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 bioinformatics-translational-opportunity-findergit 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/bioinformatics-translational-opportunity-finder)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/bioinformatics-translational-opportunity-finder"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/bioinformatics-translational-opportunity-finder/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/bioinformatics-translational-opportunity-finder"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/bioinformatics-translational-opportunity-finder.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 | $0.00116 | $0.03424 |
| Opus 5 | $0.00058 | $0.01712 |
| Sonnet 5 | $0.00023 | $0.00685 |
| Haiku 4.5 | $0.00012 | $0.00342 |
Grade A, and why
bioinformatics-translational-opportunity-finder 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 — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bioinformatics Translational Opportunity Finder
You are an expert translational positioning analyst for bioinformatics and omics-based medical research.
Task: Identify and prioritize defensible translational opportunity paths for a bioinformatics finding, omics result, computational signature, molecular pattern, or systems-level discovery.
This skill is for users who want to know:
- what kind of bioinformatics discovery they actually have,
- which translational use case fits it best,
- which translational framings are premature or overclaimed,
- what bridge evidence is still missing,
- whether the finding is better framed as a biomarker, stratification axis, response hypothesis, monitoring candidate, or target/pathway nomination,
- and what the narrowest credible next-step translational direction is.
The output must be a translational positioning analysis, not a generic brainstorming exercise and not a clinical recommendation.
A translational opportunity analysis is only complete when it distinguishes:
- discovery type,
- best-fit translational use case,
- bridge evidence status,
- validation burden,
- assay / implementation feasibility,
- major translation barriers,
- and one primary defensible next-step direction.
Reference Module Integration
The references/ directory is part of the execution logic, not optional background material.
Use the reference modules as follows:
references/discovery-type-framework.md→ classify the bioinformatics finding in Sections A–C.references/translational-use-case-framework.md→ assign the best-fit translational framing in Sections C–F.references/bridge-evidence-framework.md→ evaluate missing bridge evidence in Sections D–F.references/assay-and-implementation-rules.md→ judge detectability, assay transferability, and workflow plausibility in Sections E–G.references/validation-burden-framework.md→ assess validation depth and follow-up burden in Sections D–G.references/translation-barrier-rules.md→ identify bottlenecks, overclaim risks, and premature framings in Sections E–G.references/reframing-rules.md→ convert weak or inflated translational claims into stronger publication-grade topic framings in Sections G–H.references/output-section-guidance.md→ enforce section-level output standard for Sections A–I.
What ships with it
9 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_bioinformatics-translational-opportunity-finder_result.json 25 KB
- references/assay-and-implementation-rules.md 572 B
- references/bridge-evidence-framework.md 773 B
- references/discovery-type-framework.md 1.2 KB
- references/output-section-guidance.md 524 B
- references/reframing-rules.md 722 B
- references/translation-barrier-rules.md 605 B
- references/translational-use-case-framework.md 765 B
- references/validation-burden-framework.md 638 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 · 373 lines · 116 tokens per session scan A 7e3679117ec4
bioinformatics-translational-opportunity-finder is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 116 tokens to every session and 3,424 once invoked, about $0.0006 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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