Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add a-tokyo/agent-skills --skill tribunalgit clone --depth 1 https://github.com/a-tokyo/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/a-tokyo/agent-skills/tribunal)<a href="https://agentmods.dev/skills/a-tokyo/agent-skills/tribunal"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills/tribunal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/a-tokyo/agent-skills/tribunal"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills/tribunal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00207 | $0.03047 |
| Opus 5.5 | $0.00083 | $0.01219 |
| Sonnet 5.5 | $0.00041 | $0.00609 |
| Haiku 4.5 | $0.00021 | $0.00305 |
Grade A, and why
tribunal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- tribunal — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tribunal
The orchestrator builds nothing and scores nothing itself — it slices the work, dispatches a separate doer, convenes separate verifiers, and adjudicates on evidence. Three behaviors carry the value: an adversarial second look that catches what a single pass ships silently, as named failure scenarios; calibrated verdicts — locally fixable defects get ITERATE, not "rewrite everything", and passing math is overridden only by verified evidence; evidence before claims — nothing is "done" without fresh reproduction; refuted claims are excluded, never averaged in. Announce at start: "Running this through the tribunal pattern: doer -> verifier panel -> consensus."
When to use
Multi-part or high-stakes deliverables where a shipped defect costs more than a panel. Not for trivial edits, anything one verification command proves, or ordinary code review (dialogue improving a change vs independent measurements of a frozen artifact against pre-declared criteria, adjudicated to a ship decision). Never nest tribunals — no role runs the protocol on its own output.
The loop
- Freeze acceptance criteria and verification commands BEFORE implementation; derive scoring dimensions, weights, and a pass target from them, recorded so re-panels reuse the rubric. Identify the operative skills in play — those bearing on how the artifact is built or judged (e.g. production-grade); never the tribunal skill itself nor pure orchestration skills — and fold their standards into the criteria. Verify-only entry (artifact already exists): write criteria from the original request — never reverse-engineered from it — freeze, start at step 3.
- Spawn a SEPARATE doer agent (never the orchestrator itself) with the full slice spec pasted in (never "read the plan file") and the operative skills named with an instruction to load them (load production-grade, etc.) — if it cannot load a named skill it says so rather than proceeding. If the orchestrator writes or edits the deliverable, there is no independent artifact to verify and the run collapses to one context. The doer implements, runs the verification commands, materializes the artifact durably — committed, pushed, or published so it outlives the doer's session — and reports a diff summary, verbatim output, the artifact's fetchable address, and exactly one status (table below). DONE without an address is not DONE.
- Check the report against the actual diff yourself; dispatch the panel in parallel, context-walled.
- Adjudicate per consensus-mechanics.md; record verdict, dissents, caveats, and round count in the ledger.
- SHIP -> next slice. ITERATE -> findings become a fix list for a fresh doer; re-panel with fresh verifiers re-scoring every dimension, prior findings as risks.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 198 lines · 207 tokens per session scan A e76a7098f8a2
tribunal is a skill published in the GitHub repository a-tokyo/agent-skills (17 stars, last pushed 2d ago), licensed MIT. It adds 207 tokens to every session and 3,047 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
review-task
Use to verify that a task has been completed according to its intent and criteria. Typical workflow: create-task → clarify-task → plan-task → impl-task → review-code → review-task → ship-task.
review-code
Use to review a diff for correctness, design, security, and convention conformance. High-signal, read-only, severity-gated, with an autofix lane for mechanically-certain trivia. Default review step in /at:auto-task.
pr-review-triage
Watch open PRs, check CI status, review staleness, merge conflicts, and unanswered review comments. Produces a prioritized watchlist.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.
make-pr-easy-to-review
Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".
thermo-nuclear-code-quality-review
Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.