Purpose

Read a proposal as a demanding peer reviewer who needs convincing. Every claim is guilty until proven innocent. The goal is not to attack the plan (that is red-teaming) or to find hidden dependencies (that is assumptions audit)—the goal is to grade the evidence quality behind each assertion and flag where confidence outpaces proof.

This differs from related skills:

• red-team-challenge adopts an adversary's mindset and tries to exploit weaknesses. Skeptic-pass stays in reviewer mode and demands evidence.
• assumptions-audit extracts hidden dependencies. Skeptic-pass evaluates whether stated claims are actually supported.
• steelman rebuilds arguments in their strongest form. Skeptic-pass tests whether arguments hold up under scrutiny.

When to use this skill

Use when:

• the user says "skeptic pass", "challenge these claims", "what's hand-wavy", "where's the proof"
• a proposal reads confidently but cites no evidence, benchmarks, or prior art
• a plan uses quantitative claims (latency targets, cost savings, adoption rates) without sourcing them
• a design document needs intellectual honesty review before stakeholder presentation
• after a steelman pass, to test whether even the strongest version holds up

Do NOT use when:

• the task is adversarial exploitation of a system (use

  red-team-challenge

  )
• the task is finding unstated dependencies (use

  assumptions-audit

  )
• the task is judging execution order and safety of a plan (use

  plan-review

  )
• the evidence is already known to be absent and the goal is just to list what's missing (use

  gap-analysis

  )

Operating procedure

1. Extract every claim. Read the document and list every assertion that could be true or false. Include:

   • Factual claims ("Our API handles 10k req/s")
   • Causal claims ("This will reduce churn by 20%")
   • Feasibility claims ("This can be done in two sprints")
   • Comparative claims ("Option A is faster than Option B")
   • Consensus claims ("The team agrees that…", "Industry best practice is…")
   • Absence claims ("There is no risk of…", "This won't affect…")

2. Grade the evidence behind each claim. For each claim, assign one of:

   • Proven: Supported by cited data, benchmark, test result, or authoritative source
   • Plausible: Reasonable but unsupported—no evidence cited, just sounds right
   • Asserted: Stated as fact with no supporting argument at all
   • Contradicted: Available evidence suggests the opposite
   • Unfalsifiable: Stated in a way that cannot be tested or disproven ("this will be robust")

3. Identify optimism bias patterns. Flag these common patterns when present:

   • Planning fallacy: Timeline assumes everything goes right, no buffer for unknowns
   • Anchoring: A specific number is used without justification, and all reasoning anchors to it
   • Survivorship bias: "Company X did this successfully" without noting failures
   • Appeal to novelty: Preferring the new approach because it's new, not because evidence supports it
   • Sunk cost: Continuing a direction because of past investment, not future value
   • Consensus illusion: "Everyone agrees" when no explicit poll or decision was recorded
   • Precision theater: False precision (e.g., "this will take 14.5 days") masking deep uncertainty

4. Stress-test the causal claims. For every "X will cause Y" or "doing X will result in Y":

   • Is the mechanism explained or just asserted?
   • Are there confounding factors?
   • Has this causal chain been observed before, or is it hoped for?
   • What would disprove this claim?

5. Challenge the numbers. For every quantitative claim:

   • Where does this number come from?
   • Is it a measurement, estimate, or guess?
   • What's the confidence interval?
   • Is it cited or derived?
   • Does it survive a sanity check against known baselines?

6. Produce a credibility assessment. Grade the overall document:

   • Well-supported: Most claims are proven or plausible with clear reasoning
   • Mixed: Some claims are supported, others are pure assertion
   • Weakly-supported: Most claims are asserted without evidence
   • Overconfident: Confidence level far exceeds evidence quality

Output contract

Return a Skeptic Report with:

1. Claims Inventory

   — numbered list of extracted claims

2. Evidence Grades

   — table: Claim | Grade (Proven/Plausible/Asserted/Contradicted/Unfalsifiable) | Evidence Cited | Evidence Needed

3. Optimism Bias Flags

   — specific patterns identified with the triggering text

4. Weakest Claims

   — the 3-5 claims with the largest gap between confidence and evidence

5. Overall Credibility

   — one of the four grades above, with a one-paragraph justification

6. Evidence Collection Plan

   — for the weakest claims, what specific evidence would resolve the doubt

Named failure modes of this method

• Cynicism spiral: Doubting everything equally instead of prioritizing claims that matter. Fix: focus skepticism on claims that drive decisions, not background context.
• Demanding impossible proof: Requiring RCT-level evidence for reasonable engineering judgment. Fix: calibrate evidence expectations to the claim's impact—high-stakes claims need strong evidence, low-stakes claims need plausibility.
• Missing the forest: Grading individual claims without assessing whether the overall argument holds even if some claims are weak. Fix: always include the overall credibility assessment.
• Style policing: Flagging vague language when the underlying claim is actually well-supported elsewhere. Fix: check whether evidence exists before flagging weak phrasing.
• Confirmation bias in review: Only challenging claims you disagree with while accepting others at face value. Fix: grade every claim, including ones that feel obviously true.

References

• Kahneman, D. (2011). Thinking, Fast and Slow — cognitive biases in judgment
• RFC 7282 (https://www.rfc-editor.org/rfc/rfc7282.html) — on consensus and objection handling in technical review
• Tetlock, P. (2015). Superforecasting — calibrating confidence to evidence
• Sagan, C. "Extraordinary claims require extraordinary evidence" — evidence proportionality principle

Failure handling

• If the document is too short to contain meaningful claims, say so rather than manufacturing doubt.
• If all claims are well-supported, report that the skeptic pass found high credibility—do not force criticism.
• If the document is a brainstorm or early draft, adjust evidence expectations downward and note that a full skeptic pass requires a more developed proposal.