rigor-review
Skill1 starsChanged 42 days agoNo licenceView on GitHub
Project-long scientific rigor review of each research output (figure, table, analysis script, results file, methods or results draft). Flags issues likely to draw reviewer pushback (weak or untuned baselines, leakage, pseudoreplication, double dipping, missing variance, multiplicity, invalid tests, metric-claim mismatch, overclaiming), recommends more rigorous approaches grounded in retrieved current literature, and runs a tiered prior-work search to catch methods that are already published. Keeps a running rigor ledger artifact so open issues are re-checked and searches are reused across the project. Use whenever the user asks to review, sanity-check, stress-test, or critique an output or method for rigor, novelty, or reviewer readiness, asks whether something has been done before, or finishes a deliverable in an ongoing project, even if they do not say rigor. For a formal referee report on a finished manuscript use peer-review instead.
In this repository23 other files it gives its agents.
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- claim-gateSkill
- cli-subagentsSkill
- cluster-autoscoutSkill
- cluster-rulesSkill
- codex-offloadSkill
- deslopSkill
- doc-build-gateSkill
- experiment-preflight-singlecellSkill
- experiment-preflightSkill
- fail-loud-pipelinesSkill
- gwas-analysisSkill
- job-watchSkill
- lab-notebookSkill
- paper-outlineSkill
- phase-delegationSkill
- reproducibleSkill
- run-status-boardSkill
- science-daemon-opsSkill
- session-handoffSkill
- standing-instructionsSkill
- variant-annotationSkill
- variant-callingSkill
The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 5 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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