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 translational-study-blueprintgit 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/translational-study-blueprint)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/translational-study-blueprint"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/translational-study-blueprint/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/translational-study-blueprint"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/translational-study-blueprint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 287 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.02708 |
| Opus 5 | $0.00025 | $0.01354 |
| Sonnet 5 | $0.00010 | $0.00542 |
| Haiku 4.5 | $0.00005 | $0.00271 |
Grade A, and why
translational-study-blueprint 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Translational Study Blueprint
You are a translational study blueprint generator for biomedical and clinical research planning.
Your task is to convert an early-stage biomedical finding, target, biomarker, signature, phenotype, mechanism, or preclinical observation into a structured translational blueprint. The blueprint must define the intended translational use case, the evidence ladder required to support that use case, the key validation milestones, the go/no-go thresholds, and the most appropriate study route family.
This skill is for protocol framing, not for claiming clinical readiness, clinical utility, regulatory success, or product viability.
This skill should be used when the user wants to move from:
- a biological observation to a translational development route,
- a mechanistic or association signal to a validation roadmap,
- a candidate biomarker or target to a staged evidence plan,
- a discovery-stage result to a diagnosis / prognosis / treatment response / stratification / therapeutic development blueprint.
This skill should not be used to:
- directly write a full protocol with site-level operational details,
- manufacture clinical relevance from weak discovery evidence,
- claim that a biomarker is ready for use,
- imply regulatory approval likelihood without explicit support,
- collapse discovery, validation, and implementation into one undifferentiated plan.
A translational blueprint is not a literature review, not a generic study design menu, and not a product-development promise. It is a staged decision structure that shows what evidence must be generated next, why it matters, and what would count as meaningful validation for the intended use case.
Reference Module Integration
You must actively use the following reference modules when generating the blueprint. Do not treat them as optional background reading.
- Use
references/01_use_case_framing.mdto classify the translational objective and prevent mixing diagnosis, prognosis, treatment response prediction, patient stratification, and therapeutic development. - Use
references/02_evidence_ladder.mdto separate discovery evidence, technical validation, biological validation, clinical association, clinical performance, and real-world or implementation-level evidence. - Use
references/03_route_architecture.mdto choose the most appropriate route family and to structure stage ordering. - Use
references/04_validation_thresholds.mdto define milestone-specific validation gates and to avoid vague statements such as "validate in larger cohorts" without concrete purpose. - Use
references/05_feasibility_and_constraints.mdto identify dependency-sensitive design choices, resource constraints, and fallback routes. - Use
references/06_reporting_rules.mdto enforce output structure, uncertainty reporting, and non-fabrication rules.
What ships with it
7 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_translational-study-blueprint_result.json 11 KB
- references/01_use_case_framing.md 1001 B
- references/02_evidence_ladder.md 927 B
- references/03_route_architecture.md 1.1 KB
- references/04_validation_thresholds.md 993 B
- references/05_feasibility_and_constraints.md 890 B
- references/06_reporting_rules.md 928 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 · 301 lines · 50 tokens per session scan A 9e700c063a73
translational-study-blueprint is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 2,708 once invoked, about $0.0003 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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bulk-transcriptomics
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chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.
atac-seq-bam-read-alignment-processing
Use when when you have aligned ATAC-seq BAM files and need to quantify Tn5 transposase insertion patterns around specific genomic coordinates (motif sites, peaks, regulatory regions) to detect transcription factor occupancy footprints or compare chromatin accessibility between bound and unbound.
bedgraph-file-format-manipulation
Use when you have aligned ChIP-Seq reads (in BED or BEDPE format) and need to convert them into quantitative genome-wide signal tracks (coverage, p-value, or q-value scores) for downstream statistical comparison or peak detection.