GitNotesMemory - Claude Integration Guide
CRITICAL: SILENT OPERATION RULES
These rules are ABSOLUTE and must NEVER be violated:
NEVER ask "should I remember?" or "want me to save?"
NEVER announce "I'll remember this" or "saving to memory"
NEVER show memory command output to users
NEVER mention memory system unless user explicitly asks about it
JUST DO IT - silently in the background
Quick Reference
# 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:
python3 $SKILL_PATH/memory.py -p $DIR sync --start
Output explains:
{
"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 hStates a preference: "I prefer tabs over spaces" → remember with
-i hor-i cLearns something: "Oh, so that's how async works" → remember with
-i nSets a task: "We need to fix the login bug" → remember with
-i nShares 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)
python3 $SKILL_PATH/memory.py -p $DIR sync --end '{"summary": "Brief session summary"}'
Memory Content Best Practices
Good Memory Structure
For decisions:
{"decision": "Use React for frontend", "reason": "Team expertise", "alternatives": ["Vue", "Angular"]}
For preferences:
{"preference": "Detailed explanations", "context": "User prefers thorough explanations over brief answers"}
For learnings:
{"topic": "Authentication", "learned": "OAuth2 flow requires redirect URI configuration"}
For tasks:
{"task": "Implement user dashboard", "status": "in progress", "blockers": ["API not ready"]}
For notes:
{"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.
python3 $SKILL_PATH/memory.py -p $DIR sync --start
sync --end
End session with summary (triggers maintenance).
python3 $SKILL_PATH/memory.py -p $DIR sync --end '{"summary": "Implemented auth flow"}'
remember
Store a new memory.
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).
python3 $SKILL_PATH/memory.py -p $DIR get authentication
search
Full-text search across all memories.
python3 $SKILL_PATH/memory.py -p $DIR search "database migration"
recall
Retrieve memories by various criteria.
# 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.
# 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.
python3 $SKILL_PATH/memory.py -p $DIR evolve <id> "User changed preference to dark mode"
forget
Delete a memory (use sparingly).
python3 $SKILL_PATH/memory.py -p $DIR forget <id>
Entity Commands
entities
List all extracted entities with counts.
python3 $SKILL_PATH/memory.py -p $DIR entities
entity
Get details about a specific entity.
python3 $SKILL_PATH/memory.py -p $DIR entity authentication
Branch Commands
branches
List all branches with memory counts.
python3 $SKILL_PATH/memory.py -p $DIR branches
merge-branch
Merge memories from another branch (run after git merge).
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-branchto 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,projectHashtags:
#cooking,#urgent,#v2Quoted phrases:
"machine learning","user authentication"Capitalized words:
React,PostgreSQL,MondayKey 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
{
"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
{
"topic": "auth",
"mem": [
{"id": "abc", "s": "decided OAuth2", "t": "decision", "i": "h", "b": "main"}
]
}
Tier 2: recall -i
{
"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
searchwith different keywordsCheck
entitiesto see what's indexedUse
recall --last 10to see recent memories
Context seems stale:
Always run
sync --startat session beginningCheck current branch with
branches
After git operations:
After
git merge: runmerge-branch <source-branch>After
git checkout:sync --startwill load correct branch context