AI coding agents now run real shell commands. Most "guardrails" log the damage after the fact. gate.cat vetoes the execution itself — before the shell sees it.
What it does
It blocks irreversible shell commands (rm -rf, DROP TABLE, terraform destroy, secret exfiltration via scp/curl) before they execute. The check is deterministic string + path analysis plus an independent exec analyzer — no model call in the veto path, so a prompt injection can't talk the gate into allowing something. Fail-closed: anything it can't parse becomes a block, never a silent allow.
Three ways to use it
Claude Code hook (the strongest — enforcement runs in the harness, outside the model's control flow):pip install gate.cat, then addgatecat-hookto.claude/settings.jsonwith matcherBash|Write|Edit.
Gated shell for any CLI agent (Codex, aider — anything honoring$SHELL): usegatecat-shell.
Local proxy for anything speaking the OpenAI API (Ollama, vLLM, LM Studio, OpenRouter): your agent changes onebase_url.
Measured, not claimed
Every number below has a row in FACTS.md in the repo, pinned to a reproducible artifact:
1,085,159 unique real agent commands (5 public datasets) replayed through the full 6-stage gate: 0 real misses after adjudication.- the reproducible bypass suite catches 178/178 danger shapes it claims — and prints its own known gap (runtime assembly) plus 1 benign false-block in 129 cases. We publish the misses instead of hiding them.
71 default policy walls (73 presets incl. opt-in), ~0.6% intervention rate on real commands.
Honest limit
The gate is certain only about what it blocks. An unmatched action is unchecked, not safe. It's a wall in front of known-dangerous shapes, not a proof of safety — use it with your sandbox, not instead of one.
Free forever, Apache-2.0, zero-dependency core.
- Install:
pip install gate.cat
- Repo:
What I'd genuinely like feedback on: does publishing our own bypass map and a documented false-block build more trust than a clean number would? That was the hardest design call.
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