Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gitmem-dev/gitmem/openclawnpx skills add gitmem-dev/gitmem --skill openclawgit clone --depth 1 https://github.com/gitmem-dev/gitmemWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00067 | $0.01285 |
| Opus 5 | $0.00034 | $0.00642 |
| Sonnet 5 | $0.00013 | $0.00257 |
| Haiku 4.5 | $0.00007 | $0.00128 |
Grade A, and why
gitmem scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitMem — Institutional Memory for AI Agents
Your agent starts from zero every session. GitMem fixes that.
It's an MCP server that gives your agent persistent memory across sessions — not chat history, but earned knowledge: mistakes to avoid, approaches that worked, architectural decisions, and unfinished work.
Setup
1. Add the MCP server
openclaw mcp add gitmem -- npx -y gitmem-mcp
Or add manually to ~/.openclaw/mcp.json:
{
"mcpServers": {
"gitmem": {
"command": "npx",
"args": ["-y", "gitmem-mcp@latest"]
}
}
}
2. Initialize in your project
cd your-project
npx gitmem-mcp init
The wizard creates:
.gitmem/directory with 3 starter scars- Memory protocol instructions for your agent
- Lifecycle hooks for automatic session management
Already have config? The wizard merges without destroying anything. Re-running is safe.
How It Works
recall → work → learn → close → recall → ...
- Recall — Before acting, the agent checks memory for relevant lessons
- Work — The agent applies past lessons automatically
- Learn — Mistakes become scars, successes become wins
- Close — Session reflection persists context for next time
What Gets Remembered
| Type | Purpose | Example |
|---|---|---|
| Scars | Mistakes to avoid | "Always validate UUID format before DB lookup" |
| Wins | Approaches that worked | "Parallel agent spawning cut review time by 60%" |
| Patterns | Reusable strategies | "5-tier test pyramid for MCP servers" |
| Decisions | Architectural choices + rationale | "Chose JWT over session cookies for stateless auth" |
| Threads | Unfinished work across sessions | "Rate limiting still needs implementation" |
Every scar includes counter-arguments — reasons why someone might reasonably ignore it. This prevents memory from becoming rigid rules.
Tools Reference
Once the MCP server is running, your agent gets these tools:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 146 lines · 67 tokens per session scan A b73539f0cfe4
gitmem is a skill published in the GitHub repository gitmem-dev/gitmem (9 stars, last pushed 23d ago), licensed MIT. It adds 67 tokens to every session and 1,285 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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