Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Estimate per-turn token attribution across 6 categories in Claude Code sessions to show where context budget is spent
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Estimate per-turn token attribution across 6 categories in Claude Code sessions. Based on claude-devtools visible context tracker.
Skill({ skill: 'context-attribution' });
Use when: context pressure is high, optimizing CLAUDE.md sizes, understanding which tool calls consume the most tokens, debugging context overflow.
| Category | Detection Pattern | Typical % |
|---|---|---|
| CLAUDE.md files | System messages with claudeMd or CLAUDE.md content | 20-40% |
| @-mentioned files | Read tool results triggered by user file references | 10-20% |
| Tool outputs | All tool_result content blocks | 15-30% |
| AI thinking/text | Assistant message content (text + thinking blocks) | 10-25% |
| Team coordination | Messages containing <teammate-message> XML | 0-15% |
| User messages | User role messages (prompts, follow-ups) | 5-15% |
# Find most recent session
SESSION=$(ls -t ~/.claude/projects/$(pwd | sed 's|/|-|g; s|^-||')/*.jsonl | head -1)
For each message, classify into one of the 6 categories and estimate tokens:
# Count user messages (Category 6)
grep '"role":"user"' "$SESSION" | grep -v '"tool_result"' | wc -l
# Count tool results (Categories 2-3)
grep '"type":"tool_result"' "$SESSION" | wc -l
# Count assistant output (Category 4)
grep '"role":"assistant"' "$SESSION" | wc -l
# Check for team messages (Category 5)
grep 'teammate-message' "$SESSION" | wc -l
Use the usage field from each assistant turn for accurate counts. Fall back to chars/4 when unavailable.
Turn | CLAUDE.md | Files | Tools | AI Out | Team | User | Total
-----|-----------|-------|-------|--------|------|------|------
1 | 12,400 | 0 | 0 | 800 | 0 | 200 | 13,400
2 | 0 | 3,200 | 1,500 | 2,100 | 0 | 150 | 6,950
... | ... | ... | ... | ... | ... | ... | ...
Report which category consumes the most tokens and suggest optimizations (e.g., reduce CLAUDE.md size, compress tool outputs).
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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概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。