A fact-forcing hook gate for Claude Code.

Also shipped as a skill in everything-claude-code. The JS port lives there; this repo is the Python upstream.

Not to be confused with gateguard-personal — an internal hook used at ZUNO WORKS with its own version series (v4.x). This repository is the public Python package gateguard-ai (v0.x.y series).

GateGuard makes Claude Code pause and investigate before it edits your files. When Claude tries to modify, create, or run something, the gate blocks the first attempt and forces Claude to present concrete facts — who imports this file, what the data actually looks like, what the user's instruction was — before it is allowed to proceed.

Self-evaluation ("are you sure?") doesn't change LLM behavior. Forced investigation does. GateGuard is the smallest thing that reliably moves that needle.

Three tasks, scored on a 10-point rubric (code structure, edge cases, pattern compliance, test quality, design decisions). GateGuard hooks were physically active — not prompt injection. The ungated agent ran without hooks.

Where the gap comes from:

• Conflict detection: The gated agent spotted mismatches between existing code patterns and the user's instruction, then followed the instruction. The ungated agent silently deviated (e.g. using threshold 0.6 when the codebase uses 0.7).
• Data verification: The gated agent checked real data records and used the correct schema keys. The ungated agent assumed a schema and missed source_law_ids / source_ghost_ids fields entirely.
• Pattern compliance: The gated agent matched existing dataclass patterns. The ungated agent returned plain dicts.

These are the errors tests don't catch: the code runs, but the design is shallow. Over a multi-file project, this 2-point gap compounds into significant rework.

1. Gated condition: The tester ran Claude Code with GateGuard hooks physically registered in ~/.claude/settings.json. Every Edit, Write, and Bash triggered a real PreToolUse deny — the LLM was forced to investigate before retrying. This is not prompt injection — the hook blocks the tool call at the Claude Code runtime level.

2. Ungated condition: A separate Claude Code Agent (subagent) executed the same task with no hooks registered. Agents do not inherit the parent session's hooks, so this is a genuine no-gate baseline.

3. Same task, same codebase: Both conditions received identical prompts and worked on the same source tree (reset via git checkout between runs).

4. Scoring: 5 criteria × 2 points each = 10-point rubric. Code structure, edge case handling, pattern compliance, test quality, design decisions. Scored after comparing diffs side by side.

5. Limitations: N=3 tasks, self-scored (potential bias). The gated tester had seen prior results in the same session. A clean replication would use a fresh session with no prior exposure to the task.

• Claude Opus 4.7 — primary target, dogfooded for v0.4.0
• Claude Sonnet 4.6 — expected to work, not benchmarked
• Haiku 4.5 / older — may retry instead of investigate; YMMV

GateGuard's hooks are model-agnostic at the protocol layer, but the quality gain depends on the model treating a PreToolUse deny as a cue to gather facts, not to retry the same call.

pip install gateguard-ai

From the project directory you want to protect:

gateguard init

This does three things:

1. Writes .gateguard.yml into the current directory.
2. Registers a PreToolUse hook in ~/.claude/settings.json that runs gateguard-hook on every Edit, Write, and Bash call.
3. Registers a PostToolUse hook that tracks which files have been Read (needed for the Read-before-Edit gate).

Restart Claude Code and the gate is active.

Each gate fires once per target per session. After the facts are presented, the next attempt passes through.

The bughunt gate has a 300-second cooldown after firing, so one missed reminder does not pin the session. Bypass per-session with GATEGUARD_BUGHUNT_DISABLED=1.

Since v0.4.1, the bughunt gate skips edits to .md / .txt / .rst / .log / .gitignore and conventional filenames (CHANGELOG, TODO, LICENSE, ...). Repeated edits to the same file within 10 minutes count as a single edit, so step-by-step refactors of one function don't trip the gate. These defaults keep the signal-to-noise ratio high without needing per-project config.

In our A/B test, both agents (gated and ungated) wrote code that assumed ISO-8601 dates and bare JSON arrays. The real data used %Y/%m/%d %H:%M dates and {"schema_version": "1.0", "items": [...]} wrappers. Both agents got this wrong — because neither actually looked at the data.

The gate forces the LLM to verify assumptions against reality before writing code. v0.3.0 adds conflict detection: when existing code patterns contradict the user's instruction, the gate forces the LLM to state the conflict explicitly — then follow the instruction, not the buggy pattern.

gateguard init writes a .gateguard.yml you can edit:

enabled: true

gates:
  read_before_edit: true
  fact_force_edit: true
  fact_force_write: true
  fact_force_bash_destructive: true
  fact_force_bash_routine: true
  bughunt_gate: false  # v0.4.0 opt-in — deny the 4th Edit/Write if tests haven't run

destructive_bash_extra:
  - "supabase db reset"
  - "prisma migrate reset"

messages:
  edit: |
    Before editing {file_path}, present:
    1. ...

ignore_paths:
  - ".venv/**"
  - "node_modules/**"
  - ".git/**"

gateguard init [path] [--force] [--skip-hook]
gateguard logs [--tail N]
gateguard reset
gateguard --version

• init — write .gateguard.yml and register both hooks
• logs — print recent gate events from ~/.gateguard/gate_log.jsonl
• reset — clear the current session's state file (~/.gateguard/.session_state_{id}.json)

The core insight: asking an LLM to evaluate itself ("did you violate any policies?") doesn't change its behavior. It always says no. But asking it to gather facts — "list every file that imports this module" — forces it to use Grep and Read. The act of investigation creates awareness that the self-evaluation never did.

Every competitor in the AI guardrails space stops at deny. GateGuard does deny + force investigation + demand evidence. The model can't proceed until it has demonstrated understanding.

GateGuard is a Claude Code PreToolUse hook that:

1. Denies the first attempt at Edit/Write/Bash
2. Tells the model exactly which facts to gather (importers, public API, data schemas, user instruction)
3. Allows the retry after facts are presented

The second attempt succeeds — but now the model has context it didn't have before, producing measurably better code.

Add this line to your project's CLAUDE.md to make GateGuard available to every Claude Code user who works on your repo:

## Code quality gate
This project uses GateGuard. Run `pip install gateguard-ai && gateguard init` before starting work.

Anyone who opens Claude Code in your repo will see this instruction automatically.

MIT — see LICENSE.