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 skills add gitmem-dev/gitmem --skill openclawgit clone --depth 1 https://github.com/gitmem-dev/gitmemWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gitmem-dev/gitmem/openclaw)<a href="https://agentmods.dev/skills/gitmem-dev/gitmem/openclaw"><img src="https://agentmods.dev/badge/skills/gitmem-dev/gitmem/openclaw/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gitmem-dev/gitmem/openclaw"><img src="https://agentmods.dev/badge/skills/gitmem-dev/gitmem/openclaw.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00067 | $0.01285 |
| Opus 5.5 | $0.00027 | $0.00514 |
| Sonnet 5.5 | $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 1mo ago.
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.
- 1mo ago 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 13d ago), licensed MIT. It adds 67 tokens to every session and 1,285 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
wiki-ingest
Use when an operation issue asks to ingest a captured raw/ source into the LLM Wiki, or the user says "ingest ". Create durable source, entity, concept, synthesis, index, and log pages; use paperclip-distill for Paperclip bundles.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
memory-context
Load relevant long-term memory from Hindsight before starting work. Use at the start of every task or request, before planning or answering, and whenever the user mentions people, projects, decisions, preferences or anything done before.
memory-retain
Save what was learned to Hindsight long-term memory. Use when a task finishes, when the user states a fact, decision or preference, and when you discover something a later run or another Bot would need.
awm-rsi-toolkit
Run AWM (Agent Workflow Memory, Wang et al. 2024) on an agent - induce reusable workflows from its successful trajectories, online over a training stream or offline from supplied experiences, then freeze the workflow memory and measure it on a published Benchmark.