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Review panel

Skill grimaldost/craft-collection/plugins/session-workflow/skills/review-panel

A Claude Code plugin marketplace that codifies engineering craft: disciplined Python and data-engineering practice alongside deliberate session-knowledge workflows, packaged as reusable skills.

Install
npx -y skills add grimaldost/craft-collection --skill review-panel

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One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Use when the user has iterated on a design, plan, spec, architecture, code, or prose over several rounds and can no longer judge it cleanly, before a high-stakes or hard-to-reverse decision, or when they ask for "fresh eyes", "a second opinion", "red team this", "poke holes in this", "sanity-check this independently", "am I anchored" / "too close to this", "critique this from different angles", or run "/review-panel". Prefer this over reviewing the artifact yourself — its value is convening fresh reviewer subagents that are blind to this conversation and to each other, which a review you run while anchored on the same context cannot replicate. Requires spawning fresh-context reviewers (subagents where the harness has them; sequential clean contexts otherwise); always show the plan and rough cost and ask before firing. Not for a quick factual check, a single obvious answer, or a first-pass review of something just created.

SKILL.md

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Review Panel

Fresh, independent eyes on something you have been too close to. After many rounds on an artifact, both the user and the model anchor on the current direction — each new pass defends it rather than questioning it. A panel of agents that have never seen the iteration, pointed at the artifact from adversarial angles, surfaces what the anchored view structurally cannot. The deliverable is a comparison and synthesis, not a single opinion.

When to convene — and at what size

Convene when the cost of being wrong exceeds the cost of the panel:

  • you have iterated many rounds and genuinely can't tell if it's good anymore;
  • a high-stakes or hard-to-reverse decision (architecture, a launch, a structural choice) — including a design or spec before any build, where pre-code defects (a wrong interface, a missed failure mode, a motivated assumption) are cheapest to fix;
  • an irreversible outward step is next — publishing a repo, cutting a release, going public: the artifact is "done" and self-verified, which is exactly when fresh eyes still find what the author's own verification missed (measured twice on launch-ready trees);
  • an explicit ask: "fresh eyes", "second opinion", "red team", "poke holes", "am I anchored", /review-panel.

A design/spec is panel-ready only when it is concrete enough to critique — explicit interfaces, failure modes, data flow, and at least one worked example. Panelling a bare sketch or intent yields bikeshedding and false confidence, not defects; iterate it to concreteness first (or fire a single Level-1 contrarian instead). Design-stage review is a qualifier on the high-stakes trigger above, not a standing "always review the design" gate.

Scale effort to stakes — the ladder (don't fire a full panel at a Level-1 question):

LevelConveneFor
1 — Solo contrarian1 fresh agent told to refutea quick gut-check
2 — Small panel2–3 lenses (skeptic + domain expert [+ user])a normal decision
3 — Full panel4–5 lensesan expensive / irreversible call

The protocol — this is what makes it work

  1. Curate a NEUTRAL artifact brief. Like a context hand-off, but stripped of your conclusions. Give reviewers the artifact and what to judge — never "here's what I decided, do you agree?". Do not narrate the rounds you've been through or the direction you favor; that re-anchors them on you. State facts, not your verdict.
  2. Pick the lenses. Default quartet: Skeptic/Minimalist, Best-Practices Auditor, End-User Advocate, Domain Expert. For depth, load the persona pack for the artifact type (below) and tailor a couple of personas to specifics.
  3. Make them blind and adversarial. Each reviewer is blind to the others and to this conversation. Frame the job as refute / find what's missing, not "review" — independence over politeness.
  4. Demand structured, comparable output — a fixed per-reviewer schema (verdict + scores + reasons) so results sit side by side. See references/prompt-template.md.
  5. Fire them — mechanism by ladder level. Levels 1–2: one fresh reviewer per lens, concurrently (Claude Code: one message, multiple Agent calls; Opus for high stakes). Level 3, or any panel that needs per-lens reasoning-effort control: drive the lenses through the Workflow tool — agent(prompt, {effort, schema}) per lens — which buys what the Agent tool does not expose: reasoning-effort control and schema-forced, mechanically comparable output. If the Workflow tool is not available in the session, fall back to the Levels 1–2 mechanism (concurrent Agent calls), accepting the loss of per-lens effort control. Show the plan first — lenses, agent count, rough cost — and get a go-ahead; never fire silently. A durable pre-authorization counts as the go-ahead: show the plan, cite the grant, and fire — an autonomous session that insists on a fresh ask deadlocks the panel.
  6. Persist raw output before synthesis. Write each reviewer's structured output to disk as it lands, at a destination named in the plan (the reviewed tool's own feedback intake is often right). A max-effort panel returns more than in-band messages carry — a truncated notification, or a dead orchestrator, loses the corpus; the output file is often the only copy. When a verify stage follows the lenses, collect the findings in a barrier before it — a pipeline that drops a finding on a verifier's error loses a real finding to a coarse failure, not to a refutation.
  7. Synthesize — don't average. Produce a comparison matrix, where they agree (consensus = high confidence), where they disagree (the tension worth examining), and — most important for an anchored author — where the panel diverges from the current direction, and what you may be missing.

Persona packs — load the one that fits

ArtifactPackLenses
design / spec / architecturereferences/personas-design.mdarchitect · skeptic · user · ops
code / a diffreferences/personas-code.mdsecurity · performance · correctness · maintainability
writing / docsreferences/personas-writing.mdeditor · fact-checker · audience · misreader
plan / decisionreferences/personas-plan.mdpremortem · dependencies · cost · stakeholder
research / a claimreferences/personas-research.mdrefuter · methodology · sources · bias
a release (assembled diff + changelog + docs)references/personas-release.mdconsumer-upgrade · docs-coherence · changelog · interactions

The default quartet works for anything; the packs sharpen it. Mix and match.

Guard-rails

  • Requires fresh-context reviewers. With a subagent primitive (Claude Code), spawn them; without one, run each lens sequentially in a clean context or session — or paste the neutral artifact brief into N separate fresh chats yourself. Independence survives the fallback; concurrency is what you lose.
  • Cost is real. A capacity-dispatch policy, when installed (e.g. humblepowers' choosing-models), sets reviewer tier by stakes; otherwise offer to drop a ladder level.
  • Reviewing a repo whose plugin is also installed? State which copy the panel reads (working tree vs installed cache) — the two diverge in either direction mid-release.
  • Independence is the whole point. Brief reviewers with your conclusion and you have wasted the panel. Keep the artifact brief neutral and the reviewers blind.
  • You synthesize; they don't decide. The panel informs; the call stays with the user. Surface the range, including the lone dissent — a 3–1 split is signal.

What this does NOT do

  • Replace a quick factual check or a single obvious answer — just answer it.
  • Audit a large file corpus — that is a blind fan-out over many files; use corpus-review.
  • Auto-fire — it proposes and waits for the go-ahead.
  • Reach consensus by averaging — disagreement is the signal, not noise.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.