by SafeRL-Lab
CheetahClaws: A Fast and Easy-to-Use Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems
# Add to your Claude Code skills
git clone https://github.com/SafeRL-Lab/cheetahclawsLast scanned: 5/10/2026
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}cheetahclaws is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by SafeRL-Lab. CheetahClaws: A Fast and Easy-to-Use Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems. It has 767 GitHub stars.
Yes. cheetahclaws passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/SafeRL-Lab/cheetahclaws" and add it to your Claude Code skills directory (see the Installation section above).
cheetahclaws is primarily written in Python. It is open-source under SafeRL-Lab on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh cheetahclaws against similar tools.
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pip install cheetahclaws
Then just run:
cheetahclaws # start chatting!
Other install methods: one-line install script | install from source | uv install | run from source install | full install details | docker install
🖥️ Prefer a native app? A desktop build (Electron) wraps the full chat UI in a window — no terminal needed. See
desktop/.
auto mode asks only when an action can change your files, run arbitrary code, or reach outside the session. Auto-approved now: every registry-marked read-only tool (18 more than before — diagnostics, task/memory queries, document readers), read-only shell pipelines (git log | head -20, ls -la | grep test — the old check rejected every |), session-state tools (tasks/memories/skills), and creating a new file inside the workspace. Still asks: overwrites, writes outside the workspace, .git/hooks and .github/workflows paths, interpreters and test/build runners, anything that deletes or uploads, and sub-agent spawns. The prompt also gained s — approve and stop asking for that one command or file for the session, a scoped alternative to accept-all (/permissions clear drops grants). The shell check is now a real parser instead of a prefix match, which along the way closed a hole where anything starting with python /node /find auto-ran. Details/model openrouter/<vendor>/<model> (e.g. openrouter/deepseek/deepseek-v4-flash) routes through OpenRouter; the key comes from OPENROUTER_API_KEY or /config openrouter_api_key=sk-or-..., and the model shows up in the /model Tab picker and the Web UI picker automatically. Append @<provider>[/<quantization>] — openrouter/deepseek/deepseek-v4-flash@gmicloud/fp8 — to pin which upstream serves the request; it is sent as OpenRouter's provider request-body object, so the model field stays a real catalog ID. Shipped alongside four routing fixes that gateway model IDs exposed: the provider is no longer re-derived from an already-stripped model string (which read openrouter/deepseek/… as the DeepSeek API and leaked DeepSeek-only request fields), cost estimates and context windows now resolve per model instead of defaulting to $0.00 and a flat 128k, and the @… routing suffix no longer strips a model of its prompt-family overlay. Details/config input_suggest=false or CHEETAH_SUGGEST=0. Also in this release: the terminal tab title now configures itself over Remote-SSH / WSL / devcontainers — it used to write a settings file on the server that the editor never reads, and never retry; it now targets the remote Machine settings the window actually reads. First tagged release carrying the July 11 tab-title / prompt-cache and July 20 tool_profile / bounded-I/O changes. Detailstool_profile selects how many tool schemas are sent each turn — full (default, nothing hidden) / standard (compact coding) / research / orchestration — to cut prompt tokens on small-context models, switchable with /config tool_profile=standard. Also fixes two bounded-I/O regressions: SummarizeLargeFile no longer "summarizes" its own chunk-failure markers (clean Error when map/reduce fails), and the DuckDuckGo parser no longer crashes on a valueless class attribute. Detailsprompt_toolkit is a core dependency (no [autosuggest] extra needed, so pip install / uv tool install both get it out of the box); /model gained a Tab-completion picker (provider/model + a two-level LiteLLM tree, PR #166); and sessions now autosave every turn (atomic write + fsync) so a crash or power-loss mid-conversation stays recoverable via /resume — the loud daily/history save still happens once on exit. Detailsdocker pull chauncygu/cheetahclaws) so you can run the Web UI without cloning; fixes a first-run PermissionError by pre-creating the .cheetahclaws/workspace dirs owned by the non-root user, makes the compose image overridable via CHEETAH_IMAGE, and adds scripts/docker-publish.sh (auto-reads the version, multi/single-arch). New docs sections: Pull from Docker Hub and Interactive setup / CLI mode. Details/workspace command manages isolated working directories under ~/.cheetahclaws/workspaces (list/switch/default/create/delete) (PR #162); startup auto-switching is opt-in via workspace_auto (off by default, so launching in a project directory is unchanged), and default is now a sticky key separate from last-used. Details/image now enriches the prompt with local OCR text so even non-vision models can act on clipboard screenshots (error dumps, code, tables); runs only when pytesseract/tesseract are installed and is fully opt-out via CHEETAHCLAWS_IMAGE_OCR=0. DetailsFor more news, see here.
CheetahClaws: A Fast and Easy-to-Use Python native Agent Harness Infrastructure, Supporting Any Model, such as Claude, GPT, Gemini, Kimi, Qwen, Zhipu, DeepSeek, MiniMax, and local open-source models via Ollama or any OpenAI-compatible endpoint.
More animated demos (code review,
/research,/brainstorm,/lab, Telegram/WeChat/Slack bridges) live indocs/media/.
Claude Code is a powerful, production-grade AI coding assistant — but its source is a compiled ~12 MB TypeScript/Node bundle (~1,300 files, ~283K lines), tightly coupled to the Anthropic API, hard to modify, and impossible to run again