What does this skill do, and when should you use it?
This skill provides a clear explanation of claude-mem's inner workings. It describes how every Read, Edit, and Bash action is converted into compressed observations, which are summarized at session end and relevant ones auto-injected into future prompts. It also covers when memory injection begins (second session) and where data resides (~/.claude-mem). Ideal for users needing a quick overview of the system's behavior and privacy model.
Reads user queries about claude-mem's functionality and responds with a structured explanation based on the SKILL.md content. It does not perform memory capture or injection itself; it only serves as documentation.
- A developer new to claude-mem wants to understand how it works before adoption.
- A user wonders when memory injection triggers and how to pre-load the codebase with /learn-codebase.
- A privacy-conscious user needs confirmation that data stays local.
- A user troubleshooting why memory isn't appearing wants to verify the injection timeline.
How do you install this skill?
- Core functionality depends on external AI services (e.g., Anthropic API) which may require proxy or alternative setup for users in mainland China.
- Installation runs scripts and auto-downloads dependencies (Bun, Chroma, etc.); ensure your network and security policies allow it.
- Although docs claim local storage, compression and summarization send data to third-party AI providers; be cautious when processing sensitive code or data.
- Publisher identity is unverified, posing supply-chain risk; review the code before installation.
- The skill is descriptive and lacks tests specific to itself and known-limitation disclosure; consult full documentation before use.
- Shell / CLI
- Network access
- Local filesystem
Node.js >=20BunuvSQLite 3
Installed as part of the claude-mem plugin. Run npx claude-mem install or use /plugin marketplace add thedotmack/claude-mem then /plugin install claude-mem in Claude Code. The skill becomes available automatically.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/thedotmack/claude-mem.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/plugin/skills/how-it-works" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- How does claude-mem work?
- What is this thing doing?
Simply ask in a Claude Code session: "How does claude-mem work?" or "What is this thing doing?" The skill will provide the explanation. No additional steps needed.
What are this skill's strengths and limitations?
- Offers a concise, accurate explanation of core mechanisms.
- Includes concrete details like data path and injection timing.
- Easy to access as part of the plugin.
- Only provides information; no interactive diagnostics or troubleshooting.
- Relies on shared README facts without independent versioning.
- No mention of testing or maintenance status for this skill specifically.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| claude-mem How It Works this page | 54 · Use with care | ★ 98k | 3d ago | Apache-2.0 |
| Engram Persistent Memory Protocol | 68 · Recommended | ★ 7.1k | 4d ago | MIT |
| Engram Memory Skill | 54 · Use with care | ★ 7.1k | 4d ago | MIT |
| memU | 50 · Use with care | ★ 15k | 20d ago | NOASSERTION |
| Engram Business-Rules Guardrails | 54 · Use with care | ★ 7.1k | 4d ago | MIT |
How did FollowSkills review this skill?
The skill document clearly discloses data flow: data is stored locally in ~/.claude-mem and does not leave the machine except to configured AI providers (Claude/OpenRouter/Gemini), with clean removal on uninstall. SECURITY.md provides privacy controls (private tags), permission scope (localhost binding, port validation, no elevated privileges), and a documented security audit history. However, as a third-party marketplace skill, publisher identity is unverified; installation involves running setup scripts and auto-downloading dependencies without explicit user consent; the skill itself does not mention least privilege or rollback mechanisms. Deductions: transparent data flow but incomplete confirmation and recovery mechanisms.
The skill document is descriptive, containing no executable instructions or scripts, so its behavior cannot be verified. However, the repository has a relatively complete CI pipeline (including typecheck, build, unit tests, e2e tests) and multiple test files covering integration adapters, path resolution, and other critical paths. Yet the skill itself lacks explicit test coverage, and the server-side components it depends on are not directly verified in the provided files. Due to static review, key paths cannot be reproduced, and no skill-level error-handling explanation is provided; hence deduction.
The skill has a clear target audience (users of Claude Code and similar tools) and clear trigger scenarios ('how does claude-mem work?'). It offers Chinese mode (code--zh) and considers reachability from mainland China, but core functionality depends on external AI services (e.g., Anthropic API) that may be inaccessible from mainland networks without proxy or alternative configuration. The skill's boundaries are not clearly stated, such as unsupported features or conflict scenarios. Therefore moderate score, deducting for insufficient boundary/trigger evidence and network reachability risk.
The skill documentation is well-structured: includes descriptions, trigger conditions, data locations, and links to layered documentation. There is a clear license (Apache-2.0), version number (13.13.1), author information, and security policy. However, the skill itself lacks version-specific changelog and known limitations disclosure (docs do not mention limitations). Publisher identity is unverified; maintenance responsibility is nominally clear, but the sustainability of community maintenance is unknown. Deductions due to incomplete versioning and change management.
The skill's purpose is clear; explaining how it works helps user understanding, but as a skill it does not directly execute complex tasks; its value lies in information provision. The documentation provides clear architecture descriptions and operational instructions but lacks tests or user feedback evidence to verify effectiveness. Static review cannot confirm actual task-completion efficiency. Deductions due to lack of directly verifiable outputs or comparative benefit evidence.
The repository contains many test files and CI configuration, providing automated verification evidence. However, the skill itself has no independent test for its content, and statements in the documentation are not verified through actual execution. As a static review, reproduction or independent verification is not possible. Multiple evidence types exist (docs, tests, CI), but coverage is incomplete. Deductions due to limited coverage and no executed verification.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →