Comprehensive session wrap-up workflow with multi-agent analysis.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsession-wrapExecute the skills CLI command in your project's root directory to begin installation:
Fetches session-wrap from ai-native-camp/camp-2 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate session-wrap. Access via /session-wrap in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Comprehensive session wrap-up workflow with multi-agent analysis.
┌─────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────┘
git status --short
git diff --stat HEAD~3 2>/dev/null || git diff --stat
Execute 4 agents in parallel (single message with 4 Task calls).
Session Summary:
- Work: [Main tasks performed in session]
- Files: [Created/modified files]
- Decisions: [Key decisions made]
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 | 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 |
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
"""
)
## 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]
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"}
]
}]
)
Execute only the actions selected by user.
See references/multi-agent-patterns.md for detailed orchestration patterns.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
We added session-wrap from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in session-wrap — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend session-wrap for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: session-wrap is the kind of skill you can hand to a new teammate without a long onboarding doc.
session-wrap has been reliable in day-to-day use. Documentation quality is above average for community skills.
session-wrap reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend session-wrap for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: session-wrap is the kind of skill you can hand to a new teammate without a long onboarding doc.
session-wrap fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
session-wrap reduced setup friction for our internal harness; good balance of opinion and flexibility.
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