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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>
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For tracking costs and tokens in your Agent SDK plugin, you have built-in programmatic access to usage data through the SDK itself(1).
Agent SDK Cost Tracking
The Claude Agent SDK provides detailed token usage information for each interaction(1). Here's how to track it:
TypeScript:
import { query } from "@anthropic-ai/claude-agent-sdk";
const result = await query({
prompt: "Your task here",
options: {
onMessage: (message) => {
if (message.type === 'assistant' && message.usage) {
console.log(`Message ID: ${message.id}`);
console.log(`Usage:`, message.usage);
}
}
}
});
The final result message contains the total cumulative usage from all steps in the conversation(1):
console.log("Total usage:", result.usage);
console.log("Total cost:", result.usage.total_cost_usd);
Important: Avoid Double-Counting
When Claude executes tools in parallel, multiple assistant messages may share the same ID and usage data(1). You should only charge once per unique message ID(1):
const processedMessageIds = new Set<string>();
onMessage: (message) => {
if (message.type === 'assistant' && message.usage) {
// Skip if already processed
if (processedMessageIds.has(message.id)) {
return;
}
processedMessageIds.add(message.id);
// Record usage here
}
}
Usage Fields
Each usage object contains(1):
input_tokens: Base input tokens processedoutput_tokens: Tokens generated in the responsecache_creation_input_tokens: Tokens used to create cache entriescache_read_input_tokens: Tokens read from cachetotal_cost_usd: Total cost in USD (only in result message)