council-gate 1.2.3

Cross-model adversarial review with an entropy gate that routes consensus vs disagreement to an explicit escalation channel.

council-gate

Cross-model adversarial review with an asymmetric entropy gate.

⚠️ Pre-stable. The 1.x line is functionally complete, but CLI flags / env var names / report format aren't frozen until 2.0. Pin a version in CI. See CHANGELOG → Stability.

council-gate runs your document, proposal, or PR diff past 3+ AI models from different providers (Claude, GPT, Gemini, Llama, …), then tells you where they agree, where they disagree, and what they're statistically likely to have missed together. Single-model reviews are biased toward their own outputs — consensus from one model isn't a real signal.

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Who is this for?

If you're a…	You hand it…	You get back…
Product manager / researcher / grant writer	a .docx proposal, .pdf strategy doc, or .md brief	a clean markdown report flagging unclear claims, missing failure modes, audience-fit issues, statistical pitfalls — three independent AI editors in one pass
Engineer	a PR diff, design spec, RFC, or source file	structured findings on edge cases, security boundaries, silent-failure paths — and where reviewers disagreed badly enough that a human should look

No AI/ML background required. You need a file and ~60 seconds.

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Install (one command)

The fastest path on each platform:

Platform	Command
Python users	pip install council-gate
macOS / Linux / WSL	curl -LsSf https://raw.githubusercontent.com/AdishAssain/council-gate/main/install.sh | sh
Windows (PowerShell)	irm https://raw.githubusercontent.com/AdishAssain/council-gate/main/install.ps1 | iex
Claude Code	/plugin marketplace add github:AdishAssain/council-gate then /plugin install council-gate
Docker (no install)	docker run --rm -v "$PWD:/work" -w /work -e OPENROUTER_API_KEY=... ghcr.io/adishassain/council-gate review proposal.docx

The non-pip installers handle everything for you: install uv, install Python, install council-gate from PyPI, fix your PATH. No prerequisite knowledge required, no Python pre-installed needed.

Then:

council-gate init                                  # paste your OpenRouter key (free at https://openrouter.ai/keys)
council-gate review path/to/proposal.docx          # report saved to ./council-gate-proposal-<timestamp>.md

That's it. The default model mix runs on ~$1–2 of OpenRouter credit per review. Open the saved markdown in any viewer (Cursor, VS Code, GitHub, even Notes.app).

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What you can review

Supported formats — auto-detected, no flags:

• Documents: .docx, .pdf, .pptx, .xlsx, .odt, .rtf, .epub (converted to markdown via MarkItDown)
• Plain text & code: .md, .txt, .diff, .patch, source code in any language — read verbatim

Review styles are auto-picked: .docx/.pdf/.odt → proposal; diffs / code / .md → eng. Override with --mode:

--mode	Best for	Looks for
eng	engineering specs, PR diffs, design docs	correctness, edge cases, failure modes, security boundaries, silent-failure paths
proposal	grant proposals, strategy docs, pitches	claim/evidence asymmetry, vague language, audience fit, missing failure modes
analysis	data analyses, research findings	sample bias, confounders, unsupported causal claims, reproducibility
general	mixed / fallback	factual errors, internal inconsistencies, unsupported claims

Custom prompt? --prompt path/to/my-prompt.md.

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What a report looks like

council-gate review proposal.docx saves a single markdown file. Excerpt:

# Council review — `proposal.docx`

**The council disagreed.** Reviewers did not converge on a single set of findings.
Read the individual reviews below before acting.

## At a glance
| | |
|---|---|
| Verdict | ESCALATE |
| Reviewers | 4 returned reviews · 1 errored |
| Disagreement | 0.62 on a 0–1 scale (threshold 0.35; higher = more divergence) |
| Mode | proposal |

## What each reviewer said

### claude-haiku-4-5
| Severity | Where | Issue |
|---|---|---|
| MAJOR | Section 2 | Causal claim ("X drives Y") not supported by cited data |
| MINOR | Abstract | "Significantly improves" is unquantified |
…

Three verdicts:

• ESCALATE — reviewers disagreed; needs human judgement.
• CONSENSUS_CHECK — reviewers agreed, but the report ships with a checklist of dimensions where frontier AI models tend to share blindspots. Don't trust agreement as approval.
• INCONCLUSIVE — too few reviewers returned usable output (network, quota, etc).

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Other commands

Command	What it does
council-gate init	Writes ~/.config/council-gate/.env with your OpenRouter key. Interactive. Repairs your PATH if needed.
council-gate review <file>	Runs the council. Auto-saves a markdown report. --no-save for stdout-only; --print for both.
council-gate doctor	Diagnoses common setup issues: config present, key set, PATH on, codex CLI available.
council-gate update	Reinstalls the latest from PyPI.

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How it works

                  artifact (spec / diff / plan)
                              │
                              ▼
        ┌──────────────────  Council  ──────────────────┐
        │  Adapter A      Adapter B      Adapter C       │  (different providers,
        │  e.g. Claude    e.g. GPT       e.g. Gemini     │   generator excluded)
        └────────┬───────────┬───────────────┬───────────┘
                 │           │               │
                 ▼           ▼               ▼
                Review      Review          Review        (structured findings + raw)
                              │
                              ▼
                       Entropy Gate
                              │
              ┌───────────────┴───────────────┐
              ▼                               ▼
     disagreement ≥ τ                disagreement < τ
              │                               │
              ▼                               ▼
        ESCALATE                      CONSENSUS_CHECK
   (formatted message               (verify against known
    for human channel)               correlated blindspots)

The two original primitives are the council (cross-model, not cross-prompt) and the entropy gate (asymmetric — only high disagreement is a clean signal; low disagreement is treated as suspect, not as approval).

Why cross-model, not cross-prompt

Same-model self-evaluation is biased. Panickssery et al. (2024) showed that LLM evaluators recognise and favour their own generations — the bias is consistent and measurable. A "council" of three Claude personas reviewing Claude-generated code is doing performance, not adversarial work.

council-gate enforces this with one rule: the generator is excluded from the council. Host integrations declare which provider produced the artifact via COUNCIL_GENERATOR_PROVIDER, and that provider's seats are dropped before the council runs.

Why the gate is asymmetric

The naive design — low entropy means trust, high entropy means escalate — is half wrong.

Kim et al. (2025) studied 350+ LLMs and found that models agree roughly 60% of the time when both err. The reported inter-model error correlation of r ≈ 0.77 implies an effective ensemble size of ~1.3 from three models — barely more diversified than asking one. The drivers: shared providers, shared architectures, and shared capability tier. Larger frontier models are more correlated even across providers, not less, because they converge on similar training distributions.

Shin et al. (2025) sharpened this: frontier LLMs systematically over-weight technical validity and under-weight novelty when reviewing scientific work. A shared blindspot, not random noise.

The gate handles this asymmetrically:

Council output	Naive read	council-gate read
High disagreement	"Bad — humans must adjudicate."	Correct. Format the escalation.
Low disagreement	"Good — auto-proceed."	Treat as suspect consensus. Surface known correlated-failure dimensions explicitly.

In practice, low-disagreement output ships with a checklist (novelty, edge cases, failure modes, missing-data handling, long-term maintenance) for the human to verify against, rather than a green check.

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Configuration

Lives at ~/.config/council-gate/.env (XDG-compliant). council-gate never reads from the working directory; nothing lands in your repo.

Three keys matter:

• COUNCIL_MODELS — comma-separated OpenRouter model ids. Default is cost-conscious (Haiku, GPT-mini, Gemini Flash, Llama, Qwen, DeepSeek) — works on a $1–2 OpenRouter balance. Swap in flagship variants for higher-stakes reviews; see commented alternatives in .env.
• COUNCIL_GENERATOR_PROVIDER — slug (anthropic, openai, google) of whichever model produced the artifact. The corresponding seats are excluded from the council.
• GATE_THRESHOLD — disagreement threshold τ ∈ [0, 1] above which the gate fires escalation. Default 0.35.

For an extra council seat using OpenAI's Codex CLI, install and authenticate codex separately (openai/codex).

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Privacy & secret-leak guardrails

council-gate sends the artifact body to LLM APIs of every council seat. Three layers of protection ship by default:

1. Filename refusal. The CLI refuses to read files matching obvious secret-bearing patterns (.env, .pem, .key, id_rsa, *credentials*, *secret*, *.gpg, *.kdbx).
2. Inline redaction. The artifact body is scanned for known secret patterns (OpenAI/Anthropic/Google/AWS/Slack/GitHub keys, JWTs, PEM private-key blocks) and redacted with [REDACTED:…] placeholders before any model sees it. Redaction count is logged.
3. Disclosure (this section). Don't pass files containing secrets, PII, or confidential third-party data. The redaction layer is defence in depth, not a substitute for judgement.

To bypass both, pass --skip-redaction-check. Don't use this flag unless you've audited the artifact yourself.

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Integrations

council-gate is a CLI; host integrations are thin wrappers that set COUNCIL_GENERATOR_PROVIDER before invoking it.

• Claude Code plugin — .claude-plugin/ (use /plugin install council-gate)
• Claude Code skill — integrations/claude-code/
• Codex CLI — integrations/codex/
• GitHub Action — integrations/github-action/

Each is a copy-paste install. None require modifying council-gate itself.

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Contributing

PRs, issues, and dogfood reports all welcome. The most useful contribution is running council-gate on your real proposals/PRs/specs and filing the friction.

See CONTRIBUTING.md for dev setup, where help is most needed, and the project's two non-negotiable design primitives.

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Related work

• Panickssery, A., Bowman, S. R., & Feng, S. (2024). LLM Evaluators Recognize and Favor Their Own Generations. NeurIPS 2024. arXiv:2404.13076
• Kim, E., Garg, A., Peng, K., & Garg, N. (2025). Correlated Errors in Large Language Models. ICML 2025. arXiv:2506.07962
• Shin, H. et al. (2025). Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews. EMNLP 2025 (Oral). arXiv:2502.17086

How disagreement is measured

Pairwise Jaccard distance over normalized token sets extracted from each reviewer's findings, averaged across reviewer pairs. The metric measures lexical overlap, not semantic agreement — sufficient for the asymmetric gate's purpose, since high disagreement still means high disagreement, and low disagreement is already treated as suspect rather than as approval.

License

MIT. See LICENSE.