gateguard-ai 0.6.0

A fact-forcing hook gate for Claude Code. Makes the AI pause and investigate before editing.

GateGuard

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 without having looked first, the gate blocks the attempt and forces Claude to gather 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. And since v0.6.0, investigation is no longer demanded and then taken on trust: an evidence ledger observes what Claude actually did (Read / Grep / Glob / investigative Bash), and the gate opens on observed behavior — never on claims. If Claude already did the homework, no gate fires at all. If it didn't, the gate fires and the homework it then does is recorded, becoming its pass on retry.

Evidence: A/B test results

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.

Task	With GateGuard	Without GateGuard	Gap
Analytics module (codebase integration)	8.0 / 10	6.5 / 10	+1.5
Webhook validator (data parsing)	10.0 / 10	7.0 / 10	+3.0
Analytics module (re-test, v0.3.0)	8.0 / 10	6.5 / 10	+1.5
Average	8.7	6.7	+2.0

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.

How we tested

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.

Recommended models

• 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.

Install

pip install gateguard-ai

Quick start

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 on Read|Grep|Glob|Bash — the evidence ledger (v0.6.0). It records observed investigation for the recognition audit and tracks Read files for the Read-before-Edit gate. Re-running gateguard init upgrades a v0.5.x "Read"-only registration in place.

Restart Claude Code and the gate is active.

What the gates do

Gate	Trigger	What Claude must do
Read-before-Edit	Edit on a file not yet Read this session	Read the file first
Fact-force Edit	First Edit per file	Quote the user's instruction, list importers, detect conflicts between existing patterns and instruction (instruction wins), verify data schemas from real records
Fact-force Write	First Write per file	Quote the user's instruction, confirm no duplicate exists, detect conflicts (instruction wins), verify data schemas
Fact-force destructive Bash	rm -rf, git reset --hard, drop table, etc.	List what will be destroyed, give a rollback, quote the instruction
Fact-force routine Bash	First Bash per session (v0.6.0: read-only commands — ls, cat, grep, git status, safe pipes — bypass this gate entirely)	Quote the user's current instruction
Bughunt (v0.4.0+, opt-in)	3+ Edit/Write ops to non-docs files since the last test/build run	Run tests, verify the build, exercise the change on real input, check edge cases

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

Recognition audit (v0.6.0)

The fact-forcing gate demands facts, then trusts the retry — which leaves one piece of self-report inside GateGuard itself: it cannot tell whether the investigation actually happened. v0.6.0 removes it. The read-tracker hook becomes an evidence ledger (PostToolUse on Read|Grep|Glob|Bash) that records observed investigation, and the gate consults the ledger before demanding the ceremony:

• Evidence pass — if the target file was Read and its area was actually searched (a grep/glob/scan mentioning the file or its directory, within 30 min), the fact-forcing gate is skipped. The AI's observed behavior — not its answer — is the passport.
• Scope pass — passing the gate (or an evidence pass) grants the file's directory a 30-minute pass. Read files inside a recently-verified directory skip the ceremony; moving to a new directory re-gates. This ends the "same confirmation over and over in long sessions" failure mode structurally.
• Risk tiers — comment/blank-line-only edits pass with no ceremony (trivial). Signature changes (def/class/import/export) require deep evidence: the dependents must have been searched (elevated). Auth / payment / migration / .env / CI paths are never exempted by evidence and add an explicit user-confirmation demand (high).

With an empty ledger, behavior is exactly v0.5.0 — the audit only ever converts "already investigated" denies into passes, plus a stronger gate on high-impact paths. All four switches live under audit: in .gateguard.yml.

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.

Why "verify data schemas"?

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.

Configuration

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
  readonly_bash_bypass: true  # v0.6.0 — ls/cat/grep/git status skip the routine gate

audit:                  # v0.6.0 recognition audit (all default true)
  evidence_pass: true   # skip the gate when investigation was observed
  scope_pass: true      # 30-min directory pass after a gate pass
  trivial_pass: true    # comment-only edits skip the ceremony
  high_risk_guard: true # auth/payment/migration/.env/CI: never exempted

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

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

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

CLI

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)

How it works

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 + verify the investigation happened. The model can't proceed until it has demonstrated understanding — and since v0.6.0 the demonstration is checked against observed tool history, not taken on trust.

GateGuard is a pair of Claude Code hooks:

1. The observation hook (PostToolUse) records every Read, Grep, Glob, and investigative Bash into an evidence ledger. It never denies anything — observation and enforcement are separate hooks by design.
2. The gate (PreToolUse) consults the ledger before Edit/Write/Bash. Investigated target → pass, silently. Uninvestigated target → deny, with exact instructions on which facts to gather (importers, data schemas, user instruction).
3. The investigation the model then performs is itself recorded, so the retry passes on evidence — and grants the directory a 30-minute scope pass so verified areas stay quiet.

Either way the model ends up with context it didn't have before, producing measurably better code — the difference is that ceremony is only paid when recognition is actually missing.

Spread via CLAUDE.md

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.

---

License

MIT — see LICENSE.