Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Parses and merges Claude .jsonl transcripts with debug logs to generate a timeline heuristics report of API limits, context overflows, hook blocks, and tool failures.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Determine the target session. Two modes:
--session <uuid> to scan ~/.claude/projects/<project-dir>/ for a .jsonl file whose name contains the UUID.--session): scan the project directory (derived from process.cwd()) for the most recently modified .jsonl file. Fall back to a global scan of ~/.claude/projects/ if the project directory does not exist.Project folder name derivation: replace the drive colon+backslash with --, then replace remaining path separators with -.
Example: C:\dev\projects\agent-studio → C--dev-projects-agent-studio.
Once the session ID is extracted from the transcript filename (<uuid>.jsonl), look for a matching debug log at ~/.claude/debug/<uuid>.txt. If not found, do a prefix/contains scan of the debug directory. Warn but continue if no debug log is found.
Read the file line by line. Skip blank or malformed lines. For each valid JSON object:
event.type === 'user' where event.message.content[0].type === 'text'type === 'tool_use'; accumulate per-tool counts in a frequency maptype === 'tool_result' and is_error === trueRead line by line. Collect lines that match any of:
[ERROR] or error: — general errorsBLOCKED — hook blocks (router lockdown, creator guard)prompt is too long — context-length API errorsSyntaxError: Unterminated string in JSON — hook JSON parse failuresBuild a Markdown report with these sections:
ROUTER-LOCKDOWN, TASK-CREATE VIOLATION)prompt is too long)Unterminated string in JSON)Default output path: .tmp/transcript-analysis-<first-8-chars-of-uuid>.md (create .tmp/ if needed).
If --out <path> was supplied, write there instead.
</execution_process>
<best_practices>
JSON.parse() in try/catch per line; never abort on a single bad line.process.cwd() not from a hardcoded path — supports running from any agent-studio checkout location.</best_practices> </instructions>
<examples> <usage_example> **Analyze the most recent session (auto-detect):**node scripts/analyze-session-transcript.mjs
# or
pnpm debug:analyze
Analyze a specific session by UUID:
node scripts/analyze-session-transcript.mjs --session f1326443-490f-486b-bb67-01c72bf42408
Write report to a custom path:
node scripts/analyze-session-transcript.mjs --out .claude/context/reports/backend/session-analysis-2026-03-10.md
Typical report output structure:
# Session Analysis Report: f1326443-490f-486b-bb67-01c72bf42408
Generated: 2026-03-10T12:00:00.000Z
## Overview Metrics
- **User Turns:** 14
- **Tool Invocations:** 87
- **Tool Errors:** 3
- **Debug Log Errors:** 2
## Tool Usage Summary
- `Read`: 34 times
- `Edit`: 18 times
- `Bash`: 15 times
- `Write`: 12 times
- `TaskUpdate`: 8 times
## Heuristic Findings
### Context Length Exceeded (API Error)
The orchestrator accumulated too many tokens and crashed the API request.
Occurred **1** times.
## Top Tool Failures (from Transcript UI)
- `toolu_01ABC`: File not found: .claude/context/runtime/stale-tasks.json...
</usage_example> </examples>
For code discovery and search tasks, follow this priority order:
Before starting: ```bash cat .claude/context/memory/learnings.md cat .claude/context/memory/decisions.md ```
After completing:
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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