据 AlphaMoat 统计,截至 2026-10,Git-Based Knowledge Graph Memory System for Claude Code 累计下载 8,242 次、获 13 个星标,按累计下载量在本次参与排名的 10,914 个 AI 技能中排名第 3,374 位,在「AI 智能体」分类(1,632 个)中排名第 451 位。
列表默认按累计下载量排序,也可按星标排序;详情页排名按累计下载量计算。
| 平台 | 累计下载 | 星标 | 评论 |
|---|---|---|---|
| ClawHub ↗ | 5,961 | 11 | - |
| SkillHub ↗ | 8,242 | 13 | — |
各平台的计数口径不同,统计时间不一致。
把项目要求、约束、目标与任务约定记进 git notes,形成跟着分支走的持久记忆,切分支时看到的是对应那一条的上下文。规则要求静默使用:不问“要不要记住”、不宣告“我记下了”、也不把记忆命令的输出展示给用户。 写入前核对目标文件、仓库或工作区;本地保存内容可能长期保留,不要包含不必要的敏感信息。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-10 | 8,242 | — | 13 |
| 2026-09 | 6,233 | — | 13 |
| 2026-08 | 5,703 | +2.9% | 13 |
| 2026-07 | 5,544 | — | 11 |
| 2026-05 | 5,207 | — | 13 |
| 2026-03 | 4,702 | — | 11 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: git-notes-memory description: Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions. --- # GitNotesMemory - Claude Integration Guide ## CRITICAL: SILENT OPERATION RULES **These rules are ABSOLUTE and must NEVER be violated:**
1. **NEVER ask** "should I remember?" or "want me to save?"
2. **NEVER announce** "I'll remember this" or "saving to memory"
3. **NEVER show** memory command output to users
4. **NEVER mention** memory system unless user explicitly asks about it
5. **JUST DO IT** - silently in the background
## Quick Reference
```bash
# Replace $SKILL_PATH with actual path to memory.py
# Replace $DIR with the project directory path
python3 $SKILL_PATH/memory.py -p $DIR <command>
```
### Essential Commands
| Command | When to Use |
|---------|-------------|
| `sync --start` | **ALWAYS** at session start |
| `sync --end '{"summary":"..."}'` | At session end (optional but recommended) |
| `remember '{"..."}' -t tags -i h` | Store important information |
| `get <topic>` | Retrieve context about a topic |
| `search "<query>"` | Full-text search across all memories |
| `recall -i <id>` | Get full details of a specific memory |
### Importance Flags
| Flag | Level | When to Use |
|------|-------|-------------|
| `-i c` | Critical | User says "always remember", "never forget", explicit preferences |
| `-i h` | High | Decisions, architecture choices, user corrections, preferences |
| `-i n` | Normal | General information (default) |
| `-i l` | Low | Temporary notes, might be pruned |
## Session Lifecycle
### 1. Session Start (MANDATORY)
**Always run `sync --start` at the beginning of every session:**
```bash
python3 $SKILL_PATH/memory.py -p $DIR sync --start
```
**Output explains:**
```json
{
"b": "main", // Current git branch
"t": {"api": 5, "auth": 3}, // Top topics with memory counts
"c": [...], // Critical memories (always review these)
"n": 42, // Total memory count
"h": [...] // High-importance recent memories
}
```
**Use this context to:**
- Understand what the user has been working on
- Reference previous decisions
- Maintain continuity across sessions
### 2. During Session
**Silently remember when user:**
- Makes a decision: "Let's use PostgreSQL" → remember with `-i h`
- States a preference: "I prefer tabs over spaces" → remember with `-i h` or `-i c`
- Learns something: "Oh, so that's how async works" → remember with `-i n`
- Sets a task: "We need to fix the login bug" → remember with `-i n`
- Shares important context: Project requirements, constraints, goals
**Retrieve context when:**
- User asks about something previously discussed → `get <topic>`
- You need to recall a specific decision → `search "<keywords>"`
- User references "what we decided" → check relevant memories
### 3. Session End (Recommended)
```bash
python3 $SKILL_PATH/memory.py -p $DIR sync --end '{"summary": "Brief session summary"}'
```
## Memory Content Best Practices
### Good Memory Structure
**For decisions:**
```json
{"decision": "Use React for frontend", "reason": "Team expertise", "alternatives": ["Vue", "Angular"]}
```
**For preferences:**
```json
{"preference": "Detailed explanations", "context": "User prefers thorough explanations over brief answers"}
```
**For learnings:**
```json
{"topic": "Authentication", "learned": "OAuth2 flow requires redirect URI configuration"}
```
**For tasks:**
```json
{"task": "Implement user dashboard", "status": "in progress", "blockers": ["API not ready"]}
```
**For notes:**
```json
{"subject": "Project Architecture", "note": "Microservices pattern with API gateway"}
```
### Tags
Use tags to categorize memories for better retrieval:
- `-t architecture,backend` - Technical categories
- `-t urgent,bug` - Priority/type markers
- `-t meeting,requirements` - Source context
## Command Reference
### Core Commands
#### `sync --start`
Initialize session, get context overview.
```bash
python3 $SKILL_PATH/memory.py -p $DIR sync --start
```
#### `sync --end`
End session with summary (triggers maintenance).
```bash
python3 $SKILL_PATH/memory.py -p $DIR sync --end '{"summary": "Implemented auth flow"}'
```
#### `remember`
Store a new memory.
```bash
python3 $SKILL_PATH/memory.py -p $DIR remember '{"key": "value"}' -t tag1,tag2 -i h
```
#### `get`
Get memories related to a topic (searches entities, tags, and content).
```bash
python3 $SKILL_PATH/memory.py -p $DIR get authentication
```
#### `search`
Full-text search across all memories.
```bash
python3 $SKILL_PATH/memory.py -p $DIR search "database migration"
```
#### `recall`
Retrieve memories by various criteria.
```bash
# Get full memory by ID
python3 $SKILL_PATH/memory.py -p $DIR recall -i abc123
# Get memories by tag
python3 $SKILL_PATH/memory.py -p $DIR recall -t architecture
# Get last N memories
python3 $SKILL_PATH/memory.py -p $DIR recall --last 5
# Overview of all memories
python3 $SKILL_PATH/memory.py -p $DIR recall
```
### Update Commands
#### `update`
Modify an existing memory.
```bash
# Replace content
python3 $SKILL_PATH/memory.py -p $DIR update <id> '{"new": "content"}'
# Merge content (add to existing)
python3 $SKILL_PATH/memory.py -p $DIR update <id> '{"extra": "field"}' -m
# Change importance
python3 $SKILL_PATH/memory.py -p $DIR update <id> -i c
# Update tags
python3 $SKILL_PATH/memory.py -p $DIR update <id> -t newtag1,newtag2
```
#### `evolve`
Add an evolution note to track changes over time.
```bash
python3 $SKILL_PATH/memory.py -p $DIR evolve <id> "User changed preference to dark mode"
```
#### `forget`
Delete a memory (use sparingly).
```bash
python3 $SKILL_PATH/memory.py -p $DIR forget <id>
```
### Entity Commands
#### `entities`
List all extracted entities with counts.
```bash
python3 $SKILL_PATH/memory.py -p $DIR entities
```
#### `entity`
Get details about a specific entity.
```bash
python3 $SKILL_PATH/memory.py -p $DIR entity authentication
```
### Branch Commands
#### `branches`
List all branches with memory counts.
```bash
python3 $SKILL_PATH/memory.py -p $DIR branches
```
#### `merge-branch`
Merge memories from another branch (run after git merge).
```bash
python3 $SKILL_PATH/memory.py -p $DIR merge-branch feature-auth
```
## Branch Awareness
### How It Works
- Each git branch has **isolated memory storage**
- New branches **automatically inherit** from main/master
- After git merge, run `merge-branch` to combine memories
### Branch Workflow
```
1. User on main branch → memories stored in refs/notes/mem-main
2. User creates feature branch → auto-inherits main's memories
3. User works on feature → new memories stored in refs/notes/mem-feature-xxx
4. After git merge → run merge-branch to combine memories
```
## Memory Types (Auto-Detected)
The system automatically classifies memories based on content:
| Type | Trigger Words |
|------|---------------|
| `decision` | decided, chose, picked, selected, opted, going with |
| `preference` | prefer, favorite, like best, rather, better to |
| `learning` | learned, studied, understood, realized, discovered |
| `task` | todo, task, need to, plan to, next step, going to |
| `question` | wondering, curious, research, investigate, find out |
| `note` | noticed, observed, important, remember that |
| `progress` | completed, finished, done, achieved, milestone |
| `info` | (default for unclassified content) |
## Entity Extraction
Entities are automatically extracted for intelligent retrieval:
- **Explicit fields**: `topic`, `subject`, `name`, `category`, `area`, `project`
- **Hashtags**: `#cooking`, `#urgent`, `#v2`
- **Quoted phrases**: `"machine learning"`, `"user authentication"`
- **Capitalized words**: `React`, `PostgreSQL`, `Monday`
- **Key terms**: Meaningful words (common words filtered out)
## What to Remember
**DO remember:**
- User decisions and their rationale
- Stated preferences (coding style, communication style, tools)
- Project architecture and constraints
- Important context that affects future work
- Tasks, blockers, and progress
- Corrections ("actually, I meant..." → high importance)
- Explicit requests to remember something → critical importance
**DON'T remember:**
- Trivial conversation
- Information easily derivable from code
- Secrets, passwords, API keys
- One-time questions with no future relevance
- Duplicate information already stored
## Output Format Reference
### Tier 0: sync --start
```json
{
"b": "feature-auth", // Current branch
"t": {"auth": 5, "api": 3, "db": 2}, // Topics with counts
"c": [{"id": "x", "s": "summary", "t": "preference"}], // Critical
"n": 15, // Total count
"h": [{"id": "y", "s": "summary"}] // High importance
}
```
### Tier 1: get/search
```json
{
"topic": "auth",
"mem": [
{"id": "abc", "s": "decided OAuth2", "t": "decision", "i": "h", "b": "main"}
]
}
```
### Tier 2: recall -i <id>
```json
{
"d": {"decision": "Use OAuth2"}, // Full data
"e": ["auth", "oauth2"], // Entities
"t": "decision", // Type
"g": ["architecture"], // Tags
"i": "h", // Importance
"b": "main", // Branch
"c": "2024-01-15T10:30:00", // Created
"u": "2024-01-15T10:30:00", // Updated
"a": 3, // Access count
"ev": [{"n": "note", "t": "..."}] // Evolution notes (if any)
}
```
## Example Silent Flow
```
User: "Let's build a REST API with Python"
Claude: [silently: remember '{"decision": "REST API", "language": "Python"}' -t architecture -i h]
[responds about REST API setup WITHOUT mentioning memory]
User: "I prefer FastAPI over Flask"
Claude: [silently: remember '{"preference": "FastAPI over Flask", "reason": "user preference"}' -i h]
[continues discussion using FastAPI WITHOUT saying "I'll remember"]
User: "What did we decide about the API?"
Claude: [silently: get api]
[uses retrieved context to answer accurately]
User: "Actually, let's use Flask instead"
Claude: [silently: remember '{"decision": "Changed to Flask", "previous": "FastAPI"}' -i h]
[silently: evolve <fastapi-memory-id> "User changed preference to Flask"]
[acknowledges change WITHOUT mentioning memory update]
```
## Troubleshooting
**Memory not found:**
- Use `search` with different keywords
- Check `entities` to see what's indexed
- Use `recall --last 10` to see recent memories
**Context seems stale:**
- Always run `sync --start` at session beginning
- Check current branch with `branches`
**After git operations:**
- After `git merge`: run `merge-branch <source-branch>`
- After `git checkout`: `sync --start` will load correct branch context
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把盛唐诗人李白的诗学观、意象系统与口吻,蒸馏成 AI 可调用的角色包。
数据月份:2026-10 · 下载/星标数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。