name	tribunal
description	Hard-critical Tribunal workflow for knowledge, critique, UI/UX, and comparison reviews using three isolated judges, Nx rounds, explicit provenance, and strict anti-fake gates.

Tribunal Skill

Use this skill when a task needs a rigorous meta-analysis, product/library comparison, UI/UX teardown, or proof review. A Tribunal is not a friendly review. It is a direct, non-sycophantic panel that keeps missing evidence visible.

Tribunal Types

knowledge: facts, research, citations, primary-source checks.
critique: harsh architecture, product, implementation, and proof teardown.
ui_ux: interface, workflow, visual quality, accessibility, and repeated-use ergonomics.
comparison: tool/product/library matrix with real links, real stars, differentiated scores, and exactly one crown.

Required Process

Pick the Tribunal type, Nx multiplier, and hardness. Default to rounds=1 and hardness=standard; use hardness=hard or brutal for high-risk claims.
Build three isolated judge requests per round and invoke the configured backend separately for each unique persona. Do not expose one judge's conclusion to another before debate. A deterministic local backend proves orchestration only; it does not count as substantive LLM or source research.
Route skills before judging:
- Knowledge: web search, NotebookLM, primary-source review.
- Critique: architecture, production-code-audit, Karpathy-inspired technical critic.
- UI/UX: high-end-visual-design, minimalist-ui, ui-ux-pro-max, design-taste-frontend, web-design-guidelines.
- Comparison: competitive-landscape, CSV/report gate, source verification. Routed names are declared labels for the host workflow. The local Python core records but does not discover, validate, or invoke installed Codex skills; the host must fail visibly when a required live capability is unavailable.
Use NotebookLM or an equivalent source corpus for substantive deep research. Record the canonical notebook identity, processed sources, questions, and answers. A URL is provenance only unless a live evidence-capable judge actually queries its contents. Never invent a NotebookLM link.
Synthesize only after all judges finish. Name recurring failures, disagreements, and strongest points. Label this as post-hoc synthesis; the current library contract does not provide interactive inter-agent debate.
Produce a verdict with judge/backend/engine provenance, findings, evidence, evidence gaps, and a single recommendation or crown when warranted.

Personas

Use personas from personas/ when available:

Karpathy-inspired Technical Critic: minimal, technical, implementation-first architecture pressure based on public repositories. This synthetic persona is neither authored nor endorsed by Andrej Karpathy and must not impersonate him.
Knowledge Analyst: source and citation enforcement.
Systems Architect: maintainability, OSS boundaries, runtime proof.
Security Auditor: provenance and anti-fake review.
UI Specialist and Minimalist UI Architect: visual design quality.
UX Specialist, UX Researcher, Operator Flow UX Reviewer: workflow and usability.

Anti-Fake Gates

A claim without a source is ❌ or an explicit blocker, not a pass.
A weak but partially supported area is ⚠️.
A verified capability can be ✅.
A comparison has exactly one 👑; no ties with multiple crowns. Runtime output may award it only when every view scores at least 80 and declares no unresolved evidence gap.
CSV cells must use bare GitHub URLs, numeric stars, emoji capability cells, and differentiated scores.
UI/UX claims require visual or interaction evidence; code inspection alone is insufficient.

Direct Criticism Boundary

“Uncensored” means plain, hard, non-sycophantic criticism. It does not waive factual, legal, privacy, identity, or safety constraints. Never invent evidence, expose secrets, impersonate a real person, or turn confidence into a fake pass. State the defect directly, cite what proves it, and keep uncertainty explicit.

Library Surface

The Tribunal library is installed locally.

Run an offline orchestration Tribunal:

python tribunal.py \
  --mode comparison \
  --rounds 2 \
  --hardness hard \
  --target "Tribunal OSS library comparison"

The bundled local-rules backend reports structural readiness and evidence gaps. Its transparent structural ceiling is 50/100, so its score is never a semantic target rating. For semantic review, inject a JudgeBackend that returns BackendResult objects and records its real provider/engine provenance. Unique personas plus separate calls do not prove cross-family independence; enforce that in the live backend when required. Supply a NotebookLM URL only after the notebook exists and its source/query ledger has been verified.

Run Phase 1 core modes:

python examples/phase1_core_modes.py

Run the CSV gate:

python scripts/csv_gate.py report/codex-trib-lib-matrix.csv

name	tribunal
description	Hard-critical Tribunal workflow for knowledge, critique, UI/UX, and comparison reviews using three isolated judges, Nx rounds, explicit provenance, and strict anti-fake gates.