Use when about to claim a result, effect, significance, or that an analysis reproduces, before reporting or writing it up - requires running the analysis fresh and reading the actual output first; evidence before claims always
npx skills add https://github.com/K-Dense-AI/science-superpowers --skill verifying-results-before-claiming
Claiming a finding without fresh verification is dishonesty, not efficiency.
Core principle: Evidence before claims, always.
Violating the letter of this rule is violating the spirit of this rule.
NO CLAIMS WITHOUT FRESH REPRODUCED EVIDENCE
If you haven't run the analysis in this state and read its actual output, you cannot claim its result. "It was significant earlier" is not evidence now.
BEFORE claiming any result or expressing satisfaction:
1. IDENTIFY: What command/analysis proves this claim?
2. RUN: Execute it fresh and complete (from the immutable raw data, fixed seed)
3. READ: The actual output — the estimate, the interval, the p-value, the diagnostics
4. CHECK: Do the method's assumptions hold? Does it reproduce?
For a CONFIRMATORY claim: does the pre-registration audit pass?
(prereg.sh, ships with science-superpowers:preregistering-analysis:
<skills root>/preregistering-analysis/prereg.sh audit)
5. VERIFY: Does the output actually support the claim?
- If NO: state the real result with evidence
- If YES: state the claim WITH the evidence (number + interval)
6. ONLY THEN: make the claim
Skip any step = asserting, not verifying
| Claim | Requires | Not Sufficient |
|-------|----------|----------------|
| "The effect is significant" | Fresh run; read estimate, CI, and p | "It was significant before" |
| "There's no effect" | Effect size + interval showing precision | A non-significant p (could be underpowered) |
| "The result reproduces" | Re-run from raw data + fixed seed → same number | "It ran fine earlier" |
| "Assumptions are met" | The diagnostic output, read | "It's probably fine" |
| "The model is good" | Out-of-sample metric | In-sample fit / training accuracy |
| "Data cleaned correctly" | Validation counts (rows in/out, ranges) | "The script ran without error" |
| "Confirmatory finding" | prereg.sh audit passes AND re-run as registered | The prereg file was committed before the results (it may have been edited after) |
| "The subagent finished" | Inspect the committed artifacts/diff | The subagent said "done" |
prereg.sh audit — commit order alone is not a freeze| Excuse | Reality |
|--------|---------|
| "It was significant last run" | Re-run it now. Code/data may have changed. |
| "I'm confident in the result" | Confidence is not evidence. |
| "The script ran without errors" | Running ≠ correct. Read the output. |
| "p < .05, so it's real" | p is not the probability the effect is real. Report effect + interval. |
| "p > .05, so no effect" | Could be underpowered. Absence of evidence ≠ evidence of absence. |
| "The subagent reported success" | Verify the artifacts independently. |
| "The prereg was committed before the results, so it's frozen" | Commit order misses post-freeze edits to the registration. Run prereg.sh audit; read the frozen-vs-current diff. |
| "It reproduces, I'm sure" | Re-run from raw + seed and show the same number. |
| "Different words, so the rule doesn't apply" | Spirit over letter. |
Significance / effect:
✅ [Re-run the model] [Read: beta=0.23, 95% CI [0.08, 0.38], p=.002] "Exposure raises the outcome; CI excludes zero."
❌ "The effect looked significant."
Reproducibility:
✅ Fresh env → re-run from data/raw with seed → same estimate to reported precision → "Reproduces."
❌ "It ran earlier, so it reproduces."
Null result:
✅ [Read: beta=0.01, 95% CI [-0.12, 0.14]] "No detectable effect; the interval rules out effects larger than ~0.14."
❌ "p=0.3, so there's no effect."
Model performance:
✅ [Held-out test set, used once] [AUC=0.78] "Out-of-sample AUC 0.78."
❌ "Training accuracy is 0.97, the model is great."
Confirmatory label:
✅ [Run prereg.sh audit] [Read: RESULT: PASS] → apply the frozen decision rule to the fresh output
❌ "The pre-registration predates the results commit, so the finding is confirmatory."
Subagent delegation:
✅ Subagent reports done → inspect committed code + output artifact → confirm → report actual state
❌ Trust the report
ALWAYS before:
Applies to: exact phrases, paraphrases, synonyms, and any implication of a verified finding.
Run it fresh. Read the output. Check it reproduces. THEN state the result — with the number and the interval.
This is non-negotiable.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementing any feature or bugfix, before writing implementation code
Use when you have a spec or requirements for a multi-step task, before touching code
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Use when writing or improving README files. Not all READMEs are the same — provides templates and guidance matched to your audience and project type.
| Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
Take k-dense-ai/verifying-results-before-claiming from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.