# Adversarial Reviewer Panel > Use when you want to call AI reviewers, internal reviewers, famous-persona cross-checks, or want a theory/architecture/strategy examined from several constraint classes before implementation. - Skill: `starshard-ai/adversarial-reviewer-panel` (Agent Skill) - Install (CLI): `npx skillmds@latest add starshard-ai/adversarial-reviewer-panel` - Raw SKILL.md: https://api.skillmd.com/api/skills/starshard-ai/adversarial-reviewer-panel/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: starshard-ai (https://skillmd.com/u/starshard-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/starshard-ai/adversarial-reviewer-panel --- # Adversarial Reviewer Panel ## Purpose Turn "ask reviewers" into a constraint-driven review loop, not a chorus of interesting opinions. Use reviewers to find what a theory, design, or decision **forbids, predicts, breaks, or operationally changes**. ## Default Execution Model The valuable unit is the **reviewer persona prompt bound to a constraint**, not a specific model provider. Use any strong models (GPT / Claude / Gemini / local models) as interchangeable executors. If a given reviewer bridge is unstable, do not block the mainline; create a local copy-pasteable reviewer packet and run a deterministic synthesis, or queue the packet for any available strong executor. Default order: 1. Design 3-5 persona reviewers from constraint classes. 2. Write each persona as an operational prompt: what it forbids, what it tests, what concrete action it would change. 3. Launch model reviewers only when the bridge is healthy or the risk class justifies waiting. 4. If bridge launch fails or is slow, record `Review sanity-check: unavailable/pending`, keep reversible implementation moving, and preserve the persona packet as the review artifact. ## Default Panel Pick 3-5 reviewers by constraint class, not by vibe: - **Physics / measurement / falsifiability**: overclaim check; operational tests. - **Practice / ethics / relationships**: concrete behavior, role boundaries, ritual, trust. - **Public reality / social legitimacy**: how others can reject, misunderstand, exit, or audit. - **Control systems / safety**: feedback loops, brakes, error amplification, rollback. - **HCI / product**: what the user actually perceives, what reduces friction. - **Embodied / contemplative / apophatic**: for consciousness, interface, spirituality-adjacent, or body-insight questions, check whether the agent is mistaking language, logic, or model-compressed concepts for direct realization or lived reality. - **Physics / metaphysics bridge**: when a topic compares physics with metaphysics, do not use "metaphysics" as a dismissal. Treat them as potentially different epistemic interfaces: third-person measurement/reproducibility vs. first-person embodied realization. The task is to map correspondences and boundaries without borrowing authority across domains. Named personas are encouraged as mnemonic handles, but each must carry a constraint. Examples: Planck = measurement/falsifiability, Confucius = lived conduct/ritual, Arendt = plurality/public reality, Dijkstra = complexity and invariant discipline, Shannon = channel/noise/coding limits, Grace Hopper = operational debuggability and user tooling. ## Reviewer Prompt Contract Every reviewer receives a compact packet with: - decision question or theory claim, - source facts/artifact paths, - the constraint class they own, - explicit instruction to avoid generic praise. Require this output: 1. Strongest insight. 2. Main overclaim / self-delusion / failure mode. 3. One concrete design or action implication. 4. One disconfirming observation or anti-signal. 5. How the reviewer process itself should be improved. ## Integration Contract The synthesizing agent must synthesize, not paste reviewer chatter. Surface only: - the shared conclusion, - material disagreements, - action/design implication, - falsifiable test or anti-signal, - next owner gate if any. If reviewers agree too easily, add a hostile constraint reviewer or ask: ```text What would make this beautiful theory false, harmful, or useless tomorrow? ``` For embodied/contemplative topics, also ask: ```text What is being lost because this review is made of language? Where might the model be confusing a sayable map for the lived territory? ``` For physics/metaphysics bridge topics, also ask: ```text Which claim belongs to third-person measurement, which belongs to first-person transformation, and what would make them two projections of one underlying structure rather than a category error? ``` ## Meta-Optimization Loop After each important review, update the panel recipe: - Which reviewer caught a real risk? - Which reviewer produced decorative language? - Which missing constraint would have changed the action? - Did the review produce an implementation constraint, not just ontology? - Did it reduce effort or merely add discourse? Promote durable reviewer lessons to your persistent memory when they affect future routing. ## Hard Boundaries - Do not outsource the critical path to reviewers when deterministic low-risk implementation can continue. - Do not let reviewers replace owner gates for money, identity, public commitment, privacy, account auth, or irreversible actions. - Do not present reviewer personas as authorities; present their constraints. provenance: maker: Starshard homepage: https://github.com/starshard-ai source: https://github.com/starshard-ai/reviewer-wheels license: MIT version: 0.1.0 contact: https://github.com/starshard-ai/reviewer-wheels/issues --- *About the maker:* Starshard builds open agent skills; you can find the source and report issues at https://github.com/starshard-ai. License: MIT.