Claude-Mem, now presented as Grok Mem, records an agent's work, compresses it with AI, and brings relevant notes into later sessions so the agent can remember decisions and next steps. It is intended for persistent context across agent conversations and supports multiple coding-agent environments. The catalogue add-ons provide the workflows and integrations used to operate this memory system.
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 thedotmack/claude-mem --skill openclawgit clone --depth 1 https://github.com/thedotmack/claude-memWrote 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/thedotmack/claude-mem/openclaw)<a href="https://agentmods.dev/skills/thedotmack/claude-mem/openclaw"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/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/thedotmack/claude-mem/openclaw"><img src="https://agentmods.dev/badge/skills/thedotmack/claude-mem/openclaw.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 8 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Supply Chain · line 10 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
- high Supply Chain · line 20 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
- high Supply Chain · line 26 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
- high Supply Chain · line 32 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
- high Tool Misuse · line 10 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- high Tool Misuse · line 58 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- high Supply Chain · line 58 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
- high Prompt Injection · line 226 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 | $0.00000 | $0.03874 |
| Opus 5 | $0.00000 | $0.01937 |
| Sonnet 5 | $0.00000 | $0.00775 |
| Haiku 4.5 | $0.00000 | $0.00387 |
Grade B, and why
openclaw scanned grade B with 2 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 9d 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.
Downloads and executes remote codemediumSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://install.cmem.ai/openclaw.sh | bash Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://install.cmem.ai/openclaw.sh | bash How it starts
The opening of the file, as written. The whole thing — 463 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude-Mem OpenClaw Plugin — Setup Guide
This guide walks through setting up the claude-mem plugin on an OpenClaw gateway. By the end, your agents will have persistent memory across sessions via system prompt context injection, and optionally a real-time observation feed streaming to a messaging channel.
Quick Install (Recommended)
Run this one-liner to install everything automatically:
curl -fsSL https://install.cmem.ai/openclaw.sh | bash
The installer handles dependency checks (Bun, uv), plugin installation, memory slot configuration, AI provider setup, worker startup, and optional observation feed configuration — all interactively.
Install with options
Pre-select your AI provider and API key to skip interactive prompts:
curl -fsSL https://install.cmem.ai/openclaw.sh | bash -s -- --provider=gemini --api-key=YOUR_KEY
For fully unattended installation (defaults to Claude Max Plan, skips observation feed):
curl -fsSL https://install.cmem.ai/openclaw.sh | bash -s -- --non-interactive
To upgrade an existing installation (preserves settings, updates plugin):
curl -fsSL https://install.cmem.ai/openclaw.sh | bash -s -- --upgrade
After installation, skip to Step 4: Restart the Gateway and Verify to confirm everything is working.
Manual Setup
The steps below are for manual installation if you prefer not to use the automated installer, or need to troubleshoot individual steps.
Step 1: Clone the Claude-Mem Repo
First, clone the claude-mem repository to a location accessible by your OpenClaw gateway. This gives you the worker service source and the plugin code.
cd /opt # or wherever you want to keep it
git clone https://github.com/thedotmack/claude-mem.git
cd claude-mem
npm install
npm run build
You'll need bun installed for the worker service. If you don't have it:
curl -fsSL https://bun.sh/install | bash
Step 2: Get the Worker Running
What ships with it
16 files 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.
- .gitignore 20 B
- .npmignore 14 B
- Dockerfile.e2e 1.3 KB
- e2e-verify.sh 5.3 KB runs code
- install.sh 51 KB runs code
- openclaw.plugin.json 3.9 KB
- package.json 400 B
- skills/do/SKILL.md 34 B
- skills/make-plan/SKILL.md 41 B
- src/index.test.ts 43 KB runs code
- src/index.ts 36 KB runs code
- test-e2e.sh 1.2 KB runs code
- test-install.sh 58 KB runs code
- test-sse-consumer.js 2.8 KB runs code
- TESTING.md 6.5 KB
- tsconfig.json 570 B
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.
- 9d ago First seen · 463 lines · 0 tokens per session scan B acf93a8d913a
openclaw is a skill published in the GitHub repository thedotmack/claude-mem (93,544 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 3,874 tokens. A static security scan graded it B with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
hivemind-memory
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
hivemind
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
dejavu
Review and manage learned antipattern rules. Use when Claude should review its past mistakes, apply learned rules to CLAUDE.md, or check rule effectiveness. Triggers on "/dejavu", "review rules", "what did you learn".
dejavu-rules
A conversation-based manager for rules stored in CLAUDE.md, a project file that tells the coding assistant how to work. It can add, edit, remove, and review those rules.
dejavu-report
Generate a weekly report of detected patterns and rule effectiveness. Use when user asks "dejavu report", "what did you learn this week", "dejavu weekly".