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session-logger会话记录器

Agent Skill

session-logger 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

11,579

周安装

473

GitHub Stars

47

下载量

3,708
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:session-logger(会话记录器)
来源仓库:https://github.com/charon-fan/agent-playbook
仓库路径:skills/session-logger
安装命令:
npx skills add https://github.com/charon-fan/agent-playbook --skill session-logger
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/charon-fan/agent-playbook --skill session-logger

简介

会话记录器用于自动提取对话中的成就、决策与待办事项。

  • 适合保存会议纪要、技术细节与后续行动清单。
  • 支持多种触发方式,并可扩展为自我改进代理的学习源。
  • 日志默认存储于 sessions/ 目录,请确保其不被纳入 git 提交。
  • session-logger 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

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:

  1. Metadata

- Date and duration - Context/working directory - Main topic

  1. Summary

- What was accomplished - Key decisions made - Files created/modified

  1. Actions Taken

- Checklist of completed tasks - Pending follow-ups

  1. Technical Notes

- Important code snippets - Commands used - Solutions found

  1. 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

InputFilename
"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 saySkill 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

  1. Save at key milestones: After completing a feature, fixing a bug, etc.
  2. Be specific with topics: Helps when searching later
  3. Include code snippets: Save important solutions
  4. Track decisions: Why did you choose X over Y?
  5. 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:

  1. Extract episodic memory: Capture the full context of what happened
  2. Identify semantic patterns: Tag reusable knowledge
  3. Update working memory: Note immediate follow-ups needed
  4. 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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

31.16%
按下载量换算1,155

OpenCode

23.79%
按下载量换算882

Codex

17.26%
按下载量换算640

Antigravity

12.98%
按下载量换算481

Gemini CLI

8.82%
按下载量换算327

windsurf

3.95%
按下载量换算146

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

未通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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