session-wrap
The session-wrap skill orchestrates a multi-agent analysis of completed coding work by examining git changes and running four parallel analysis agents that evaluate documentation updates, automation opportunities, key learnings, and follow-up tasks. Use this skill when ending a coding session to systematically document progress, extract insights, identify patterns for automation, and surface next steps before committing work.
git clone --depth 1 https://github.com/team-attention/plugins-for-claude-natives /tmp/session-wrap && cp -r /tmp/session-wrap/plugins/session-wrap/skills/session-wrap ~/.claude/skills/session-wrapSKILL.md
# Session Wrap Skill
Comprehensive session wrap-up workflow with multi-agent analysis.
## Execution Flow
```
┌─────────────────────────────────────────────────────┐
│ 1. Check Git Status │
├─────────────────────────────────────────────────────┤
│ 2. Phase 1: 4 Analysis Agents (Parallel) │
│ ┌─────────────────┬─────────────────┐ │
│ │ doc-updater │ automation- │ │
│ │ (docs update) │ scout │ │
│ ├─────────────────┼─────────────────┤ │
│ │ learning- │ followup- │ │
│ │ extractor │ suggester │ │
│ └─────────────────┴─────────────────┘ │
├─────────────────────────────────────────────────────┤
│ 3. Phase 2: Validation Agent (Sequential) │
│ ┌───────────────────────────────────┐ │
│ │ duplicate-checker │ │
│ │ (Validate Phase 1 proposals) │ │
│ └───────────────────────────────────┘ │
├─────────────────────────────────────────────────────┤
│ 4. Integrate Results & AskUserQuestion │
├─────────────────────────────────────────────────────┤
│ 5. Execute Selected Actions │
└─────────────────────────────────────────────────────┘
```
## Step 1: Check Git Status
```bash
git status --short
git diff --stat HEAD~3 2>/dev/null || git diff --stat
```
## Step 2: Phase 1 - Analysis Agents (Parallel)
Execute 4 agents in parallel (single message with 4 Task calls).
### Session Summary (Provide to all agents)
```
Session Summary:
- Work: [Main tasks performed in session]
- Files: [Created/modified files]
- Decisions: [Key decisions made]
```
### Parallel Execution
```
Task(
subagent_type="doc-updater",
description="Document update analysis",
prompt="[Session Summary]\n\nAnalyze if CLAUDE.md, context.md need updates."
)
Task(
subagent_type="automation-scout",
description="Automation pattern analysis",
prompt="[Session Summary]\n\nAnalyze repetitive patterns or automation opportunities."
)
Task(
subagent_type="learning-extractor",
description="Learning points extraction",
prompt="[Session Summary]\n\nExtract learnings, mistakes, and new discoveries."
)
Task(
subagent_type="followup-suggester",
description="Follow-up task suggestions",
prompt="[Session Summary]\n\nSuggest incomplete tasks and next session priorities."
)
```
### Agent Roles
| Agent | Role | Output |
|-------|------|--------|
| **doc-updater** | Analyze CLAUDE.md/context.md updates | Specific content to add |
| **automation-scout** | Detect automation patterns | skill/command/agent suggestions |
| **learning-extractor** | Extract learning points | TIL format summary |
| **followup-suggester** | Suggest follow-up tasks | Prioritized task list |
## Step 3: Phase 2 - Validation Agent (Sequential)
Run after Phase 1 completes (dependency on Phase 1 results).
```
Task(
subagent_type="duplicate-checker",
description="Phase 1 proposal validation",
prompt="""
Validate Phase 1 analysis results.
## doc-updater proposals:
[doc-updater results]
## automation-scout proposals:
[automation-scout results]
Check if proposals duplicate existing docs/automation:
1. Complete duplicate: Recommend skip
2. Partial duplicate: Suggest merge approach
3. No duplicate: Approve for addition
"""
)
```
## Step 4: Integrate Results
```markdown
## Wrap Analysis Results
### Documentation Updates
[doc-updater summary]
- Duplicate check: [duplicate-checker feedback]
### Automation Suggestions
[automation-scout summary]
- Duplicate check: [duplicate-checker feedback]
### Learning Points
[learning-extractor summary]
### Follow-up Tasks
[followup-suggester summary]
```
## Step 5: Action Selection
```
AskUserQuestion(
questions=[{
"question": "Which actions would you like to perform?",
"header": "Wrap Options",
"multiSelect": true,
"options": [
{"label": "Create commit (Recommended)", "description": "Commit changes"},
{"label": "Update CLAUDE.md", "description": "Document new knowledge/workflows"},
{"label": "Create automation", "description": "Generate skill/command/agent"},
{"label": "Skip", "description": "End without action"}
]
}]
)
```
## Step 6: Execute Selected Actions
Execute only the actions selected by user.
---
## Quick Reference
### When to Use
- End of significant work session
- Before switching to different project
- After completing a feature or fixing a bug
### When to Skip
- Very short session with trivial changes
- Only reading/exploring code
- Quick one-off question answered
### Arguments
- Empty: Proceed interactively (full workflow)
- Message provided: Use as commit message and commit directly
## Additional Resources
See `references/multi-agent-patterns.md` for detailed orchestration patterns.Collect and synthesize opinions from multiple AI agents. Use when users say "summon the council", "ask other AIs", or want multiple AI perspectives on a question.
This skill should be used when the user is building, planning, or strategizing and the key question is whether to optimize content (what) or change form (how/medium). Trigger on "내용 vs 형식", "content vs form", "metamedium", "형식을 바꿔볼까", "새로운 포맷", "관점 전환", "perspective shift", "다른 방법 없을까", "같은 방식이 안 먹혀", "diminishing returns". Applies Alan Kay's metamedium concept to surface form-level alternatives. For requirement clarification use vague; for strategy blind spots use unknown.
This skill should be used when the user provides a strategy, plan, or decision document and wants to surface hidden assumptions and blind spots using the Known/Unknown 4-quadrant framework. Trigger on "known unknown", "4분면 분석", "blind spots", "뭘 놓치고 있지", "뭘 모르는지 모르겠어", "전략 점검", "전략 분석", "assumption check", "가정 점검", "quadrant analysis", "what am I missing". Strategy-level blind spot analysis with hypothesis-driven questioning. For requirement clarification use vague; for content-vs-form reframing use metamedium.
This skill should be used when the user's request or requirement is ambiguous and needs iterative questioning to become actionable. Trigger on "clarify requirements", "refine requirements", "요구사항 명확히", "요구사항 정리", "뭘 원하는 건지", "make this clearer", "spec this out", "scope this", "/clarify". Turns vague inputs into concrete specs. For strategy blind spots use unknown; for content-vs-form reframing use metamedium.
개발 커뮤니티에서 기술 주제에 대한 다양한 의견 수집. "개발자 반응", "커뮤니티 의견", "developer reactions" 요청에 사용. Reddit, HN, Dev.to, Lobsters 등 종합.
This skill should be used when the user asks to "기술 의사결정", "뭐 쓸지 고민", "A vs B", "비교 분석", "라이브러리 선택", "아키텍처 결정", "어떤 걸 써야 할지", "트레이드오프", "기술 선택", "구현 방식 고민", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.
This skill should be used when the user asks to "트윗 가져와", "트윗 번역", "X 게시글 읽어줘", "tweet fetch", "트윗 내용", "트윗 원문", or provides an X/Twitter URL (x.com, twitter.com) and wants to read, translate, or analyze the tweet content. Also useful when other skills need to fetch tweet text programmatically.
This skill should be used when the user asks to "check email", "read emails", "send email", "reply to email", "search inbox", or manages Gmail. Supports multi-account Gmail integration for reading, searching, sending, and label management.