aiworkspace: Skill for Claude Code

.agents/skills/tribunal/SKILL.md

tribunal is a skill for Claude Code, Codex from a-tokyo/aiworkspace. It costs 180 tokens per session (2,658 once invoked), scanned A, original, Apache-2.0.

A review process in which one agent builds a deliverable and several separate agents check it against agreed criteria, including one instructed to look for reasons it should not ship.

In plain words
What is it for?
Use it for complex or high-stakes deliverables that need fresh tests, independent checks, and a clear decision about whether they are ready.
Why use it?
It catches defects and unsupported claims that a single pass may miss, while distinguishing small fixes from work that needs a full rewrite.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents; installed under .agents/ (shared by several agents).

This is a-tokyo/aiworkspace's own configuration. It tells Claude Code and Codex how to work on aiworkspace itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything aiworkspace configures →

Reuse

Borrowing it

Nothing to install: this file belongs to a-tokyo/aiworkspace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/a-tokyo/aiworkspace/main/.agents/skills/tribunal/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/a-tokyo/aiworkspace

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for tribunal

README.md
[![agentmods](https://agentmods.dev/badge/skills/a-tokyo/aiworkspace/tribunal/github.svg)](https://agentmods.dev/skills/a-tokyo/aiworkspace/tribunal)
Your own site
<a href="https://agentmods.dev/skills/a-tokyo/aiworkspace/tribunal"><img src="https://agentmods.dev/badge/skills/a-tokyo/aiworkspace/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.

agentmods 80×15 button for tribunal

Your own site · 80×15
<a href="https://agentmods.dev/skills/a-tokyo/aiworkspace/tribunal"><img src="https://agentmods.dev/badge/skills/a-tokyo/aiworkspace/tribunal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 180 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,658 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00180 $0.02658
Opus 5.5 $0.00072 $0.01063
Sonnet 5.5 $0.00036 $0.00532
Haiku 4.5 $0.00018 $0.00266

Measured 1mo ago against content hash c2fbd57c823e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.agents/skills/tribunal/SKILL.md · 178 lines

How it starts

The opening of the file, as written. The whole thing — 178 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

  1. 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.
  2. 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, and reports a diff summary, verbatim output, and exactly one status (table below).
  3. Check the report against the actual diff yourself; dispatch the panel in parallel, context-walled.
  4. Adjudicate per consensus-mechanics.md; record verdict, dissents, caveats, and round count in the ledger.
  5. 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.

Read the full file on GitHub · 178 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 178 lines · 180 tokens per session scan A c2fbd57c823e

Subscribe to this mod's changes

tribunal is a skill published in the GitHub repository a-tokyo/aiworkspace (21 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 180 tokens to every session and 2,658 once invoked, about $0.0007 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.

Related

Other skills, from other repositories