Agent Memory 1

Persistent memory system for AI agents to remember facts, learn from experience, and track entities across sessions with easy recall and updates.

Security Scan

The skill's code, instructions, and requirements are coherent with a local persistent memory component — it does not request unrelated credentials, external network access, or unusual install behavior.

Purpose & Capability

Name, description, SKILL.md, CLI wrappers, tests, and src/memory.py all implement a local persistent memory (facts, lessons, entities) stored in an SQLite DB. There are no unrelated environment variables, binaries, or cloud credentials requested.

Instruction Scope

SKILL.md and examples only instruct creating/using the AgentMemory API and storing the DB at ~/.agent-memory/memory.db (or a custom path). There are no instructions to read unrelated system files, access secrets, call external endpoints, or exfiltrate data.

Install Mechanism

This is effectively an instruction/source bundle with no install spec that downloads arbitrary code. All source is included; there are no URL downloads, package installs, or extract steps in the provided files.

Credentials

The skill declares no required environment variables or credentials, and the code does not read environment secrets. It persists data to a user-local SQLite DB only.

Persistence & Privilege

always is false (not forced), model invocation is allowed (normal), and the skill only creates/uses a DB under the user's home directory. It does not alter other skills' configs or request elevated/system-wide privileges.

Assessment

This skill appears to be what it claims: a local SQLite-based memory for agents. Before installing, consider: 1) The DB is stored in ~/.agent-memory/memory.db by default and contains any facts you store — treat it as sensitive data (restrict filesystem permissions, do not put secrets there, or set a custom db_path). 2) SQLite FTS5 is used; ensure your Python/SQLite build supports FTS5 or the FTS parts may fail. 3) The code is included and readable — review src/memory.py if you have specific security/privacy requirements. 4) Run the bundled tests or run in an isolated environment first if you want to validate behavior. There are no signs of network exfiltration or unrelated credential requests.

AgentMemory Skill

Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.

Installation

clawdhub install agent-memory

Usage

from src.memory import AgentMemory

mem = AgentMemory()

# Remember facts
mem.remember("Important information", tags=["category"])

# Learn from experience
mem.learn(
    action="What was done",
    context="situation",
    outcome="positive",  # or "negative"
    insight="What was learned"
)

# Recall memories
facts = mem.recall("search query")
lessons = mem.get_lessons(context="topic")

# Track entities
mem.track_entity("Name", "person", {"role": "engineer"})

When to Use

• Starting a session: Load relevant context from memory
• After conversations: Store important facts
• After failures: Record lessons learned
• Meeting new people/projects: Track as entities

Integration with Clawdbot

Add to your AGENTS.md or HEARTBEAT.md:

## Memory Protocol

On session start:
1. Load recent lessons: `mem.get_lessons(limit=5)`
2. Check entity context for current task
3. Recall relevant facts

On session end:
1. Extract durable facts from conversation
2. Record any lessons learned
3. Update entity information

Database Location

Default: ~/.agent-memory/memory.db

Custom: AgentMemory(db_path="/path/to/memory.db")