designing-the-analysis
Use when you have an approved research question and need a concrete analysis plan, before touching outcome data or fitting any…
Use after completing an analysis or before reporting a finding, to have a skeptical reviewer attack the conclusion before you believe it
$ npx -y skills add K-Dense-AI/science-superpowers --skill requesting-red-team-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/requesting-red-team-reviewContext preview
The summary Claude sees to decide when to auto-load this skill.
Use after completing an analysis or before reporting a finding, to have a skeptical reviewer attack the conclusion before you believe it
name: requesting-red-team-review description: Use after completing an analysis or before reporting a finding, to have a skeptical reviewer attack the conclusion before you believe it
Dispatch a reviewer subagent whose explicit job is to find why your conclusion is wrong — before you believe it or report it. The reviewer gets precisely crafted context for evaluation, never your session's history. This keeps it focused on the work product and your reasoning, not on agreeing with you, and preserves your own context.
**Core principle:** Try hardest to break a finding when it's most likely to be right. A result that survives a genuine attack is worth reporting; one that hasn't been attacked isn't.
**Mandatory:**
**Especially valuable:**
**1. Get the git SHAs that bound the work:**
BASE_SHA=$(git rev-parse HEAD~N) # before the analysis HEAD_SHA=$(git rev-parse HEAD)
**2. Dispatch the reviewer subagent** with the Task tool (general-purpose), filling the template at `reviewer.md`.
**Placeholders:**
**3. Act on feedback:**
The reviewer is prompted to look for:
[Completed primary analysis: exposure → outcome, pre-registered] You: Requesting red-team review before I report this. BASE_SHA=a1b2c3d HEAD_SHA=e4f5g6h [Dispatch reviewer subagent with reviewer.md filled in] Reviewer returns: Critical: Site is associated with both exposure and outcome but isn't in the model — likely confound (Simpson's risk). Important: No check of the homoscedasticity assumption. Minor: Figure axis unlabeled. You: [Site WAS pre-registered as a covariate — verify the model actually included it] [Check: the model formula dropped `site` due to a typo. Reviewer is right. Fix, re-run, re-verify.]
**Never:**
**If the reviewer is wrong:** push back with technical reasoning and show the verification (the diagnostic, the pre-registration line, the reproduced number) — see `science-superpowers:receiving-critical-review`.
See template at: `requesting-red-team-review/reviewer.md`
Science Superpowers is a complete computational-science methodology for your research agents, built on a set of composable skills plus initial instructions that make sure your agent actually uses them.
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