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Replace search skill with mem-search (#91)

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* feat: add mem-search skill with progressive disclosure architecture

Add comprehensive mem-search skill for accessing claude-mem's persistent
cross-session memory database. Implements progressive disclosure workflow
and token-efficient search patterns.

Features:
- 12 search operations (observations, sessions, prompts, by-type, by-concept, by-file, timelines, etc.)
- Progressive disclosure principles to minimize token usage
- Anti-patterns documentation to guide LLM behavior
- HTTP API integration for all search functionality
- Common workflows with composition examples

Structure:
- SKILL.md: Entry point with temporal trigger patterns
- principles/: Progressive disclosure + anti-patterns
- operations/: 12 search operation files

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add CHANGELOG entry for mem-search skill

Document mem-search skill addition in Unreleased section with:
- 100% effectiveness compliance metrics
- Comparison to previous search skill implementation
- Progressive disclosure architecture details
- Reference to audit report documentation

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add mem-search skill audit report

Add comprehensive audit report validating mem-search skill against
Anthropic's official skill-creator documentation.

Report includes:
- Effectiveness metrics comparison (search vs mem-search)
- Critical issues analysis for production readiness
- Compliance validation across 6 key dimensions
- Reference implementation guidance

Result: mem-search achieves 100% compliance vs search's 67%

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: Add comprehensive search architecture analysis document

- Document current state of dual search architectures (HTTP API and MCP)
- Analyze HTTP endpoints and MCP search server architectures
- Identify DRY violations across search implementations
- Evaluate the use of curl as the optimal approach for search
- Provide architectural recommendations for immediate and long-term improvements
- Outline action plan for cleanup, feature parity, DRY refactoring

* refactor: Remove deprecated search skill documentation and operations

* refactor: Reorganize documentation into public and context directories

Changes:
- Created docs/public/ for Mintlify documentation (.mdx files)
- Created docs/context/ for internal planning and implementation docs
- Moved all .mdx files and assets to docs/public/
- Moved all internal .md files to docs/context/
- Added CLAUDE.md to both directories explaining their purpose
- Updated docs.json paths to work with new structure

Benefits:
- Clear separation between user-facing and internal documentation
- Easier to maintain Mintlify docs in dedicated directory
- Internal context files organized separately

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Enhance session management and continuity in hooks

- Updated new-hook.ts to clarify session_id threading and idempotent session creation.
- Modified prompts.ts to require claudeSessionId for continuation prompts, ensuring session context is maintained.
- Improved SessionStore.ts documentation on createSDKSession to emphasize idempotent behavior and session connection.
- Refined SDKAgent.ts to detail continuation prompt logic and its reliance on session.claudeSessionId for unified session handling.

---------

Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Alex Newman <thedotmack@gmail.com>
This commit is contained in:
basher83
2025-11-11 16:15:07 -05:00
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commit 97d565e3cd
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# Streaming Input
> Understanding the two input modes for Claude Agent SDK and when to use each
## Overview
The Claude Agent SDK supports two distinct input modes for interacting with agents:
* **Streaming Input Mode** (Default & Recommended) - A persistent, interactive session
* **Single Message Input** - One-shot queries that use session state and resuming
This guide explains the differences, benefits, and use cases for each mode to help you choose the right approach for your application.
## Streaming Input Mode (Recommended)
Streaming input mode is the **preferred** way to use the Claude Agent SDK. It provides full access to the agent's capabilities and enables rich, interactive experiences.
It allows the agent to operate as a long lived process that takes in user input, handles interruptions, surfaces permission requests, and handles session management.
### How It Works
```mermaid theme={null}
%%{init: {"theme": "base", "themeVariables": {"edgeLabelBackground": "#F0F0EB", "lineColor": "#91918D", "primaryColor": "#F0F0EB", "primaryTextColor": "#191919", "primaryBorderColor": "#D9D8D5", "secondaryColor": "#F5E6D8", "tertiaryColor": "#CC785C", "noteBkgColor": "#FAF0E6", "noteBorderColor": "#91918D"}, "sequence": {"actorMargin": 50, "width": 150, "height": 65, "boxMargin": 10, "boxTextMargin": 5, "noteMargin": 10, "messageMargin": 35}}}%%
sequenceDiagram
participant App as Your Application
participant Agent as Claude Agent
participant Tools as Tools/Hooks
participant FS as Environment/<br/>File System
App->>Agent: Initialize with AsyncGenerator
activate Agent
App->>Agent: Yield Message 1
Agent->>Tools: Execute tools
Tools->>FS: Read files
FS-->>Tools: File contents
Tools->>FS: Write/Edit files
FS-->>Tools: Success/Error
Agent-->>App: Stream partial response
Agent-->>App: Stream more content...
Agent->>App: Complete Message 1
App->>Agent: Yield Message 2 + Image
Agent->>Tools: Process image & execute
Tools->>FS: Access filesystem
FS-->>Tools: Operation results
Agent-->>App: Stream response 2
App->>Agent: Queue Message 3
App->>Agent: Interrupt/Cancel
Agent->>App: Handle interruption
Note over App,Agent: Session stays alive
Note over Tools,FS: Persistent file system<br/>state maintained
deactivate Agent
```
### Benefits
<CardGroup cols={2}>
<Card title="Image Uploads" icon="image">
Attach images directly to messages for visual analysis and understanding
</Card>
<Card title="Queued Messages" icon="layer-group">
Send multiple messages that process sequentially, with ability to interrupt
</Card>
<Card title="Tool Integration" icon="wrench">
Full access to all tools and custom MCP servers during the session
</Card>
<Card title="Hooks Support" icon="link">
Use lifecycle hooks to customize behavior at various points
</Card>
<Card title="Real-time Feedback" icon="bolt">
See responses as they're generated, not just final results
</Card>
<Card title="Context Persistence" icon="database">
Maintain conversation context across multiple turns naturally
</Card>
</CardGroup>
### Implementation Example
<CodeGroup>
```typescript TypeScript theme={null}
import { query } from "@anthropic-ai/claude-agent-sdk";
import { readFileSync } from "fs";
async function* generateMessages() {
// First message
yield {
type: "user" as const,
message: {
role: "user" as const,
content: "Analyze this codebase for security issues"
}
};
// Wait for conditions or user input
await new Promise(resolve => setTimeout(resolve, 2000));
// Follow-up with image
yield {
type: "user" as const,
message: {
role: "user" as const,
content: [
{
type: "text",
text: "Review this architecture diagram"
},
{
type: "image",
source: {
type: "base64",
media_type: "image/png",
data: readFileSync("diagram.png", "base64")
}
}
]
}
};
}
// Process streaming responses
for await (const message of query({
prompt: generateMessages(),
options: {
maxTurns: 10,
allowedTools: ["Read", "Grep"]
}
})) {
if (message.type === "result") {
console.log(message.result);
}
}
```
```python Python theme={null}
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions, AssistantMessage, TextBlock
import asyncio
import base64
async def streaming_analysis():
async def message_generator():
# First message
yield {
"type": "user",
"message": {
"role": "user",
"content": "Analyze this codebase for security issues"
}
}
# Wait for conditions
await asyncio.sleep(2)
# Follow-up with image
with open("diagram.png", "rb") as f:
image_data = base64.b64encode(f.read()).decode()
yield {
"type": "user",
"message": {
"role": "user",
"content": [
{
"type": "text",
"text": "Review this architecture diagram"
},
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": image_data
}
}
]
}
}
# Use ClaudeSDKClient for streaming input
options = ClaudeAgentOptions(
max_turns=10,
allowed_tools=["Read", "Grep"]
)
async with ClaudeSDKClient(options) as client:
# Send streaming input
await client.query(message_generator())
# Process responses
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(block.text)
asyncio.run(streaming_analysis())
```
</CodeGroup>
## Single Message Input
Single message input is simpler but more limited.
### When to Use Single Message Input
Use single message input when:
* You need a one-shot response
* You do not need image attachments, hooks, etc.
* You need to operate in a stateless environment, such as a lambda function
### Limitations
<Warning>
Single message input mode does **not** support:
* Direct image attachments in messages
* Dynamic message queueing
* Real-time interruption
* Hook integration
* Natural multi-turn conversations
</Warning>
### Implementation Example
<CodeGroup>
```typescript TypeScript theme={null}
import { query } from "@anthropic-ai/claude-agent-sdk";
// Simple one-shot query
for await (const message of query({
prompt: "Explain the authentication flow",
options: {
maxTurns: 1,
allowedTools: ["Read", "Grep"]
}
})) {
if (message.type === "result") {
console.log(message.result);
}
}
// Continue conversation with session management
for await (const message of query({
prompt: "Now explain the authorization process",
options: {
continue: true,
maxTurns: 1
}
})) {
if (message.type === "result") {
console.log(message.result);
}
}
```
```python Python theme={null}
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage
import asyncio
async def single_message_example():
# Simple one-shot query using query() function
async for message in query(
prompt="Explain the authentication flow",
options=ClaudeAgentOptions(
max_turns=1,
allowed_tools=["Read", "Grep"]
)
):
if isinstance(message, ResultMessage):
print(message.result)
# Continue conversation with session management
async for message in query(
prompt="Now explain the authorization process",
options=ClaudeAgentOptions(
continue_conversation=True,
max_turns=1
)
):
if isinstance(message, ResultMessage):
print(message.result)
asyncio.run(single_message_example())
```
</CodeGroup>