Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Aperivue/medsci-skills --skill self-reviewgit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aperivue/medsci-skills/self-review)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/self-review"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/self-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/self-review"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.12165 |
| Opus 5.5 | $0.00020 | $0.04866 |
| Sonnet 5.5 | $0.00010 | $0.02433 |
| Haiku 4.5 | $0.00005 | $0.01217 |
Grade A, and why
self-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 732 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Review Skill
Check the user's own manuscript before submission and produce an actionable list of anticipated reviewer comments, each with a specific fix — not a written review.
Optional Flags
--fix: after the report, apply fixes for every issue withfixable_by_ai: true, editing the manuscript in place, then report a diff summary. Never fixfixable_by_ai: falseissues (missing data, design flaws). Maximum 2 fix-and-re-review iterations; if the score is still below threshold after the second, stop and report what remains as structural (inside/write-paperthis routes to Phase 7.4a Audit Recovery).--json: also emit the structured JSON block (Phase 3c). Default when called from/write-paperPhase 7.--panel: run the multi-agent panel review (Phase 2.6) — domain-expert reviewers in parallel plus an editor synthesis — instead of the single-pass review. Opt-in and off by default, because it spawns N reviewer agents + 1 editor and costs several times more tokens; reserve it for a high-stakes final pass on a top-tier target. Do not combine with--fix: a panel diagnoses and prioritizes; run--fixas a separate pass once the author has triaged the panel's findings.
Severity Framing
- Fatal: a design flaw existing data cannot fix (data leakage that invalidates all results, no reference standard, label–feature circularity). Submission would likely be rejected. Reserve Fatal for true design-level problems.
- Fixable: significant but addressable with existing data (missing calibration, unclear exclusion criteria, absent CIs, incomplete reporting). Most issues are Fixable.
Two Objectives: the Floor and the Ceiling
The floor (categories A–K and the gates of Phases 2.5–2.5f) minimizes rejection-for-cause — fabricated citations, numbers that do not reconcile, overclaims, missing checklist items, leakage — and much of it works by adding a hedge, caveat, disclosure or audit trail. Iterated, that over-hardens a manuscript: every finding is correct, yet the whole reads as a defensive audit (over-hedged, audit trail in the body, Abstract buried under caveats, the strongest robustness result hidden in Limitations). The ceiling pass (category L / Phase 2.5g) runs after the floor gates, reads the accurate manuscript as a whole, and recommends only SUBTRACTION — REMOVE, MOVE, or TIGHTEN. It is advisory, never blocks, and cannot relax a floor gate. Report its findings as their own block (Phase 3), not folded into the "add this" comments. Phase 2.5i then reads the floor + ceiling state to declare the loop done, including a zero-edit PASS.
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/domain-probes/ai_overclaiming.md 15 KB
- references/domain-probes/case_report.md 8.8 KB
- references/domain-probes/clinical_prediction_model.md 7.7 KB
- references/domain-probes/diagnostic_accuracy.md 15 KB
- references/domain-probes/equity_fairness.md 7.5 KB
- references/domain-probes/health_economic_evaluation.md 8.2 KB
- references/domain-probes/image_synthesis.md 6.1 KB
- references/domain-probes/mendelian_randomization.md 11 KB
- references/domain-probes/mllm_evaluation.md 7.6 KB
- references/domain-probes/model_development.md 15 KB
- references/domain-probes/narrative_review.md 13 KB
- references/domain-probes/network_meta_analysis.md 9.2 KB
- references/domain-probes/observational_confounding.md 36 KB
- references/domain-probes/polygenic_risk_score.md 9.5 KB
- references/domain-probes/qualitative_research.md 6.8 KB
- references/domain-probes/radiomics.md 5.7 KB
- references/domain-probes/rct_trial.md 9.1 KB
- references/domain-probes/record_routinely_collected_data.md 7.4 KB
- references/domain-probes/scoping_review.md 7.1 KB
- references/domain-probes/self_improving_system.md 8.2 KB
- references/domain-probes/sr_ma.md 20 KB
- references/domain-probes/survey_research.md 7.1 KB
- references/domain-probes/survival_prognostic.md 14 KB
- references/evalues_schema.md 5.2 KB
- references/exemplar_findings/cohort_arithmetic_mismatch.md 1.8 KB
- references/exemplar_findings/cross_sectional_mediation.md 2.8 KB
- references/exemplar_findings/estimand_drift_posthoc_primary.md 2.1 KB
- references/exemplar_findings/over_adjustment_collider.md 2.6 KB
- references/exemplar_findings/prediction_two_null_conflation.md 2.6 KB
- references/exemplar_findings/README.md 3.1 KB
- references/exemplar_findings/scope_overreach_cross_sectional.md 1.9 KB
- references/exemplar_findings/unadjusted_confounder.md 1.9 KB
- references/p_tests_schema.md 5.0 KB
- references/panel_review_template.md 17 KB
- references/phases/confounding_completeness.md 5.2 KB
- references/phases/phase2_5a_source_fidelity.md 5.8 KB
- references/phases/phase2_5a2_design_power.md 4.2 KB
- references/phases/phase2_5b_screening_counts.md 7.5 KB
- references/phases/phase2_5c_reference_scans.md 7.7 KB
- references/phases/phase2_5d_xref_qc.md 5.5 KB
- references/phases/phase2_5f_claim_artifact.md 12 KB
- references/phases/phase2_6_panel.md 10 KB
- references/phases/phase2_systematic_check.md 20 KB
- references/phases/phase3c_json_output.md 6.5 KB
- references/phases/phase4_fix_support.md 3.4 KB
- scripts/_frontmatter.py 2.3 KB runs code
- scripts/_prose.py 2.5 KB runs code
- scripts/_qc_findings.py 6.3 KB runs code
- scripts/check_analysis_definitions_challenge/expected/defined.txt 351 B
- scripts/check_analysis_definitions_challenge/expected/undefined.txt 921 B
- scripts/check_analysis_definitions_challenge/fixture/defined.md 1.3 KB
- scripts/check_analysis_definitions_challenge/fixture/undefined.md 887 B
- scripts/check_analysis_definitions_challenge/problem.md 2.7 KB
- scripts/check_analysis_definitions_challenge/verify.sh 1.5 KB runs code
- scripts/check_analysis_definitions.py 13 KB runs code
- scripts/check_aphorism_density.py 8.2 KB runs code
- scripts/check_artifact_coverage.py 17 KB runs code
- scripts/check_baseline_drift_challenge/expected/drifted.txt 576 B
- scripts/check_baseline_drift_challenge/expected/nobaseline.txt 139 B
- scripts/check_baseline_drift_challenge/expected/overhedged.txt 237 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 20d ago Changed · +1 lines dca8eb084221
- 21d ago Changed · +14 lines 52d153796a72
- 22d ago Changed · +33 lines 9a2c9026547c
- 25d ago Changed · -203 lines · -4 tokens per session 5e21d1aa3a9f
- 26d ago Changed 4c6d0e2889d3
- 1mo ago Changed · -2 lines 69f70f778ebf
- 1mo ago First seen · 889 lines · 54 tokens per session scan A 00453644f660
self-review is a skill published in the GitHub repository Aperivue/medsci-skills (349 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 12,165 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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