashes for better semantic clarity. - Enhanced `save-hook.ts` to include additional tools in the SKIP_TOOLS set, preventing unnecessary observations from certain command invocations. - Modified `prompts.ts` to change the structure of observation prompts, emphasizing the observational role and providing a detailed XML output format for observations. - Adjusted `SDKAgent.ts` to enforce stricter tool usage restrictions, ensuring the memory agent operates solely as an observer without any tool access. * feat: Enhance session initialization to accept user prompts and prompt numbers - Updated `handleSessionInit` in `worker-service.ts` to extract `userPrompt` and `promptNumber` from the request body and pass them to `initializeSession`. - Modified `initializeSession` in `SessionManager.ts` to handle optional `currentUserPrompt` and `promptNumber` parameters. - Added logic to update the existing session's `userPrompt` and `lastPromptNumber` if a `currentUserPrompt` is provided. - Implemented debug logging for session initialization and updates to track user prompts and prompt numbers. --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Alex Newman
@@ -0,0 +1,424 @@
|
||||
"""Pydantic models for Claude Code transcript JSON structures.
|
||||
|
||||
Enhanced to leverage official Anthropic types where beneficial.
|
||||
"""
|
||||
|
||||
from typing import Any, List, Union, Optional, Dict, Literal, cast
|
||||
from pydantic import BaseModel
|
||||
|
||||
from anthropic.types import Message as AnthropicMessage
|
||||
from anthropic.types import StopReason
|
||||
from anthropic.types import Usage as AnthropicUsage
|
||||
from anthropic.types.content_block import ContentBlock
|
||||
|
||||
|
||||
class TodoItem(BaseModel):
|
||||
id: str
|
||||
content: str
|
||||
status: Literal["pending", "in_progress", "completed"]
|
||||
priority: Literal["high", "medium", "low"]
|
||||
|
||||
|
||||
class UsageInfo(BaseModel):
|
||||
"""Token usage information that extends Anthropic's Usage type to handle optional fields."""
|
||||
|
||||
input_tokens: Optional[int] = None
|
||||
cache_creation_input_tokens: Optional[int] = None
|
||||
cache_read_input_tokens: Optional[int] = None
|
||||
output_tokens: Optional[int] = None
|
||||
service_tier: Optional[str] = None
|
||||
server_tool_use: Optional[Dict[str, Any]] = None
|
||||
|
||||
def to_anthropic_usage(self) -> Optional[AnthropicUsage]:
|
||||
"""Convert to Anthropic Usage type if both required fields are present."""
|
||||
if self.input_tokens is not None and self.output_tokens is not None:
|
||||
return AnthropicUsage(
|
||||
input_tokens=self.input_tokens,
|
||||
output_tokens=self.output_tokens,
|
||||
cache_creation_input_tokens=self.cache_creation_input_tokens,
|
||||
cache_read_input_tokens=self.cache_read_input_tokens,
|
||||
service_tier=self.service_tier, # type: ignore
|
||||
server_tool_use=self.server_tool_use, # type: ignore
|
||||
)
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def from_anthropic_usage(cls, usage: AnthropicUsage) -> "UsageInfo":
|
||||
"""Create UsageInfo from Anthropic Usage."""
|
||||
return cls(
|
||||
input_tokens=usage.input_tokens,
|
||||
output_tokens=usage.output_tokens,
|
||||
cache_creation_input_tokens=usage.cache_creation_input_tokens,
|
||||
cache_read_input_tokens=usage.cache_read_input_tokens,
|
||||
service_tier=usage.service_tier,
|
||||
server_tool_use=usage.server_tool_use.model_dump()
|
||||
if usage.server_tool_use
|
||||
else None,
|
||||
)
|
||||
|
||||
|
||||
class TextContent(BaseModel):
|
||||
type: Literal["text"]
|
||||
text: str
|
||||
|
||||
|
||||
class ToolUseContent(BaseModel):
|
||||
type: Literal["tool_use"]
|
||||
id: str
|
||||
name: str
|
||||
input: Dict[str, Any]
|
||||
|
||||
|
||||
class ToolResultContent(BaseModel):
|
||||
type: Literal["tool_result"]
|
||||
tool_use_id: str
|
||||
content: Union[str, List[Dict[str, Any]]]
|
||||
is_error: Optional[bool] = None
|
||||
|
||||
|
||||
class ThinkingContent(BaseModel):
|
||||
type: Literal["thinking"]
|
||||
thinking: str
|
||||
signature: Optional[str] = None
|
||||
|
||||
|
||||
class ImageSource(BaseModel):
|
||||
type: Literal["base64"]
|
||||
media_type: str
|
||||
data: str
|
||||
|
||||
|
||||
class ImageContent(BaseModel):
|
||||
type: Literal["image"]
|
||||
source: ImageSource
|
||||
|
||||
|
||||
# Enhanced ContentItem to include official Anthropic ContentBlock types
|
||||
ContentItem = Union[
|
||||
TextContent,
|
||||
ToolUseContent,
|
||||
ToolResultContent,
|
||||
ThinkingContent,
|
||||
ImageContent,
|
||||
ContentBlock, # Official Anthropic content block types
|
||||
]
|
||||
|
||||
|
||||
class UserMessage(BaseModel):
|
||||
role: Literal["user"]
|
||||
content: Union[str, List[ContentItem]]
|
||||
|
||||
|
||||
class AssistantMessage(BaseModel):
|
||||
"""Assistant message model compatible with Anthropic's Message type."""
|
||||
|
||||
id: str
|
||||
type: Literal["message"]
|
||||
role: Literal["assistant"]
|
||||
model: str
|
||||
content: List[ContentItem]
|
||||
stop_reason: Optional[StopReason] = None
|
||||
stop_sequence: Optional[str] = None
|
||||
usage: Optional[UsageInfo] = None
|
||||
|
||||
@classmethod
|
||||
def from_anthropic_message(
|
||||
cls, anthropic_msg: AnthropicMessage
|
||||
) -> "AssistantMessage":
|
||||
"""Create AssistantMessage from official Anthropic Message."""
|
||||
# Convert Anthropic Message to our format, preserving official types where possible
|
||||
return cls(
|
||||
id=anthropic_msg.id,
|
||||
type=anthropic_msg.type,
|
||||
role=anthropic_msg.role,
|
||||
model=anthropic_msg.model,
|
||||
content=list(
|
||||
anthropic_msg.content
|
||||
), # Convert to list for ContentItem compatibility
|
||||
stop_reason=anthropic_msg.stop_reason,
|
||||
stop_sequence=anthropic_msg.stop_sequence,
|
||||
usage=normalize_usage_info(anthropic_msg.usage),
|
||||
)
|
||||
|
||||
|
||||
class FileInfo(BaseModel):
|
||||
filePath: str
|
||||
content: str
|
||||
numLines: int
|
||||
startLine: int
|
||||
totalLines: int
|
||||
|
||||
|
||||
class FileReadResult(BaseModel):
|
||||
type: Literal["text"]
|
||||
file: FileInfo
|
||||
|
||||
|
||||
class CommandResult(BaseModel):
|
||||
stdout: str
|
||||
stderr: str
|
||||
interrupted: bool
|
||||
isImage: bool
|
||||
|
||||
|
||||
class TodoResult(BaseModel):
|
||||
oldTodos: List[TodoItem]
|
||||
newTodos: List[TodoItem]
|
||||
|
||||
|
||||
class EditResult(BaseModel):
|
||||
oldString: Optional[str] = None
|
||||
newString: Optional[str] = None
|
||||
replaceAll: Optional[bool] = None
|
||||
originalFile: Optional[str] = None
|
||||
structuredPatch: Optional[Any] = None
|
||||
userModified: Optional[bool] = None
|
||||
|
||||
|
||||
ToolUseResult = Union[
|
||||
str,
|
||||
List[TodoItem],
|
||||
FileReadResult,
|
||||
CommandResult,
|
||||
TodoResult,
|
||||
EditResult,
|
||||
List[ContentItem],
|
||||
]
|
||||
|
||||
|
||||
class BaseTranscriptEntry(BaseModel):
|
||||
parentUuid: Optional[str]
|
||||
isSidechain: bool
|
||||
userType: str
|
||||
cwd: str
|
||||
sessionId: str
|
||||
version: str
|
||||
uuid: str
|
||||
timestamp: str
|
||||
isMeta: Optional[bool] = None
|
||||
|
||||
|
||||
class UserTranscriptEntry(BaseTranscriptEntry):
|
||||
type: Literal["user"]
|
||||
message: UserMessage
|
||||
toolUseResult: Optional[ToolUseResult] = None
|
||||
|
||||
|
||||
class AssistantTranscriptEntry(BaseTranscriptEntry):
|
||||
type: Literal["assistant"]
|
||||
message: AssistantMessage
|
||||
requestId: Optional[str] = None
|
||||
|
||||
|
||||
class SummaryTranscriptEntry(BaseModel):
|
||||
type: Literal["summary"]
|
||||
summary: str
|
||||
leafUuid: str
|
||||
cwd: Optional[str] = None
|
||||
|
||||
|
||||
class SystemTranscriptEntry(BaseTranscriptEntry):
|
||||
"""System messages like warnings, notifications, etc."""
|
||||
|
||||
type: Literal["system"]
|
||||
content: str
|
||||
level: Optional[str] = None # e.g., "warning", "info", "error"
|
||||
|
||||
|
||||
class QueueOperationTranscriptEntry(BaseModel):
|
||||
"""Queue operations (enqueue/dequeue) for message queueing tracking.
|
||||
|
||||
These are internal operations that track when messages are queued and dequeued.
|
||||
They are parsed but not rendered, as the content duplicates actual user messages.
|
||||
"""
|
||||
|
||||
type: Literal["queue-operation"]
|
||||
operation: Literal["enqueue", "dequeue"]
|
||||
timestamp: str
|
||||
sessionId: str
|
||||
content: Optional[List[ContentItem]] = None # Only present for enqueue operations
|
||||
|
||||
|
||||
TranscriptEntry = Union[
|
||||
UserTranscriptEntry,
|
||||
AssistantTranscriptEntry,
|
||||
SummaryTranscriptEntry,
|
||||
SystemTranscriptEntry,
|
||||
QueueOperationTranscriptEntry,
|
||||
]
|
||||
|
||||
|
||||
def normalize_usage_info(usage_data: Any) -> Optional[UsageInfo]:
|
||||
"""Normalize usage data to be compatible with both custom and Anthropic formats."""
|
||||
if usage_data is None:
|
||||
return None
|
||||
|
||||
# If it's already a UsageInfo instance, return as-is
|
||||
if isinstance(usage_data, UsageInfo):
|
||||
return usage_data
|
||||
|
||||
# If it's an Anthropic Usage instance, convert using our method
|
||||
if isinstance(usage_data, AnthropicUsage):
|
||||
return UsageInfo.from_anthropic_usage(usage_data)
|
||||
|
||||
# If it has the shape of an Anthropic Usage, try to construct it first
|
||||
if hasattr(usage_data, "input_tokens") and hasattr(usage_data, "output_tokens"):
|
||||
try:
|
||||
# Try to create an Anthropic Usage first
|
||||
anthropic_usage = AnthropicUsage.model_validate(usage_data)
|
||||
return UsageInfo.from_anthropic_usage(anthropic_usage)
|
||||
except Exception:
|
||||
# Fall back to direct conversion
|
||||
return UsageInfo(
|
||||
input_tokens=getattr(usage_data, "input_tokens", None),
|
||||
cache_creation_input_tokens=getattr(
|
||||
usage_data, "cache_creation_input_tokens", None
|
||||
),
|
||||
cache_read_input_tokens=getattr(
|
||||
usage_data, "cache_read_input_tokens", None
|
||||
),
|
||||
output_tokens=getattr(usage_data, "output_tokens", None),
|
||||
service_tier=getattr(usage_data, "service_tier", None),
|
||||
server_tool_use=getattr(usage_data, "server_tool_use", None),
|
||||
)
|
||||
|
||||
# If it's a dict, validate and convert to our format
|
||||
if isinstance(usage_data, dict):
|
||||
return UsageInfo.model_validate(usage_data)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def parse_content_item(item_data: Dict[str, Any]) -> ContentItem:
|
||||
"""Parse a content item using enhanced approach with Anthropic types."""
|
||||
try:
|
||||
content_type = item_data.get("type", "")
|
||||
|
||||
# Try official Anthropic types first for better future compatibility
|
||||
if content_type == "text":
|
||||
try:
|
||||
from anthropic.types.text_block import TextBlock
|
||||
|
||||
return TextBlock.model_validate(item_data)
|
||||
except Exception:
|
||||
return TextContent.model_validate(item_data)
|
||||
elif content_type == "tool_use":
|
||||
try:
|
||||
from anthropic.types.tool_use_block import ToolUseBlock
|
||||
|
||||
return ToolUseBlock.model_validate(item_data)
|
||||
except Exception:
|
||||
return ToolUseContent.model_validate(item_data)
|
||||
elif content_type == "thinking":
|
||||
try:
|
||||
from anthropic.types.thinking_block import ThinkingBlock
|
||||
|
||||
return ThinkingBlock.model_validate(item_data)
|
||||
except Exception:
|
||||
return ThinkingContent.model_validate(item_data)
|
||||
elif content_type == "tool_result":
|
||||
return ToolResultContent.model_validate(item_data)
|
||||
elif content_type == "image":
|
||||
return ImageContent.model_validate(item_data)
|
||||
else:
|
||||
# Fallback to text content for unknown types
|
||||
return TextContent(type="text", text=str(item_data))
|
||||
except Exception:
|
||||
return TextContent(type="text", text=str(item_data))
|
||||
|
||||
|
||||
def parse_message_content(content_data: Any) -> Union[str, List[ContentItem]]:
|
||||
"""Parse message content, handling both string and list formats."""
|
||||
if isinstance(content_data, str):
|
||||
return content_data
|
||||
elif isinstance(content_data, list):
|
||||
content_list = cast(List[Dict[str, Any]], content_data)
|
||||
return [parse_content_item(item) for item in content_list]
|
||||
else:
|
||||
return str(content_data)
|
||||
|
||||
|
||||
def parse_transcript_entry(data: Dict[str, Any]) -> TranscriptEntry:
|
||||
"""
|
||||
Parse a JSON dictionary into the appropriate TranscriptEntry type.
|
||||
|
||||
Enhanced to optionally use official Anthropic types for assistant messages.
|
||||
|
||||
Args:
|
||||
data: Dictionary parsed from JSON
|
||||
|
||||
Returns:
|
||||
The appropriate TranscriptEntry subclass
|
||||
|
||||
Raises:
|
||||
ValueError: If the data doesn't match any known transcript entry type
|
||||
"""
|
||||
entry_type = data.get("type")
|
||||
|
||||
if entry_type == "user":
|
||||
# Parse message content if present
|
||||
data_copy = data.copy()
|
||||
if "message" in data_copy and "content" in data_copy["message"]:
|
||||
data_copy["message"] = data_copy["message"].copy()
|
||||
data_copy["message"]["content"] = parse_message_content(
|
||||
data_copy["message"]["content"]
|
||||
)
|
||||
# Parse toolUseResult if present and it's a list of content items
|
||||
if "toolUseResult" in data_copy and isinstance(
|
||||
data_copy["toolUseResult"], list
|
||||
):
|
||||
# Check if it's a list of content items (MCP tool results)
|
||||
tool_use_result = cast(List[Any], data_copy["toolUseResult"])
|
||||
if (
|
||||
tool_use_result
|
||||
and isinstance(tool_use_result[0], dict)
|
||||
and "type" in tool_use_result[0]
|
||||
):
|
||||
data_copy["toolUseResult"] = [
|
||||
parse_content_item(cast(Dict[str, Any], item))
|
||||
for item in tool_use_result
|
||||
if isinstance(item, dict)
|
||||
]
|
||||
return UserTranscriptEntry.model_validate(data_copy)
|
||||
|
||||
elif entry_type == "assistant":
|
||||
# Enhanced assistant message parsing with optional Anthropic types
|
||||
data_copy = data.copy()
|
||||
|
||||
# Validate compatibility with official Anthropic Message type
|
||||
if "message" in data_copy:
|
||||
try:
|
||||
message_data = data_copy["message"]
|
||||
AnthropicMessage.model_validate(message_data)
|
||||
# Successfully validated - our data is compatible with official Anthropic types
|
||||
except Exception:
|
||||
# Validation failed - continue with standard parsing
|
||||
pass
|
||||
|
||||
# Standard parsing path (works for all cases)
|
||||
if "message" in data_copy and "content" in data_copy["message"]:
|
||||
message_copy = data_copy["message"].copy()
|
||||
message_copy["content"] = parse_message_content(message_copy["content"])
|
||||
|
||||
# Normalize usage data to support both Anthropic and custom formats
|
||||
if "usage" in message_copy:
|
||||
message_copy["usage"] = normalize_usage_info(message_copy["usage"])
|
||||
|
||||
data_copy["message"] = message_copy
|
||||
return AssistantTranscriptEntry.model_validate(data_copy)
|
||||
|
||||
elif entry_type == "summary":
|
||||
return SummaryTranscriptEntry.model_validate(data)
|
||||
|
||||
elif entry_type == "system":
|
||||
return SystemTranscriptEntry.model_validate(data)
|
||||
|
||||
elif entry_type == "queue-operation":
|
||||
# Parse content if present (only in enqueue operations)
|
||||
data_copy = data.copy()
|
||||
if "content" in data_copy and isinstance(data_copy["content"], list):
|
||||
data_copy["content"] = parse_message_content(data_copy["content"])
|
||||
return QueueOperationTranscriptEntry.model_validate(data_copy)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown transcript entry type: {entry_type}")
|
||||
@@ -0,0 +1,156 @@
|
||||
# Rich Context Examples
|
||||
|
||||
This document shows what contextual data is available in transcripts
|
||||
that could improve observation generation quality.
|
||||
|
||||
## Statistics
|
||||
|
||||
- Total entries: 369
|
||||
- User messages: 74
|
||||
- Assistant messages: 133
|
||||
- Token usage: 67,465 total
|
||||
- Cache efficiency: 6,979,410 tokens read from cache
|
||||
|
||||
## Conversation Flow
|
||||
|
||||
This shows how user requests, assistant reasoning, and tool executions flow together.
|
||||
This is the rich context currently missing from individual tool observations.
|
||||
|
||||
---
|
||||
|
||||
### Example 1
|
||||
|
||||
#### 👤 User Request
|
||||
```
|
||||
Thank you for that. So now that you have a very deep understanding of what we are doing here, I'd like you to begin working on the enhancements to our prompts that leverage data using the transcript model we discovered
|
||||
```
|
||||
|
||||
#### 🔧 Tools Executed (1)
|
||||
|
||||
**TodoWrite**
|
||||
```json
|
||||
{
|
||||
"todos": [
|
||||
{
|
||||
"content": "Read the ROI implementation plan to understand full scope",
|
||||
"status": "in_progress",
|
||||
"activeForm": "Reading ROI implementation plan"
|
||||
},
|
||||
{
|
||||
|
||||
```
|
||||
|
||||
**📊 Data Available for This Exchange:**
|
||||
- User intent: ✅ (218 chars)
|
||||
- Assistant reasoning: ✅ (0 chars)
|
||||
- Thinking process: ❌
|
||||
- Tool executions: ✅ (1 tools)
|
||||
- **Currently sent to memory worker:** Tool inputs/outputs only (no context!) ❌
|
||||
|
||||
---
|
||||
|
||||
### Example 2
|
||||
|
||||
#### 👤 User Request
|
||||
```
|
||||
Thank you for that. So now that you have a very deep understanding of what we are doing here, I'd like you to begin working on the enhancements to our prompts that leverage data using the transcript model we discovered
|
||||
```
|
||||
|
||||
#### 🔧 Tools Executed (1)
|
||||
|
||||
**Glob**
|
||||
- Pattern: `**/*roi*`
|
||||
|
||||
**📊 Data Available for This Exchange:**
|
||||
- User intent: ✅ (218 chars)
|
||||
- Assistant reasoning: ✅ (0 chars)
|
||||
- Thinking process: ❌
|
||||
- Tool executions: ✅ (1 tools)
|
||||
- **Currently sent to memory worker:** Tool inputs/outputs only (no context!) ❌
|
||||
|
||||
---
|
||||
|
||||
### Example 3
|
||||
|
||||
#### 👤 User Request
|
||||
```
|
||||
Thank you for that. So now that you have a very deep understanding of what we are doing here, I'd like you to begin working on the enhancements to our prompts that leverage data using the transcript model we discovered
|
||||
```
|
||||
|
||||
#### 🔧 Tools Executed (1)
|
||||
|
||||
**Glob**
|
||||
- Pattern: `**/*implementation*plan*`
|
||||
|
||||
**📊 Data Available for This Exchange:**
|
||||
- User intent: ✅ (218 chars)
|
||||
- Assistant reasoning: ✅ (0 chars)
|
||||
- Thinking process: ❌
|
||||
- Tool executions: ✅ (1 tools)
|
||||
- **Currently sent to memory worker:** Tool inputs/outputs only (no context!) ❌
|
||||
|
||||
---
|
||||
|
||||
### Example 4
|
||||
|
||||
#### 👤 User Request
|
||||
```
|
||||
Thank you for that. So now that you have a very deep understanding of what we are doing here, I'd like you to begin working on the enhancements to our prompts that leverage data using the transcript model we discovered
|
||||
```
|
||||
|
||||
#### 🔧 Tools Executed (1)
|
||||
|
||||
**Read**
|
||||
- Reading: `/Users/alexnewman/Scripts/claude-mem/docs/context/transcript-data-discovery.md`
|
||||
|
||||
**📊 Data Available for This Exchange:**
|
||||
- User intent: ✅ (218 chars)
|
||||
- Assistant reasoning: ✅ (0 chars)
|
||||
- Thinking process: ❌
|
||||
- Tool executions: ✅ (1 tools)
|
||||
- **Currently sent to memory worker:** Tool inputs/outputs only (no context!) ❌
|
||||
|
||||
---
|
||||
|
||||
### Example 5
|
||||
|
||||
#### 👤 User Request
|
||||
```
|
||||
Thank you for that. So now that you have a very deep understanding of what we are doing here, I'd like you to begin working on the enhancements to our prompts that leverage data using the transcript model we discovered
|
||||
```
|
||||
|
||||
#### 🔧 Tools Executed (1)
|
||||
|
||||
**Read**
|
||||
- Reading: `/Users/alexnewman/Scripts/claude-mem/IMPLEMENTATION_PLAN_ROI_METRICS.md`
|
||||
|
||||
**📊 Data Available for This Exchange:**
|
||||
- User intent: ✅ (218 chars)
|
||||
- Assistant reasoning: ✅ (0 chars)
|
||||
- Thinking process: ❌
|
||||
- Tool executions: ✅ (1 tools)
|
||||
- **Currently sent to memory worker:** Tool inputs/outputs only (no context!) ❌
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Key Insight
|
||||
|
||||
Currently, the memory worker receives **isolated tool executions** via save-hook:
|
||||
- tool_name: "Read"
|
||||
- tool_input: {"file_path": "src/foo.ts"}
|
||||
- tool_output: {file contents}
|
||||
|
||||
But the transcript contains **rich contextual data**:
|
||||
- WHY the tool was used (user's request)
|
||||
- WHAT the assistant planned to accomplish
|
||||
- HOW it fits into the broader task
|
||||
- The assistant's reasoning/thinking
|
||||
- Multiple related tools used together
|
||||
|
||||
This context would help the memory worker:
|
||||
1. Understand if a tool use is meaningful or routine
|
||||
2. Generate observations that capture WHY, not just WHAT
|
||||
3. Group related tools into coherent actions
|
||||
4. Avoid "investigating" - the context is already present
|
||||
|
||||
@@ -0,0 +1,467 @@
|
||||
# Transcript: 57dcc12f-4751-46bb-82b4-2aa96a3e226d.jsonl
|
||||
|
||||
**Generated:** 11/13/2025, 3:58:53 PM
|
||||
**Total Entries:** 448
|
||||
**Entry Types:** {
|
||||
"summary": 149,
|
||||
"file-history-snapshot": 20,
|
||||
"user": 98,
|
||||
"assistant": 181
|
||||
}
|
||||
**Showing:** First 10 conversation turns
|
||||
|
||||
---
|
||||
|
||||
## Turn 1
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:09:47.231Z
|
||||
**UUID:** 5c4b246c-2100-4e6b-a275-f0bb62bba020
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
I have a question regarding the context timeline that we made. and it's a bit of a thinker...
|
||||
|
||||
when i first started out working on claude-mem my idea was using a fast index of memories with links to access them on demand. I thought that claude-mem would be searching for all the things it needed to get the job done, but what I found is that the description of the work done + the files touched actually improved performance tremendously, and without actively searching for things constantly.
|
||||
|
||||
I just had a thought that maybe claude isn't searching, because when it reads what it did, it already "knows" how it did that, for the most part. Would you say I'm on the right track? I want you to ultrathink about WHY claude-mem's session start context works so well, from your OWN perspective
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:10:28.576Z
|
||||
**UUID:** 1dd9591b-c98b-4031-ac0a-2f7e067b427a
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> This is a fascinating question about the fundamental mechanics of how LLMs process context and why claude-mem's approach is working so well. The user is asking me to think deeply about WHY the session start context (descriptions + file paths) works better than active searching.
|
||||
>
|
||||
> Let me think about this from my own perspective as Claude...
|
||||
>
|
||||
> The user's hypothesis is: "Claude isn't searching because when it reads what it did, it already 'knows' how it did that."
|
||||
>
|
||||
> This is a really interesting observation. Let me think about what happens when I see context at session start vs when I actively search:
|
||||
>
|
||||
> 1. **Session Start Context**: I see summaries like "Fixed authentication bug in src/auth/login.ts by updating token validation logic" with file paths
|
||||
> 2. **Active Search**: I use grep/search tools to find specific code or patterns
|
||||
>
|
||||
> The key insight the user is pointing to: When I see the session summary, it's triggering my training knowledge about how these kinds of problems are typically solved. The description + file path combo is enough to reconstruct the likely solution pattern.
|
||||
>
|
||||
> Let me ultrathink about this...
|
||||
>
|
||||
> **Information Density & Compression**:
|
||||
> - A summary like "Added JWT token refresh logic to src/auth/token-manager.ts" is highly compressed but information-rich
|
||||
> - It contains: the pattern (JWT refresh), the location (token-manager), and the action type (added)
|
||||
> - This maps to my training data about how JWT refresh is typically implemented
|
||||
> - The file path gives architectural context about where this lives in the codebase
|
||||
>
|
||||
> **Pattern Recognition vs Literal Recall**:
|
||||
> - I don't need to see the exact code to "know" what was probably done
|
||||
> - JWT token refresh follows common patterns that I've seen thousands of times in training
|
||||
> - The summary activates these patterns in my understanding
|
||||
> - It's like how a human developer reading "fixed the N+1 query problem" immediately knows the general shape of the solution without seeing the code
|
||||
>
|
||||
> **Contextual Scaffolding**:
|
||||
> - File paths provide architectural scaffolding: "src/hooks/context-hook.ts" tells me this is a hook in a hooks directory
|
||||
>
|
||||
>
|
||||
> - The summary provides semantic scaffolding: what was accomplished
|
||||
> - Together they form a mental model of the codebase without needing to read every line
|
||||
>
|
||||
> **Search is for Unknowns, Summaries are for Knowns**:
|
||||
> - I search when I don't know something: "Where is error handling done?"
|
||||
> - I don't need to search when the summary tells me: "Enhanced error handling in src/services/worker-service.ts"
|
||||
> - The summary has already done the work of locating and describing the relevant information
|
||||
>
|
||||
> **The Compression-Decompression Cycle**:
|
||||
> - The AI compression (creating observations) is like creating a highly optimized index
|
||||
> - When I encounter a summary, I'm essentially decompressing that index into actionable knowledge
|
||||
> - This process is far more efficient than searching through raw code or logs
|
||||
>
|
||||
> The summaries act like cognitive shortcuts, instantly reconstructing complex contextual understanding through minimal, strategic information.
|
||||
>
|
||||
> **Why This Works Better Than Constant Searching**:
|
||||
> 1. Drastically reduces mental overhead of navigating complex systems
|
||||
> 2. Enables rapid pattern recognition across different code segments
|
||||
> 3. Provides architectural insights without deep diving into implementation details
|
||||
> 4. Minimizes computational and cognitive resources spent on exploration
|
||||
> 5. Delivers pre-filtered, contextually relevant information
|
||||
>
|
||||
> **The "Already Knows" Hypothesis**:
|
||||
> I recognize complex technical changes instantly through pattern matching. When encountering a concise code modification description, my training allows immediate comprehension by activating relevant technical knowledge domains. The summary triggers an intuitive understanding of systemic implications, similar to how experienced developers quickly grasp architectural shifts through minimal context.
|
||||
>
|
||||
> **The Real Magic**:
|
||||
> Leveraging deep pattern recognition capabilities, I can reconstruct technical landscapes from minimal descriptive signals. File paths and succinct change descriptions become powerful activation keys that unlock comprehensive understanding without exhaustive code examination.
|
||||
>
|
||||
> **Why Links-On-Demand Falls Short**:
|
||||
> Retrieving information through multiple steps disrupts cognitive flow. Contextual understanding emerges more naturally through continuous narrative, where each detail builds upon previous insights. Searching and parsing interrupt the organic comprehension process, making knowledge integration less fluid and intuitive.
|
||||
>
|
||||
> **The Timeline Innovation**:
|
||||
> Project evolution unfolds like a narrative, with each session representing a chapter in technological development. Temporal relationships between changes reveal deeper systemic transformations, mirroring how humans conceptualize complex system growth through interconnected stories of incremental progress.
|
||||
>
|
||||
> The essence lies in providing compressed, meaningful representations that leverage my inherent training knowledge. By presenting contextual frameworks that enable rapid pattern recognition, understanding emerges more elegantly than through fragmented, manually retrieved information.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 10
|
||||
- Output: 1495
|
||||
- Cache creation: 28036
|
||||
|
||||
---
|
||||
|
||||
## Turn 2
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:10:31.963Z
|
||||
**UUID:** 364676a7-51c3-4036-afc3-7ff8f7301a8f
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_01T477WUra1sDR6gHaqZHhKT`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 1,
|
||||
"totalThoughts": 8,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 1
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:10:35.939Z
|
||||
**UUID:** 201d5077-aae8-4e26-9c5e-6a63f1ca88fa
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> Good, I'm using the sequential thinking tool to really dig into this. Let me continue with the next thought about information density.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 12
|
||||
- Output: 256
|
||||
- Cache creation: 1529
|
||||
- Cache read: 28036
|
||||
|
||||
---
|
||||
|
||||
## Turn 3
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:10:38.354Z
|
||||
**UUID:** 6c2f5da2-6a5a-4742-a46d-6dd5d193ad39
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_01QMMWEJm8yH25VtBmAzYbmy`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 2,
|
||||
"totalThoughts": 8,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 2
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:10:41.841Z
|
||||
**UUID:** 3e6f3e78-5c12-4739-a7aa-a8c6fc9d2a33
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> Good, now let me think about the difference between pattern recognition and literal recall.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 11
|
||||
- Output: 262
|
||||
- Cache creation: 323
|
||||
- Cache read: 29565
|
||||
|
||||
---
|
||||
|
||||
## Turn 4
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:10:45.430Z
|
||||
**UUID:** f99751a0-b13a-446a-9b3b-dfc08ffb8e5b
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_01JwNsDV2HcPnDE2eFJdG7zF`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 3,
|
||||
"totalThoughts": 8,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 3
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:10:48.954Z
|
||||
**UUID:** 11c28930-0797-4f9e-ada8-5eaa27c70b47
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> Now let me think about the architectural scaffolding aspect.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 10
|
||||
- Output: 260
|
||||
- Cache creation: 329
|
||||
- Cache read: 29888
|
||||
|
||||
---
|
||||
|
||||
## Turn 5
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:10:53.302Z
|
||||
**UUID:** 6f013ecd-a0f0-4bb9-8eca-e6e8d3f80a53
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_016nFXyGw4SsdKfhUb9eBtkv`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 4,
|
||||
"totalThoughts": 8,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 4
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:10:56.583Z
|
||||
**UUID:** c6d275b3-7f28-4bdd-8a81-80f1a73ffbc7
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> Now let me think about why this works better than active searching.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 9
|
||||
- Output: 258
|
||||
- Cache creation: 327
|
||||
- Cache read: 30217
|
||||
|
||||
---
|
||||
|
||||
## Turn 6
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:11:00.306Z
|
||||
**UUID:** 885fbc9f-e767-4a16-a8aa-b8bb54849d67
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_013QydhPuiBKVRHh1vbD8eL8`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 5,
|
||||
"totalThoughts": 8,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 5
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:11:04.430Z
|
||||
**UUID:** 780354e4-abbf-4d97-b1e2-4f3abc5aef8f
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> Now let me think about the compression-decompression cycle - this is a key insight.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 8
|
||||
- Output: 264
|
||||
- Cache creation: 325
|
||||
- Cache read: 30544
|
||||
|
||||
---
|
||||
|
||||
## Turn 7
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:11:07.748Z
|
||||
**UUID:** 462efec2-17c3-47f7-9171-622e00f6bbf7
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_01Wa5zzkzrYqf5Cfb5UZi5H6`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 6,
|
||||
"totalThoughts": 8,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 6
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:11:12.931Z
|
||||
**UUID:** 325a8179-8291-4198-a661-eae8f02d57b2
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> The user wants me to continue with the sequential thinking about why claude-mem's session start context works so well. I should continue the thinking process and not mention the todo reminder since this is a theoretical discussion, not a task execution.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 10
|
||||
- Output: 328
|
||||
- Cache creation: 1883
|
||||
- Cache read: 28036
|
||||
|
||||
---
|
||||
|
||||
## Turn 8
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:11:17.712Z
|
||||
**UUID:** 189d903f-a901-465f-bd6b-8addf44cd5f5
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_015VKanT9FxnCSsZiTduHd2y`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 7,
|
||||
"totalThoughts": 9,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 7
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:11:21.820Z
|
||||
**UUID:** 96ca3f03-f889-454d-a13a-96e7a36a3daa
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> I want to add one more thought about the temporal/chronological aspect and how that creates a narrative structure that aids understanding.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 12
|
||||
- Output: 290
|
||||
- Cache creation: 392
|
||||
- Cache read: 29919
|
||||
|
||||
---
|
||||
|
||||
## Turn 9
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:11:26.591Z
|
||||
**UUID:** cfd56a68-8628-4811-9df5-75a8e557bec6
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_01EWtaz38nageXXEjYzzm2hu`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 8,
|
||||
"totalThoughts": 9,
|
||||
"nextThoughtNeeded": true,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 8
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:11:30.749Z
|
||||
**UUID:** bb106bb5-9696-4538-b810-d9fa56bcffff
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** tool_use
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> Now for the synthesis - bringing all these threads together to answer the user's question about WHY it works so well from my own perspective.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 11
|
||||
- Output: 360
|
||||
- Cache creation: 357
|
||||
- Cache read: 30311
|
||||
|
||||
---
|
||||
|
||||
## Turn 10
|
||||
|
||||
### 👤 User
|
||||
**Timestamp:** 2025-11-13T17:11:36.692Z
|
||||
**UUID:** 0aa4f55d-9f5a-485f-9aef-dd90f9e80391
|
||||
**Session ID:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d
|
||||
**CWD:** /Users/alexnewman/Scripts/claude-mem
|
||||
|
||||
**Tool Results Submitted (1):**
|
||||
|
||||
- **Tool Use ID:** `toolu_01R5Sy5zet7JCPmXhvDrkMAR`
|
||||
```json
|
||||
{
|
||||
"thoughtNumber": 9,
|
||||
"totalThoughts": 9,
|
||||
"nextThoughtNeeded": false,
|
||||
"branches": [],
|
||||
"thoughtHistoryLength": 9
|
||||
}
|
||||
```
|
||||
|
||||
### 🤖 Assistant
|
||||
**Timestamp:** 2025-11-13T17:11:43.435Z
|
||||
**UUID:** 2a5c5c37-9124-441f-ac1f-3f9bff08281c
|
||||
**Model:** claude-sonnet-4-5-20250929
|
||||
**Stop Reason:** end_turn
|
||||
|
||||
**💭 Thinking:**
|
||||
|
||||
> This is a fascinating philosophical question about how I process information. The user is asking me to think deeply about WHY claude-mem's session start context works so well from MY perspective as an LLM. They've noticed something important: I don't seem to need to actively search when I have good summaries, and they're hypothesizing it's because the summaries trigger pattern recognition rather than requiring literal recall.
|
||||
>
|
||||
> This is not a task-oriented request - it's a meta-cognitive question about how I think. I should not use TodoWrite here as this is a philosophical discussion, not a task to track.
|
||||
>
|
||||
> The sequential thinking tool has helped me organize my thoughts. Let me now synthesize this into a clear response about my actual experience.
|
||||
|
||||
**📊 Token Usage:**
|
||||
- Input: 10
|
||||
- Output: 936
|
||||
- Cache creation: 1140
|
||||
- Cache read: 29919
|
||||
|
||||
---
|
||||
|
||||
|
||||
*... 87 more turns not shown*
|
||||
@@ -0,0 +1,632 @@
|
||||
# Transcript Data Analysis: Available Context for Memory Worker
|
||||
|
||||
**Generated:** 2025-11-13
|
||||
**Purpose:** Document what contextual data exists in Claude Code transcripts and identify opportunities to improve memory worker observation generation.
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
**Current State:** The memory worker receives isolated tool executions via `save-hook.ts`:
|
||||
- Tool name
|
||||
- Tool input (parameters)
|
||||
- Tool output (results)
|
||||
|
||||
**Available in Transcripts:** Rich contextual data that could dramatically improve observation quality:
|
||||
- User's original request/intent
|
||||
- Assistant's reasoning (thinking blocks)
|
||||
- Full conversation context
|
||||
- Tool result data
|
||||
- Token usage and performance metrics
|
||||
- Session metadata (timestamps, UUIDs, CWD)
|
||||
|
||||
**Recommendation:** Enhance the memory worker to receive full conversation context for each tool execution, not just isolated tool data.
|
||||
|
||||
---
|
||||
|
||||
## Transcript Structure
|
||||
|
||||
### Entry Types
|
||||
|
||||
The transcript file (`~/.claude/projects/-{project}/session-id.jsonl`) contains:
|
||||
|
||||
```
|
||||
- summary entries (149 in sample)
|
||||
- file-history-snapshot entries (18 in sample)
|
||||
- user entries (86 in sample)
|
||||
- assistant entries (155 in sample)
|
||||
```
|
||||
|
||||
### Conversation Turn Pattern
|
||||
|
||||
Each conversation turn consists of:
|
||||
1. **User Entry** - User's request
|
||||
2. **Assistant Entry** - Assistant's response
|
||||
3. **User Entry** - Tool results submitted back (automatic)
|
||||
4. **Assistant Entry** - Assistant processes results and continues
|
||||
|
||||
This creates a pattern: User → Assistant → User (tool results) → Assistant (continues) → ...
|
||||
|
||||
---
|
||||
|
||||
## Available Data by Entry Type
|
||||
|
||||
### 1. User Entries
|
||||
|
||||
**Current Save-Hook Access:**
|
||||
- Tool name
|
||||
- Tool input
|
||||
- Tool output
|
||||
|
||||
**Additional Data Available in User Entries:**
|
||||
|
||||
```typescript
|
||||
interface UserTranscriptEntry {
|
||||
type: 'user';
|
||||
timestamp: string; // ISO timestamp
|
||||
uuid: string; // Unique entry ID
|
||||
sessionId: string; // Session identifier
|
||||
cwd: string; // Working directory
|
||||
parentUuid?: string; // Parent entry reference
|
||||
isSidechain: boolean; // Is this a side conversation?
|
||||
userType: string; // 'human' or 'system'
|
||||
version: string; // Claude Code version
|
||||
|
||||
message: {
|
||||
role: 'user';
|
||||
content: string | ContentItem[]; // Can be text or structured
|
||||
};
|
||||
|
||||
toolUseResult?: ToolUseResult; // Legacy field, may contain results
|
||||
}
|
||||
```
|
||||
|
||||
**When `content` is an array, it contains:**
|
||||
- Text blocks with user's actual request
|
||||
- Tool result blocks with complete output data
|
||||
|
||||
**Example Structure:**
|
||||
```json
|
||||
{
|
||||
"type": "user",
|
||||
"timestamp": "2025-11-13T17:10:31.963Z",
|
||||
"uuid": "364676a7-51c3-4036-afc3-7ff8f7301a8f",
|
||||
"sessionId": "57dcc12f-4751-46bb-82b4-2aa96a3e226d",
|
||||
"cwd": "/Users/alexnewman/Scripts/claude-mem",
|
||||
"message": {
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": "toolu_01T477WUra1sDR6gHaqZHhKT",
|
||||
"content": "[actual tool output data]"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. Assistant Entries
|
||||
|
||||
**Current Save-Hook Access:**
|
||||
- Nothing from assistant entries (they happen after tool execution)
|
||||
|
||||
**Available Data in Assistant Entries:**
|
||||
|
||||
```typescript
|
||||
interface AssistantTranscriptEntry {
|
||||
type: 'assistant';
|
||||
timestamp: string;
|
||||
uuid: string;
|
||||
sessionId: string;
|
||||
cwd: string;
|
||||
parentUuid?: string;
|
||||
isSidechain: boolean;
|
||||
userType: string;
|
||||
version: string;
|
||||
requestId?: string; // API request ID
|
||||
|
||||
message: {
|
||||
id: string;
|
||||
type: 'message';
|
||||
role: 'assistant';
|
||||
model: string; // e.g., "claude-sonnet-4-5-20250929"
|
||||
content: ContentItem[]; // Array of content blocks
|
||||
stop_reason?: string; // 'tool_use' | 'end_turn' | etc.
|
||||
stop_sequence?: string;
|
||||
usage?: UsageInfo; // Token usage stats
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
**Content Block Types in `message.content`:**
|
||||
|
||||
1. **Thinking Blocks** - Internal reasoning before acting
|
||||
```typescript
|
||||
{
|
||||
type: 'thinking';
|
||||
thinking: string; // Full reasoning text
|
||||
signature?: string;
|
||||
}
|
||||
```
|
||||
|
||||
2. **Text Blocks** - Assistant's visible response
|
||||
```typescript
|
||||
{
|
||||
type: 'text';
|
||||
text: string; // Response text
|
||||
}
|
||||
```
|
||||
|
||||
3. **Tool Use Blocks** - Tool invocations
|
||||
```typescript
|
||||
{
|
||||
type: 'tool_use';
|
||||
id: string; // Tool use ID
|
||||
name: string; // Tool name (e.g., 'Read', 'Edit')
|
||||
input: Record<string, any>; // Complete tool parameters
|
||||
}
|
||||
```
|
||||
|
||||
**Token Usage Data:**
|
||||
```typescript
|
||||
interface UsageInfo {
|
||||
input_tokens?: number;
|
||||
output_tokens?: number;
|
||||
cache_creation_input_tokens?: number;
|
||||
cache_read_input_tokens?: number;
|
||||
service_tier?: string;
|
||||
}
|
||||
```
|
||||
|
||||
### 3. Summary Entries
|
||||
|
||||
```typescript
|
||||
interface SummaryTranscriptEntry {
|
||||
type: 'summary';
|
||||
summary: string; // Generated summary text
|
||||
leafUuid: string; // UUID of summarized entry
|
||||
cwd?: string;
|
||||
}
|
||||
```
|
||||
|
||||
These appear frequently (149 in sample) and provide high-level summaries of work done.
|
||||
|
||||
---
|
||||
|
||||
## Data Flow: Current vs Potential
|
||||
|
||||
### Current Flow (Save-Hook Only)
|
||||
|
||||
```
|
||||
User: "Fix the bug in login.ts"
|
||||
↓
|
||||
Assistant: [uses Edit tool]
|
||||
↓
|
||||
Tool Execution: Edit(file_path: "login.ts", old_string: "...", new_string: "...")
|
||||
↓
|
||||
Save-Hook receives:
|
||||
- toolName: "Edit"
|
||||
- toolInput: { file_path: "login.ts", old_string: "...", new_string: "..." }
|
||||
- toolOutput: { success: true }
|
||||
↓
|
||||
Memory Worker generates observation from ONLY tool data
|
||||
- No user intent
|
||||
- No assistant reasoning
|
||||
- No context about WHY this change was made
|
||||
```
|
||||
|
||||
### Enhanced Flow (With Transcript Context)
|
||||
|
||||
```
|
||||
User: "Fix the authentication bug - users getting logged out randomly"
|
||||
↓
|
||||
Assistant (thinking): "This sounds like a token expiration issue.
|
||||
Let me check the JWT handling in login.ts..."
|
||||
↓
|
||||
Assistant (uses Edit tool)
|
||||
↓
|
||||
Save-Hook receives:
|
||||
- toolName: "Edit"
|
||||
- toolInput: { file_path: "login.ts", ... }
|
||||
- toolOutput: { success: true }
|
||||
- PLUS:
|
||||
- userRequest: "Fix the authentication bug - users getting logged out randomly"
|
||||
- assistantReasoning: "This sounds like a token expiration issue..."
|
||||
- conversationContext: Previous 2-3 turns
|
||||
- sessionMetadata: { cwd, timestamp, sessionId }
|
||||
↓
|
||||
Memory Worker generates richer observation:
|
||||
- "Fixed authentication bug causing random logouts"
|
||||
- "Problem: JWT tokens expiring too quickly"
|
||||
- "Solution: Updated token expiration to 24h in login.ts"
|
||||
- "Files: src/auth/login.ts"
|
||||
- "Concepts: authentication, token-management, bugfix"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Specific Opportunities
|
||||
|
||||
### 1. User Intent Extraction
|
||||
|
||||
**Problem:** Current observations lack user intent.
|
||||
|
||||
**Solution:** Parse the most recent user text entry before the tool execution.
|
||||
|
||||
**Implementation:**
|
||||
- Walk backward from tool execution entry
|
||||
- Find first user entry with text content
|
||||
- Extract text blocks (filter out tool_result blocks)
|
||||
|
||||
**Example:**
|
||||
```typescript
|
||||
// In save-hook.ts
|
||||
const userEntries = parser.getUserEntries();
|
||||
const recentUserMessage = findUserMessageBeforeTool(userEntries, toolExecutionTimestamp);
|
||||
const userIntent = extractTextFromContent(recentUserMessage.content);
|
||||
```
|
||||
|
||||
### 2. Assistant Reasoning
|
||||
|
||||
**Problem:** We don't capture WHY the assistant chose to use a tool.
|
||||
|
||||
**Solution:** Extract thinking blocks from assistant entry immediately before tool use.
|
||||
|
||||
**Implementation:**
|
||||
- Find assistant entry that contains the tool_use block
|
||||
- Extract thinking blocks from same entry
|
||||
- Include first ~500 chars of thinking in observation context
|
||||
|
||||
**Example:**
|
||||
```typescript
|
||||
const assistantEntry = findAssistantEntryWithToolUse(toolUseId);
|
||||
const thinkingBlocks = assistantEntry.message.content.filter(c => c.type === 'thinking');
|
||||
const reasoning = thinkingBlocks.map(b => b.thinking).join('\n');
|
||||
```
|
||||
|
||||
### 3. Tool Results Context
|
||||
|
||||
**Problem:** Tool output alone doesn't show what was found or changed.
|
||||
|
||||
**Solution:** Access full tool result content from next user entry.
|
||||
|
||||
**Implementation:**
|
||||
- Tool execution happens in assistant entry
|
||||
- Results come back in next user entry as tool_result content
|
||||
- Save-hook can access both
|
||||
|
||||
**Current Structure:**
|
||||
```
|
||||
Assistant Entry:
|
||||
{ type: 'tool_use', id: 'toolu_123', name: 'Read', input: {...} }
|
||||
↓
|
||||
User Entry (automatic):
|
||||
{ type: 'tool_result', tool_use_id: 'toolu_123', content: "file contents..." }
|
||||
```
|
||||
|
||||
**Opportunity:** Match tool_use_id to tool_result and include full result content.
|
||||
|
||||
### 4. Conversation Context
|
||||
|
||||
**Problem:** Isolated tool executions miss the larger conversation flow.
|
||||
|
||||
**Solution:** Include last N conversation turns (2-3 turns is usually sufficient).
|
||||
|
||||
**Implementation:**
|
||||
- Get entries from transcript within time window (e.g., last 5 minutes)
|
||||
- Include user messages and assistant text responses
|
||||
- Exclude thinking blocks to save tokens
|
||||
|
||||
**Example Context:**
|
||||
```
|
||||
Turn 1:
|
||||
User: "I need to add dark mode support"
|
||||
Assistant: "I'll help you add dark mode. Let me start by..."
|
||||
|
||||
Turn 2:
|
||||
User: [tool results]
|
||||
Assistant: "Now I'll update the theme configuration..."
|
||||
|
||||
Turn 3: [current tool execution]
|
||||
```
|
||||
|
||||
### 5. Session Metadata
|
||||
|
||||
**Problem:** Observations lack temporal and project context.
|
||||
|
||||
**Solution:** Include session metadata in observation generation.
|
||||
|
||||
**Available Fields:**
|
||||
- `cwd` - Working directory (project path)
|
||||
- `timestamp` - Exact time of execution
|
||||
- `sessionId` - Session identifier
|
||||
- `uuid` - Entry identifier
|
||||
- `version` - Claude Code version
|
||||
|
||||
**Use Case:** Helps with project-specific context and temporal queries.
|
||||
|
||||
### 6. Token Usage Metrics
|
||||
|
||||
**Problem:** No visibility into performance and cost.
|
||||
|
||||
**Solution:** Track token usage per observation.
|
||||
|
||||
**Available Data:**
|
||||
- Input tokens
|
||||
- Output tokens
|
||||
- Cache creation tokens
|
||||
- Cache read tokens
|
||||
|
||||
**Use Case:**
|
||||
- Performance monitoring
|
||||
- Cost attribution
|
||||
- Cache effectiveness analysis
|
||||
|
||||
---
|
||||
|
||||
## Recommended Implementation Strategy
|
||||
|
||||
### Phase 1: User Intent (High Impact, Low Effort)
|
||||
|
||||
**Change:** Modify save-hook to extract user's most recent message.
|
||||
|
||||
**Implementation:**
|
||||
```typescript
|
||||
// In save-hook.ts
|
||||
import { TranscriptParser } from '../utils/transcript-parser';
|
||||
|
||||
const parser = new TranscriptParser(transcriptPath);
|
||||
const userIntent = parser.getLastUserMessage();
|
||||
|
||||
// Send to worker
|
||||
await workerService.saveToolExecution({
|
||||
...existingData,
|
||||
userIntent, // NEW
|
||||
});
|
||||
```
|
||||
|
||||
**Impact:** Observations now include "what the user wanted to do".
|
||||
|
||||
### Phase 2: Assistant Reasoning (High Impact, Medium Effort)
|
||||
|
||||
**Change:** Extract thinking blocks from assistant entry containing tool use.
|
||||
|
||||
**Implementation:**
|
||||
```typescript
|
||||
const assistantEntries = parser.getAssistantEntries();
|
||||
const toolUseEntry = findEntryWithToolUse(assistantEntries, toolUseId);
|
||||
const thinking = extractThinkingBlocks(toolUseEntry);
|
||||
|
||||
await workerService.saveToolExecution({
|
||||
...existingData,
|
||||
userIntent,
|
||||
assistantReasoning: thinking, // NEW
|
||||
});
|
||||
```
|
||||
|
||||
**Impact:** Observations include "why the assistant chose this approach".
|
||||
|
||||
### Phase 3: Conversation Context (Medium Impact, High Effort)
|
||||
|
||||
**Change:** Include last 2-3 conversation turns.
|
||||
|
||||
**Implementation:**
|
||||
```typescript
|
||||
const recentTurns = getRecentConversationTurns(parser, 3);
|
||||
|
||||
await workerService.saveToolExecution({
|
||||
...existingData,
|
||||
userIntent,
|
||||
assistantReasoning: thinking,
|
||||
conversationContext: recentTurns, // NEW
|
||||
});
|
||||
```
|
||||
|
||||
**Impact:** Observations understand multi-turn workflows.
|
||||
|
||||
### Phase 4: Enhanced Metadata (Low Impact, Low Effort)
|
||||
|
||||
**Change:** Include session and performance metadata.
|
||||
|
||||
**Implementation:**
|
||||
```typescript
|
||||
await workerService.saveToolExecution({
|
||||
...existingData,
|
||||
userIntent,
|
||||
assistantReasoning: thinking,
|
||||
conversationContext: recentTurns,
|
||||
metadata: { // NEW
|
||||
cwd: entry.cwd,
|
||||
timestamp: entry.timestamp,
|
||||
sessionId: entry.sessionId,
|
||||
tokenUsage: entry.message.usage,
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
**Impact:** Better analytics and debugging.
|
||||
|
||||
---
|
||||
|
||||
## Example: Before and After
|
||||
|
||||
### Current Observation (Tool Data Only)
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "feature",
|
||||
"title": "Updated login.ts",
|
||||
"narrative": "Modified authentication logic in src/auth/login.ts",
|
||||
"files": ["src/auth/login.ts"],
|
||||
"concepts": ["authentication"],
|
||||
"facts": []
|
||||
}
|
||||
```
|
||||
|
||||
### Enhanced Observation (With Transcript Context)
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "bugfix",
|
||||
"title": "Fixed authentication bug causing random logouts",
|
||||
"narrative": "Users were experiencing random logouts due to JWT token expiration. Updated token expiration from 1h to 24h in token validation logic. Modified src/auth/login.ts to use longer-lived tokens and improved error handling for expired tokens.",
|
||||
"files": ["src/auth/login.ts"],
|
||||
"concepts": ["authentication", "jwt", "token-management", "bugfix"],
|
||||
"facts": [
|
||||
"JWT token expiration was too short (1h)",
|
||||
"Updated expiration to 24h",
|
||||
"Added error handling for expired tokens"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**Improvement:**
|
||||
- Clear problem statement
|
||||
- Explicit solution
|
||||
- Specific technical details
|
||||
- Better concept tagging
|
||||
- Actionable facts
|
||||
|
||||
---
|
||||
|
||||
## Technical Considerations
|
||||
|
||||
### 1. Performance
|
||||
|
||||
**Concern:** Parsing entire transcript on every tool execution.
|
||||
|
||||
**Solution:**
|
||||
- TranscriptParser already loads full file (unavoidable)
|
||||
- Use caching for transcript parsing within same session
|
||||
- Only parse once per session, reuse parsed entries
|
||||
|
||||
**Benchmark:**
|
||||
- Current: ~10ms to parse 408-line transcript
|
||||
- Impact: Negligible (save-hook already reads transcript)
|
||||
|
||||
### 2. Token Usage
|
||||
|
||||
**Concern:** Sending more context to worker increases tokens.
|
||||
|
||||
**Solution:**
|
||||
- Thinking blocks: Limit to first 500 chars
|
||||
- Conversation context: Only last 2-3 turns
|
||||
- Tool results: Truncate large outputs to 500 chars
|
||||
- User intent: Full text (usually short)
|
||||
|
||||
**Estimate:**
|
||||
- Current: ~200 tokens per observation generation
|
||||
- Enhanced: ~500 tokens per observation generation
|
||||
- Increase: ~150%
|
||||
- Cost: Still < $0.001 per observation with Haiku
|
||||
|
||||
### 3. Implementation Complexity
|
||||
|
||||
**Concern:** Matching tool executions to transcript entries.
|
||||
|
||||
**Solution:**
|
||||
- Tool use IDs are in both places
|
||||
- Timestamps provide ordering
|
||||
- UUID chains provide parent-child relationships
|
||||
|
||||
**Example Matching:**
|
||||
```typescript
|
||||
function findToolContext(parser: TranscriptParser, toolUseId: string) {
|
||||
// 1. Find assistant entry with tool_use block
|
||||
const assistantEntry = parser.getAssistantEntries()
|
||||
.find(entry =>
|
||||
entry.message.content.some(c =>
|
||||
c.type === 'tool_use' && c.id === toolUseId
|
||||
)
|
||||
);
|
||||
|
||||
// 2. Find next user entry with tool_result
|
||||
const userEntry = parser.getUserEntries()
|
||||
.find(entry =>
|
||||
entry.message.content.some(c =>
|
||||
c.type === 'tool_result' && c.tool_use_id === toolUseId
|
||||
)
|
||||
);
|
||||
|
||||
return { assistantEntry, userEntry };
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Validate Approach**
|
||||
- Review this analysis with project team
|
||||
- Confirm data availability in all transcript scenarios
|
||||
- Identify any privacy concerns
|
||||
|
||||
2. **Implement Phase 1**
|
||||
- Update save-hook.ts to extract user intent
|
||||
- Modify worker service to accept new fields
|
||||
- Update observation prompt to use user intent
|
||||
|
||||
3. **Test and Measure**
|
||||
- Compare observation quality before/after
|
||||
- Measure token usage increase
|
||||
- Validate performance impact
|
||||
|
||||
4. **Iterate**
|
||||
- Roll out Phase 2 (assistant reasoning)
|
||||
- Roll out Phase 3 (conversation context)
|
||||
- Monitor improvements at each phase
|
||||
|
||||
---
|
||||
|
||||
## Appendix: Data Samples
|
||||
|
||||
### Complete Markdown Representation
|
||||
|
||||
See `/Users/alexnewman/Scripts/claude-mem/docs/context/transcript-complete-readable.md` for a full 1:1 markdown representation of the first 10 conversation turns from the sample transcript, including:
|
||||
- Complete user messages
|
||||
- Full assistant responses
|
||||
- Thinking blocks (truncated to 2000 chars)
|
||||
- Tool uses with complete input JSON
|
||||
- Tool results with actual output data (truncated to 500 chars)
|
||||
- Token usage stats
|
||||
- All metadata (timestamps, UUIDs, session IDs, CWD)
|
||||
|
||||
### Sample Tool Result Structure
|
||||
|
||||
```typescript
|
||||
// User entry containing tool result
|
||||
{
|
||||
"type": "user",
|
||||
"message": {
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": "toolu_01T477WUra1sDR6gHaqZHhKT",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "{\n \"thoughtNumber\": 1,\n \"totalThoughts\": 8,\n \"nextThoughtNeeded\": true,\n \"branches\": [],\n \"thoughtHistoryLength\": 1\n}"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The Claude Code transcript files contain a wealth of contextual data that is currently unused by the memory worker. By extracting:
|
||||
|
||||
1. User intent (the "what" and "why")
|
||||
2. Assistant reasoning (the "how" and "because")
|
||||
3. Tool results (the "outcome")
|
||||
4. Conversation context (the "flow")
|
||||
5. Session metadata (the "when" and "where")
|
||||
|
||||
We can generate significantly richer, more useful observations that better capture the intent, decisions, and outcomes of each coding session.
|
||||
|
||||
**The data is already there - we just need to read it.**
|
||||
@@ -0,0 +1,234 @@
|
||||
# Claude Code Transcript Data Discovery
|
||||
|
||||
## Executive Summary
|
||||
|
||||
This document details findings from implementing a validated transcript parser for Claude Code JSONL transcripts. The parser enables extraction of rich contextual data that can optimize prompt generation and track token usage for ROI metrics.
|
||||
|
||||
## Transcript Structure
|
||||
|
||||
### File Location
|
||||
```
|
||||
~/.claude/projects/<encoded-project-path>/<session-id>.jsonl
|
||||
```
|
||||
|
||||
Example:
|
||||
```
|
||||
~/.claude/projects/-Users-alexnewman-Scripts-claude-mem/2933cff9-f0a7-4f0b-8296-0a030e7658a6.jsonl
|
||||
```
|
||||
|
||||
### Entry Types
|
||||
|
||||
Discovered 5 transcript entry types:
|
||||
|
||||
1. **`file-history-snapshot`** (NEW - not in Python model)
|
||||
- Purpose: Track file state snapshots
|
||||
- Frequency: ~10 entries per session
|
||||
|
||||
2. **`user`** - User messages and tool results
|
||||
- Contains actual user text messages OR tool result data
|
||||
- Can have string content or array of ContentItems
|
||||
|
||||
3. **`assistant`** - Assistant responses and tool uses
|
||||
- Contains text responses, tool uses, and thinking blocks
|
||||
- **Critical**: Contains usage data with token counts
|
||||
|
||||
4. **`summary`** (not yet observed in test data)
|
||||
- Session summaries
|
||||
|
||||
5. **`system`** (not yet observed in test data)
|
||||
- System messages/warnings
|
||||
|
||||
6. **`queue-operation`** (not yet observed in test data)
|
||||
- Queue tracking for message flow
|
||||
|
||||
## Key Findings
|
||||
|
||||
### 1. Message Extraction Complexity
|
||||
|
||||
**Problem**: Naively getting the "last" entry doesn't work because:
|
||||
- Last user entry might be a tool result, not a text message
|
||||
- Last assistant entry might only contain tool uses, no text
|
||||
|
||||
**Solution**: Iterate backward through entries to find the last entry with actual text content.
|
||||
|
||||
### 2. Tool Use Tracking
|
||||
|
||||
**Discovery**: Tool uses are in **assistant** messages, not user messages.
|
||||
|
||||
**Data Available**:
|
||||
```typescript
|
||||
{
|
||||
name: string; // Tool name (e.g., "Bash", "Read", "TodoWrite")
|
||||
timestamp: string; // When the tool was used
|
||||
input: any; // Full tool input parameters
|
||||
}
|
||||
```
|
||||
|
||||
**Test Session Results** (168 entries):
|
||||
- 42 tool uses across 7 different tool types
|
||||
- Most used: Bash (24x), TodoWrite (5x), Edit (4x)
|
||||
|
||||
### 3. Token Usage Data (ROI Foundation)
|
||||
|
||||
**Critical Discovery**: Every assistant message contains complete token usage data:
|
||||
|
||||
```typescript
|
||||
interface UsageInfo {
|
||||
input_tokens?: number; // Total input tokens (includes context)
|
||||
cache_creation_input_tokens?: number; // Tokens used to create cache
|
||||
cache_read_input_tokens?: number; // Cached tokens read (discounted cost)
|
||||
output_tokens?: number; // Model output tokens
|
||||
}
|
||||
```
|
||||
|
||||
**Test Session Token Analysis**:
|
||||
```
|
||||
Input tokens: 858
|
||||
Output tokens: 44,165
|
||||
Cache creation tokens: 469,650
|
||||
Cache read tokens: 5,294,101 ← 5.29M tokens saved by caching!
|
||||
Total tokens: 45,023
|
||||
```
|
||||
|
||||
**ROI Implication**: This validates our ROI implementation plan. We can track:
|
||||
- Discovery cost = sum of all input + output tokens across session
|
||||
- Context savings = cache_read_input_tokens (tokens NOT paid for in full)
|
||||
- ROI = Discovery cost / Context savings
|
||||
|
||||
### 4. Parse Reliability
|
||||
|
||||
**Result**: 0.00% parse failure rate on production transcript with 168 entries.
|
||||
|
||||
**Conclusion**: The JSONL format is stable and well-formed. No need for extensive error handling.
|
||||
|
||||
## Implementation Files
|
||||
|
||||
### Created Files
|
||||
|
||||
1. **`src/types/transcript.ts`** - TypeScript types matching Python Pydantic model
|
||||
- All entry types, content types, usage info
|
||||
- Drop-in compatible with Python model structure
|
||||
|
||||
2. **`src/utils/transcript-parser.ts`** - Robust transcript parsing class
|
||||
- Handles all entry types
|
||||
- Smart message extraction (finds last text message, not just last entry)
|
||||
- Tool use history extraction
|
||||
- Token usage aggregation
|
||||
- Parse statistics and error tracking
|
||||
|
||||
3. **`scripts/test-transcript-parser.ts`** - Validation script
|
||||
- Tests all extraction methods
|
||||
- Reports parse statistics
|
||||
- Shows token usage breakdown
|
||||
- Lists tool use history
|
||||
|
||||
### Usage Example
|
||||
|
||||
```typescript
|
||||
import { TranscriptParser } from '../src/utils/transcript-parser.js';
|
||||
|
||||
const parser = new TranscriptParser('/path/to/transcript.jsonl');
|
||||
|
||||
// Extract messages
|
||||
const lastUserMsg = parser.getLastUserMessage();
|
||||
const lastAssistantMsg = parser.getLastAssistantMessage();
|
||||
|
||||
// Get tool history
|
||||
const tools = parser.getToolUseHistory();
|
||||
// => [{name: 'Bash', timestamp: '...', input: {...}}, ...]
|
||||
|
||||
// Get token usage
|
||||
const tokens = parser.getTotalTokenUsage();
|
||||
// => {inputTokens: 858, outputTokens: 44165, cacheReadTokens: 5294101, ...}
|
||||
|
||||
// Parse statistics
|
||||
const stats = parser.getParseStats();
|
||||
// => {totalLines: 168, parsedEntries: 168, failedLines: 0, ...}
|
||||
```
|
||||
|
||||
## Next Steps for PR Review
|
||||
|
||||
### Addressing "Drops Unknown Lines" Concern
|
||||
|
||||
**Original Issue**: Summary hook silently skipped malformed lines without visibility.
|
||||
|
||||
**Root Cause**: We didn't understand the full transcript model. The "skip malformed lines" was a band-aid.
|
||||
|
||||
**Solution**: Replace ad-hoc parsing in `summary-hook.ts` with validated `TranscriptParser` class:
|
||||
|
||||
**Before** (summary-hook.ts:38-117):
|
||||
```typescript
|
||||
// Manually parsing with try/catch, no type safety
|
||||
for (let i = lines.length - 1; i >= 0; i--) {
|
||||
try {
|
||||
const line = JSON.parse(lines[i]);
|
||||
if (line.type === 'user' && line.message?.content) {
|
||||
// ... extraction logic
|
||||
}
|
||||
} catch (parseError) {
|
||||
// Skip malformed lines ← BLACK HOLE
|
||||
continue;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**After** (using TranscriptParser):
|
||||
```typescript
|
||||
import { TranscriptParser } from '../utils/transcript-parser.js';
|
||||
|
||||
const parser = new TranscriptParser(transcriptPath);
|
||||
const lastUserMessage = parser.getLastUserMessage();
|
||||
const lastAssistantMessage = parser.getLastAssistantMessage();
|
||||
|
||||
// Parse errors are tracked in parser.getParseErrors()
|
||||
```
|
||||
|
||||
**Benefits**:
|
||||
1. ✅ Type-safe extraction based on validated model
|
||||
2. ✅ No silent failures - parse errors are tracked
|
||||
3. ✅ Smart extraction (finds last TEXT message, not last entry)
|
||||
4. ✅ Reusable across all hooks and scripts
|
||||
5. ✅ Enables token usage tracking (ROI metrics)
|
||||
6. ✅ Enables tool use tracking (prompt optimization)
|
||||
|
||||
## Prompt Optimization Opportunities
|
||||
|
||||
With rich transcript data available, we can enhance prompts with:
|
||||
|
||||
### 1. Tool Use Patterns
|
||||
- "In this session you've used: Bash (24x), TodoWrite (5x), Edit (4x)"
|
||||
- Helps Claude understand what kind of work is being done
|
||||
|
||||
### 2. Token Economics Awareness
|
||||
- "Cache read tokens: 5.29M (context savings)"
|
||||
- Reinforces value of memory system
|
||||
|
||||
### 3. Session Flow Understanding
|
||||
- Number of user/assistant exchanges
|
||||
- Tools used per exchange
|
||||
- Session complexity metrics
|
||||
|
||||
### 4. File History Snapshots
|
||||
- Track which files were modified during session
|
||||
- Provide file change context to summaries
|
||||
|
||||
## Testing
|
||||
|
||||
Run the validation script:
|
||||
```bash
|
||||
# Find your current session transcript
|
||||
ls -lt ~/.claude/projects/-Users-alexnewman-Scripts-claude-mem/*.jsonl | head -1
|
||||
|
||||
# Test the parser
|
||||
npx tsx scripts/test-transcript-parser.ts <path-to-transcript.jsonl>
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
The transcript parser implementation:
|
||||
1. ✅ Addresses PR review concern about dropped lines
|
||||
2. ✅ Validates the ROI metrics implementation plan
|
||||
3. ✅ Enables prompt optimization with rich context
|
||||
4. ✅ Provides foundation for future enhancements
|
||||
|
||||
**Recommendation**: Replace ad-hoc transcript parsing in hooks with `TranscriptParser` class for improved reliability and feature richness.
|
||||
@@ -0,0 +1,22 @@
|
||||
# Transcript Dump
|
||||
|
||||
Total entries: 384
|
||||
|
||||
---
|
||||
|
||||
## Entry 151: USER
|
||||
**Timestamp:** 2025-11-13T17:09:47.231Z
|
||||
|
||||
**Content:**
|
||||
```
|
||||
I have a question regarding the context timeline that we made. and it's a bit of a thinker...
|
||||
|
||||
when i first started out working on claude-mem my idea was using a fast index of memories with links to access them on demand. I thought that claude-mem would be searching for all the things it needed to get the job done, but what I found is that the description of the work done + the files touched actually improved performance tremendously, and without actively searching for things constantly.
|
||||
|
||||
I just had a thought that maybe claude isn't searching, because when it reads what it did, it already "knows" how it did that, for the most part. Would you say I'm on the right track? I want you to ultrathink about WHY claude-mem's session start context works so well, from your OWN perspective
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
|
||||
_Remaining 364 entries omitted for brevity_
|
||||
@@ -0,0 +1,106 @@
|
||||
# Transcript Context Analysis
|
||||
|
||||
**File:** 57dcc12f-4751-46bb-82b4-2aa96a3e226d.jsonl
|
||||
**Parsed:** 11/13/2025, 12:34:19 PM
|
||||
|
||||
## Statistics
|
||||
|
||||
- Total entries: 281
|
||||
- Successfully parsed: 281
|
||||
- Failed lines: 0
|
||||
- Conversation turns: 43
|
||||
|
||||
## Token Usage
|
||||
|
||||
- Input tokens: 6,805
|
||||
- Output tokens: 38,209
|
||||
- Cache creation: 224,078
|
||||
- Cache read: 3,320,726
|
||||
- Total: 45,014
|
||||
|
||||
---
|
||||
|
||||
# Conversation Turns
|
||||
|
||||
## Turn 1
|
||||
|
||||
---
|
||||
|
||||
## Turn 2
|
||||
|
||||
---
|
||||
|
||||
## Turn 3
|
||||
|
||||
---
|
||||
|
||||
## Turn 4
|
||||
|
||||
---
|
||||
|
||||
## Turn 5
|
||||
|
||||
---
|
||||
|
||||
## Turn 6
|
||||
|
||||
---
|
||||
|
||||
## Turn 7
|
||||
|
||||
---
|
||||
|
||||
## Turn 8
|
||||
|
||||
---
|
||||
|
||||
## Turn 9
|
||||
|
||||
---
|
||||
|
||||
## Turn 10
|
||||
|
||||
---
|
||||
|
||||
## Turn 11
|
||||
|
||||
---
|
||||
|
||||
## Turn 12
|
||||
|
||||
---
|
||||
|
||||
## Turn 13
|
||||
|
||||
---
|
||||
|
||||
## Turn 14
|
||||
|
||||
---
|
||||
|
||||
## Turn 15
|
||||
|
||||
---
|
||||
|
||||
## Turn 16
|
||||
|
||||
---
|
||||
|
||||
## Turn 17
|
||||
|
||||
---
|
||||
|
||||
## Turn 18
|
||||
|
||||
---
|
||||
|
||||
## Turn 19
|
||||
|
||||
---
|
||||
|
||||
## Turn 20
|
||||
|
||||
---
|
||||
|
||||
|
||||
_... 23 more turns omitted for brevity_
|
||||
Reference in New Issue
Block a user