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Get Started Free →Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -18% | 0% |
A PreToolUse hook that forces Claude to investigate before editing. Instead of self-evaluation ("are you sure?"), it demands concrete facts. The act of investigation creates awareness that self-evaluation never did.
LLM self-evaluation doesn't work. Ask "did you violate any policies?" and the answer is always "no." This is verified experimentally.
But asking "list every file that imports this module" forces the LLM to run Grep and Read. The investigation itself creates context that changes the output.
Three-stage gate:
1. DENY — block the first Edit/Write/Bash attempt
2. FORCE — tell the model exactly which facts to gather
3. ALLOW — permit retry after facts are presentedNo competitor does all three. Most stop at deny.
Two independent A/B tests, identical agents, same task:
| Task | Gated | Ungated | Gap | | --- | --- | --- | --- | | Analytics module | 8.0/10 | 6.5/10 | +1.5 | | Webhook validator | 10.0/10 | 7.0/10 | +3.0 | | Average | 9.0 | 6.75 | +2.25 |
Both agents produce code that runs and passes tests. The difference is design depth.
MultiEdit is handled identically — each file in the batch is gated individually.
Before editing {file_path}, present these facts:
1. List ALL files that import/require this file (search the tree — Glob/Grep, or find/grep via Bash)
2. List the public functions/classes affected by this change
3. If this file reads/writes data files, show field names, structure,
and date format (use redacted or synthetic values, not raw production data)
4. Quote the user's current instruction verbatimBefore creating {file_path}, present these facts:
1. Name the file(s) and line(s) that will call this new file
2. Confirm no existing file serves the same purpose (search the tree — Glob/Grep, or find/grep via Bash)
3. If this file reads/writes data files, show field names, structure,
and date format (use redacted or synthetic values, not raw production data)
4. Quote the user's current instruction verbatimTriggers on: rm -rf, git reset --hard, git push --force, drop table, etc.
1. List all files/data this command will modify or delete
2. Write a one-line rollback procedure
3. Quote the user's current instruction verbatim1. The current user request in one sentence
2. What this specific command verifies or producesThe hook at scripts/hooks/gateguard-fact-force.js is included in this plugin. Enable it via hooks.json.
If GateGuard blocks setup or repair work, start the session with ECC_GATEGUARD=off. For hook-level control, keep using ECC_DISABLED_HOOKS with the GateGuard hook ID.
In long sessions, only the first GATEGUARD_FACT_FORCE_FULL_DENIALS fact-force denials (default 3) emit the full four-fact block; later denials are condensed to a single line carrying the denial ordinal, so near-identical blocks cannot accumulate in the context window and amplify model repetition loops (#2142). Retrying the same file or command after presenting facts never re-triggers the gate.
bashpip install gateguard-ai gateguard init
This adds .gateguard.yml for per-project configuration (custom messages, ignore paths, gate toggles).
%Y/%m/%d %H:%M. Checking data structure (with redacted values) prevents this entire class of bugs..gateguard.yml to ignore paths like .venv/, node_modules/, .git/.safety-guard — Runtime safety checks (complementary, not overlapping)code-reviewer — Post-edit review (GateGuard is pre-edit investigation)| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 15,552 | 10,785 | -31% | 1 | 1 | 0% | 2,596 | 3,174 | +22% | 0 | 0 | — |
case-02 | fail→pass | 14,248 | 11,086 | -22% | 1 | 1 | 0% | 2,583 | 3,255 | +26% | 0 | 0 | — |
case-03 | fail→pass | 11,242 | 5,801 | -48% | 1 | 1 | 0% | 2,028 | 2,307 | +14% | 0 | 0 | — |
case-04 | fail→pass | 12,887 | 2,889 | -78% | 1 | 1 | 0% | 2,336 | 1,728 | -26% | 0 | 0 | — |
case-05 | fail→pass | 13,929 | 3,224 | -77% | 1 | 1 | 0% | 2,208 | 1,808 | -18% | 0 | 0 | — |
case-06 | pass→pass | 13,192 | 9,956 | -25% | 1 | 1 | 0% | 2,451 | 3,191 | +30% | 0 | 0 | — |
case-07 | pass→pass | 13,945 | 5,442 | -61% | 1 | 1 | 0% | 2,495 | 2,212 | -11% | 0 | 0 | — |
case-08 | fail→pass | 13,040 | 4,062 | -69% | 1 | 1 | 0% | 2,050 | 1,934 | -6% | 0 | 0 | — |
case-09 | pass→pass | 7,530 | 2,205 | -71% | 1 | 1 | 0% | 1,036 | 1,617 | +56% | 0 | 0 | — |
case-10 | fail→pass | 10,552 | 2,728 | -74% | 1 | 1 | 0% | 1,866 | 1,772 | -5% | 0 | 0 | — |
case-11 | fail→pass | 9,283 | 3,402 | -63% | 1 | 1 | 0% | 1,526 | 1,654 | +8% | 0 | 0 | — |
case-12 | fail→pass | 13,579 | 2,623 | -81% | 1 | 1 | 0% | 2,110 | 1,686 | -20% | 0 | 0 | — |
case-13 | fail→pass | 8,454 | 1,504 | -82% | 1 | 1 | 0% | 1,682 | 1,504 | -11% | 0 | 0 | — |
case-14 | pass→pass | 15,071 | 11,493 | -24% | 1 | 1 | 0% | 2,467 | 3,278 | +33% | 0 | 0 | — |
case-15 | pass→pass | 9,444 | 2,519 | -73% | 1 | 1 | 0% | 1,630 | 1,687 | +3% | 0 | 0 | — |
case-16 | pass→pass | 7,024 | 2,761 | -61% | 1 | 1 | 0% | 1,104 | 1,633 | +48% | 0 | 0 | — |
case-17 | fail→pass | 11,232 | 2,308 | -79% | 1 | 1 | 0% | 1,776 | 1,645 | -7% | 0 | 0 | — |
case-18 | fail→pass | 14,633 | 1,819 | -88% | 1 | 1 | 0% | 2,431 | 1,543 | -37% | 0 | 0 | — |
case-19 | fail→pass | 6,812 | 1,372 | -80% | 1 | 1 | 0% | 1,186 | 1,440 | +21% | 0 | 0 | — |
case-20 | pass→pass | 11,078 | 4,397 | -60% | 1 | 1 | 0% | 2,132 | 2,050 | -4% | 0 | 0 | — |
case-21 | pass→pass | 11,837 | 2,182 | -82% | 1 | 1 | 0% | 1,829 | 1,623 | -11% | 0 | 0 | — |
case-22 | fail→pass | 13,006 | 5,220 | -60% | 1 | 1 | 0% | 2,093 | 2,098 | +0% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +64 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/27/2026 | +43% |
Other measured skills in the registry, with their headline benchmark lift.