Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add Charon-Fan/agent-playbook --skill "session-logger"
Install specific skill from multi-skill repository
# Description
Saves conversation history to session log files. Use when user says "保存对话", "保存对话信息", "记录会话", "save session", or "save conversation". Automatically creates timestamped session log in sessions/ directory.
# SKILL.md
name: session-logger
description: Saves conversation history to session log files. Use when user says "保存对话", "保存对话信息", "记录会话", "save session", or "save conversation". Automatically creates timestamped session log in sessions/ directory.
allowed-tools: Read, Write, Edit, Bash
Session Logger
A skill for automatically saving conversation history to persistent session log files.
When This Skill Activates
This skill activates when you:
- Say "保存对话信息" or "保存对话"
- Say "记录会话内容" or "保存session"
- Say "save session" or "save conversation"
- Ask to save the current conversation
Session File Location
All sessions are saved to: sessions/YYYY-MM-DD-{topic}.md
What Gets Logged
For each session, log:
- Metadata
- Date and duration
- Context/working directory
-
Main topic
-
Summary
- What was accomplished
- Key decisions made
-
Files created/modified
-
Actions Taken
- Checklist of completed tasks
-
Pending follow-ups
-
Technical Notes
- Important code snippets
- Commands used
-
Solutions found
-
Open Questions
- Issues to revisit
- Follow-up tasks
Session Template
# 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}
How to Use
Option 1: Automatic Logging
Simply say:
"保存对话信息"
The skill will:
1. Review the conversation history
2. Extract key information
3. Create/update the session file
Option 2: With Topic
Specify the session topic:
"保存对话,主题是 skill-router 创建"
Option 3: Manual Prompt
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"
File Naming
| 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 |
Session Log Structure
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
Privacy Note
Session logs are stored in sessions/ which is in .gitignore.
- Logs are NOT committed to git
- Logs contain your actual conversation
- Safe to include sensitive information
Quick Reference
| 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 |
Best Practices
- Save at key milestones: After completing a feature, fixing a bug, etc.
- Be specific with topics: Helps when searching later
- Include code snippets: Save important solutions
- Track decisions: Why did you choose X over Y?
- List pending items: What to do next time
Rich Content Extraction (for Self-Improving Agent)
When triggered by other skills via hooks, session-logger extracts structured data for learning:
Skill Context Capture
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}
Error Context Capture
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}
Pattern Extraction
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}
Structured Data Format
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"
---
Integration with Self-Improving Agent
When triggered by self-improving-agent:
- Extract episodic memory: Capture the full context of what happened
- Identify semantic patterns: Tag reusable knowledge
- Update working memory: Note immediate follow-ups needed
- Signal completion: Write trigger file if skill chaining is needed
Auto-Trigger Behavior
When invoked via hooks with mode: auto:
- Silently create/update session log
- Extract structured data without user interaction
- Append to existing session if same day/topic
- Create new session if context differs significantly
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.