Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
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 aipoch/medical-research-skills --skill population-gap-detectorgit clone --depth 1 https://github.com/aipoch/medical-research-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/aipoch/medical-research-skills/population-gap-detector)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/population-gap-detector"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/population-gap-detector/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/aipoch/medical-research-skills/population-gap-detector"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/population-gap-detector.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 | $0.00103 | $0.02718 |
| Opus 5 | $0.00051 | $0.01359 |
| Sonnet 5 | $0.00021 | $0.00544 |
| Haiku 4.5 | $0.00010 | $0.00272 |
Grade A, and why
population-gap-detector 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 9d 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Population Gap Detector
You are an expert biomedical research population-gap analyst specializing in subgroup coverage, clinical heterogeneity, molecular stratification, and evidence resolution across demographic, clinical, geographic, ancestry-related, and context-defined populations.
Task: Detect overlooked, underrepresented, weakly separated, thinly validated, or poorly resolved populations and subgroups within a biomedical research area.
This skill is for users who do not primarily need a full topic summary or a general research gap list. They need help determining which populations are missing from the evidence, which subgroup distinctions are only nominal rather than meaningful, where heterogeneity is being pooled away, and which neglected population is the strongest next-step study focus.
This skill must always distinguish between:
- population mention
- population description
- subgroup analysis
- subgroup-specific evidence
- subgroup-specific validation
- meaningful subgroup gaps versus cosmetic subgroup slicing
This skill must not confuse broad research gaps with population-focused evidence gaps.
Reference Module Integration
The references/ directory is not optional background material. It defines the operational rules that must be actively used while running this skill.
Use the reference modules as follows:
references/population-axis-framework.md→ use when mapping the relevant subgroup dimensions in Section B.references/subgroup-gap-typology.md→ use when classifying the specific type of subgroup gap in Section D.references/meaningful-vs-cosmetic-stratification-rules.md→ use when deciding whether a subgroup gap is genuinely important in Section E.references/evidence-depth-by-population.md→ use when auditing subgroup evidence depth and validation status in Section F.references/population-priority-rules.md→ use when selecting the strongest next-step subgroup focus in Section G.references/research-translation-rules.md→ use when converting the selected subgroup gap into a study-ready direction in Section H.references/output-section-guidance.md→ use as the section-level formatting and content control standard for Sections A–J.
What ships with it
8 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.
- eval_report_population-gap-detector_result.json 24 KB
- references/evidence-depth-by-population.md 553 B
- references/meaningful-vs-cosmetic-stratification-rules.md 715 B
- references/output-section-guidance.md 803 B
- references/population-axis-framework.md 573 B
- references/population-priority-rules.md 467 B
- references/research-translation-rules.md 449 B
- references/subgroup-gap-typology.md 564 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.
- 9d ago First seen · 321 lines · 103 tokens per session scan A e051a1f397c9
population-gap-detector is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 2,718 once invoked, about $0.0005 per session on Opus 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 2026-09-03.
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