A skill for automatically saving conversation history to persistent session log files.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsession-loggerExecute the skills CLI command in your project's root directory to begin installation:
Fetches session-logger from charon-fan/agent-playbook 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-logger. Access via /session-logger 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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A skill for automatically saving conversation history to persistent session log files.
This skill activates when you:
All sessions are saved to: sessions/YYYY-MM-DD-{topic}.md
For each session, log:
Metadata
Summary
Actions Taken
Technical Notes
Open Questions
# Session: {Topic}
**Date**: {YYYY-MM-DD}
**Duration**: {approximate}
**Context**: {project/directory}
## Summary
{What was accomplished in this session}
## Key Decisions
1. {Decision 1}
2. {Decision 2}
## Actions Taken
- [x] {Completed action 1}
- [x] {Completed action 2}
- [ ] {Pending action 3}
## Technical Notes
{Important technical details}
## Open Questions / Follow-ups
- {Question 1}
- {Question 2}
## Related Files
- `{file-path}` - {what changed}
Simply say:
"保存对话信息"
The skill will:
Specify the session topic:
"保存对话,主题是 skill-router 创建"
If auto-extraction misses something, provide details:
"保存对话,重点是:1) 创建了 skill-router,2) 修复了 front matter"
| Input | Filename |
|---|---|
| "保存对话" | YYYY-MM-DD-session.md |
| "保存对话,主题是 prd" | YYYY-MM-DD-prd.md |
| "保存今天的讨论" | YYYY-MM-DD-discussion.md |
sessions/
├── README.md # This file
├── 2025-01-11-skill-router.md # Session about skill-router
├── 2025-01-11-prd-planner.md # Session about PRD planner
└── 2025-01-12-refactoring.md # Session about refactoring
Session logs are stored in sessions/ which is in .gitignore.
| You say | Skill does |
|---|---|
| "保存对话信息" | Creates session log with today's date |
| "保存今天的对话" | Creates session log |
| "保存session" | Creates session log |
| "记录会话" | Creates session log |
When triggered by other skills via hooks, session-logger extracts structured data for learning:
When a skill completes, capture:
## Skill Execution Context
**Skill**: {skill-name}
**Trigger**: {user-invoked | hook-triggered | auto-triggered}
**Status**: {completed | error | partial}
**Duration**: {approximate time}
### Input Context
- User request: {original request}
- Files involved: {list of files}
- Codebase patterns detected: {patterns}
### Output Summary
- Actions taken: {list}
- Files modified: {list with changes}
- Decisions made: {key decisions}
### Learning Signals
- What worked well: {successes}
- What could improve: {areas for improvement}
- Patterns discovered: {new patterns}
- Errors encountered: {errors and resolutions}
When a skill encounters errors:
## Error Context
**Error Type**: {type}
**Error Message**: {message}
**Stack Trace**: {if available}
### Resolution Attempted
- Approach: {what was tried}
- Result: {success/failure}
- Root cause: {if identified}
### Prevention Notes
- How to avoid: {prevention strategy}
- Related patterns: {similar issues}
Extract reusable patterns for the self-improving-agent:
## Extracted Patterns
### Code Patterns
- Pattern name: {name}
- Context: {when to use}
- Example: {code snippet}
### Workflow Patterns
- Trigger: {what initiates}
- Steps: {sequence}
- Outcome: {expected result}
### Anti-Patterns
- Pattern: {what to avoid}
- Why: {reason}
- Alternative: {better approach}
For machine-readable extraction, use YAML front matter in session logs:
---
session_type: skill_execution
skill_name: code-reviewer
trigger_source: hook
status: completed
files_modified:
- path: src/utils.ts
changes: refactored error handling
patterns_learned:
- name: error-boundary-pattern
category: error-handling
confidence: high
errors_encountered: []
learning_signals:
successes:
- "Identified code smell in utils.ts"
improvements:
- "Could have suggested more specific refactoring"
---
When triggered by self-improving-agent:
When invoked via hooks with mode: auto:
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.
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cursor/plugins
ailabs-393/ai-labs-claude-skills
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mattpocock/skills
session-logger has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: session-logger is focused, and the summary matches what you get after install.
We added session-logger from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in session-logger — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend session-logger for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
session-logger fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for session-logger matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: session-logger is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for session-logger matched our evaluation — installs cleanly and behaves as described in the markdown.
session-logger reduced setup friction for our internal harness; good balance of opinion and flexibility.
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