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Expert redteam review

Skill carbonshow/intent-fluid/skills/expert-redteam-review

Use when a user asks to evaluate a complex, high-impact, ambiguous, cross-functional, or hard-to-reverse decision; mentions expert panel, red team, adversarial review, rebuttal, judge, arbitration, risk review, challenge assumptions, poke holes, or wants stronger decision quality than a normal review.From its SKILL.md

Install
npx -y skills add carbonshow/intent-fluid --skill expert-redteam-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 6 stars6 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.

SKILL.md

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Expert Red-Team Review

Use this skill to improve decision quality for complex work. The main agent stays accountable for state, scope, synthesis, and final communication. Experts, red team, rebuttal, and judge roles exist to reveal blind spots; they do not replace the user's decision.

Core Rules

  • Scale the workflow to the task. Do not run a full panel for trivial work.
  • Keep expert initial judgments independent. Do not show one expert another expert's initial answer.
  • Prefer 3-5 experts for serious reviews. More than 5 usually adds integration noise.
  • Build vertical-domain experts from success criteria and failure modes; do not pretend a generic role is domain expertise.
  • For vertical-domain L2/L3 work, propose the domain-specific panel and ask the user to confirm or adjust it before running the full review, unless the user has already approved the panel.
  • Label important claims with evidence tags: [source], [code], [test], [data], [inference], [engineering-judgment], [creative-judgment], or [unknown].
  • Optimize for decision quality, not consensus. Preserve meaningful disagreement.
  • The judge must arbitrate. A judge that only summarizes has failed.
  • P0/P1 red-team findings require minimum fixes. P2 risks require explicit acceptance rationale.
  • For one-way-door or high-blast-radius actions, stop at recommendation and ask for human approval before execution.

Level Selection

LevelUse WhenShape
L0 Quick CheckThe user wants a fast challenge or self-check.Single-agent red-team review.
L1 Expert Review2-3 perspectives are useful, but formal adversarial review is unnecessary.Small independent expert panel, no red team.
L2 Full ReviewThe user asks for expert panel/red team/judge or the decision has major uncertainty.Expert panel, synthesis, red team, rebuttal, judge.
L3 Gated ReviewThe decision is hard to reverse or affects production, security, privacy, finance, compliance, or shared systems.L2 plus explicit human gate before action.

Default downshift: if the task is low-risk or the user asks for speed, choose the lowest useful level. Default upshift: if L0/L1 reveals P0/P1 risks or severe disagreement, propose L2.

Operating Flow

  1. Triage the request and choose L0-L3.
  2. Build a compact context package: decision point, scope, non-goals, hard constraints, success criteria, evidence policy, and output target.
  3. Select orthogonal roles. For vertical domains, construct role cards from domain success criteria and failure modes.
  4. If the task is vertical-domain L2/L3 and the panel has not already been confirmed, stop and ask the user to confirm or adjust the proposed panel before full review.
  5. Run independent expert analysis when L1+ is needed.
  6. Synthesize consensus and disagreements. Lead with disagreements that affect the decision.
  7. Run red-team attack for L2/L3.
  8. Run rebuttal from relevant experts only.
  9. Judge the result: supported claims, weak claims, blocking risks, accepted risks, rejected alternatives, recommended path, minimum validation.
  10. For L3, ask the user before executing any irreversible or externally visible action.

Reference Files

FileRead When
references/workflow.mdYou need the detailed L0-L3 workflow, escalation rules, or failure handling.
references/role-library.mdYou need to choose experts or construct vertical-domain roles.
references/templates.mdYou need prompt/output templates for context package, experts, red team, rebuttal, or judge.
references/surge-integration.mdThe task involves an optional integration with surge.
references/evaluation.mdYou need to check whether the review itself is high quality.

Output Contract

For L0, keep the answer compact. For L1-L3, produce a decision package with:

  • Final judgment.
  • Recommended path and rejected alternatives.
  • Must-fix issues.
  • Accepted residual risks.
  • Open or deferred questions.
  • Minimum validation action.
  • Human gate status.
  • Files created or changed, if any.

Common Failure Modes

  • A panel gives agreeable summaries but no decision.
  • The red team attacks style instead of core assumptions.
  • The judge averages opinions instead of arbitrating.
  • Generic roles are used for a vertical domain without constructing domain-specific lenses.
  • The workflow becomes a hard dependency for another system that should only use it as an optional checkpoint.
  • The output is long but not actionable.

What ships with it: 6 files

27.6 KB alongside SKILL.md

evals/

Gives 0 of the 12 instructions most review quality skills give in 986 tokens

Counted across 1,273 of the 2,403 authors here whose files we hold, read 2026-09-06

  • Ask one question at a timein 63 of 1273, across 62 files
  • Provide a recommended answer for each questionin 47 of 1273, across 45 files
  • Rank findings by severityin 44 of 1273
  • Use parameterized queries for database accessin 38 of 1273, across 20 files
  • Validate all user input with schemasin 33 of 1273, across 15 files
  • Store secrets in environment variablesin 32 of 1273, across 14 files
  • Explore the codebase to answer questionsin 31 of 1273, across 29 files
  • Store tokens in httpOnly cookiesin 30 of 1273, across 12 files
  • Implement rate limiting on API endpointsin 30 of 1273, across 12 files
  • Sanitize user-provided HTMLin 29 of 1273, across 11 files
  • Return generic error messages to usersin 28 of 1273, across 10 files
  • Cite file and line for every findingin 28 of 1273, across 25 files

Said here and by no other author read

  • scale the review level to the task
  • keep expert initial judgments independent
  • construct domain-specific roles for vertical tasks
  • confirm the expert panel with the user for L2 or L3
  • label important claims with evidence tags
  • preserve meaningful disagreement instead of seeking consensus

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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