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。
Extract actionable insights from completed coding sessions. Use when a session completes to capture learnings for future sessions.
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Analyze completed coding sessions and extract structured learnings for the memory system. Insights help future sessions avoid mistakes, follow established patterns, and understand the codebase faster.
Core principle: Extract ACTIONABLE knowledge, not logs. Every insight should help a future session do something better.
Always:
Exceptions:
NO SESSION END WITHOUT INSIGHT EXTRACTION FOR NON-TRIVIAL WORK
Non-trivial sessions should capture learnings before context is lost.
To extract insights, you need:
# Get the diff of changes
git diff HEAD~1 --stat
git diff HEAD~1
# Get commit message
git log -1 --pretty=format:"%s%n%n%b"
# Get list of modified files
git diff HEAD~1 --name-only
For each modified file, extract:
Good example:
{
"path": "src/stores/terminal-store.ts",
"purpose": "Zustand store managing terminal session state with immer middleware",
"changes_made": "Added setAssociatedTask action to link terminals with tasks",
"patterns_used": ["Zustand action pattern", "immer state mutation"],
"gotchas": ["State changes must go through actions, not direct mutation"]
}
Bad example (too vague):
{
"path": "src/stores/terminal-store.ts",
"purpose": "A store file",
"changes_made": "Added some code",
"patterns_used": [],
"gotchas": []
}
Only extract patterns that are reusable:
Good example:
{
"pattern": "Use e.stopPropagation() on interactive elements inside containers with onClick handlers",
"applies_to": "Any clickable element nested inside a parent with click handling",
"example": "Terminal.tsx header - dropdown needs stopPropagation to prevent focus stealing"
}
Must be specific and actionable:
Good example:
{
"gotcha": "Terminal header onClick steals focus from child interactive elements",
"trigger": "Adding buttons/dropdowns to Terminal header without stopPropagation",
"solution": "Call e.stopPropagation() in onClick handlers of child elements"
}
Capture the learning from success or failure:
This helps future sessions learn from past attempts.
Specific, actionable advice for future work:
Good: "When adding more controls to Terminal header, follow the dropdown pattern in this session - use stopPropagation and position relative to header"
Bad: "Write good code" or "Test thoroughly"
Create the structured insight output:
# Session Insights: [Task Name]
## Date
[timestamp]
## Task
[Description of what was being implemented]
## Outcome
[SUCCESS/FAILURE]
## File Insights
### [file-path]
- **Purpose**: [what this file does]
- **Changes**: [what was changed and why]
- **Patterns**: [patterns used]
- **Gotchas**: [things to watch out for]
## Patterns Discovered
### [Pattern Name]
- **Pattern**: [description]
- **Applies to**: [when to use]
- **Example**: [file or code reference]
## Gotchas Discovered
### [Gotcha Name]
- **Issue**: [what to avoid]
- **Trigger**: [what causes it]
- **Solution**: [how to handle]
## Approach Analysis
### What Worked
[Description of successful approach]
### What Failed (if applicable)
[Description of failed approaches and why]
### Alternatives Tried
[List of other approaches attempted]
## Recommendations for Future Sessions
1. [Specific recommendation 1]
2. [Specific recommendation 2]
Save to .claude/context/memory/learnings.md (append).
If the diff is very small or empty:
If the session failed:
Before completing insight extraction:
Why it's wrong: "Fixed the bug" helps no one.
Do this instead: "Fixed race condition in useEffect by adding cleanup function. Pattern: always return cleanup from async effects."
Why it's wrong: "Test your code" is not actionable.
Do this instead: "Run npm test src/stores after changing store logic - the tests catch state management bugs."
Why it's wrong: Future sessions won't understand why.
Do this instead: Include file paths, function names, and specific scenarios.
This skill works well with:
[CODE], [WORKFLOW], [SECURITY], etc.) — untagged insights are effectively invisible to agents searching by domain.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Recording activity ("implemented auth") instead of learnings | Agents can reconstruct history from git; they cannot reconstruct tacit knowledge | Record "why" and "gotcha": "Fiber's CSRF middleware must be before route registration, not after" |
| Skipping deduplication check | Duplicate entries inflate memory size and confuse retrieval | grep -i "keyword" learnings.md before appending; update existing entry if found |
| Vague insights without file/function context | Future sessions can't locate or apply the insight | Include concrete path: "In .claude/hooks/routing/routing-guard.cjs line 47: exit 0 on parse error" |
| Extracting only at session end | Long sessions lose early context; critical gotchas forgotten | Extract after each significant task completion, not just at session boundary |
| Storing insights only in task metadata | Task metadata is not read by future sessions or agents | Always write to .claude/context/memory/learnings.md with tagged format |
Before starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
まだレビューはありません。使ってみた感想をお寄せください。
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。
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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.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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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。