القائمة
Persistent memory system for AI agents — remember facts, learn from experience, and track entities across sessions.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
AI-powered PDF generator for legal docs, pitch decks, and reports. SAFEs, NDAs, term sheets, whitepapers. npx ai-pdf-builder. Works with Claude, Cursor, GPT, Copilot.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Search and summarize papers from ArXiv. Use when the user asks for the latest research, specific topics on ArXiv, or a daily summary of AI papers.
Automatically update Clawdbot and all installed skills once daily. Runs via cron, checks for updates, applies them, and messages the user with a summary of what changed.
| name | agent-memory |
| description | Persistent memory system for AI agents — remember facts, learn from experience, and track entities across sessions. |
Persistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
clawdhub install agent-memory
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"})
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
Default: ~/.agent-memory/memory.db
Custom: AgentMemory(db_path="/path/to/memory.db")