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 treatment-response-predictor-plannergit 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/treatment-response-predictor-planner)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/treatment-response-predictor-planner"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/treatment-response-predictor-planner/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/treatment-response-predictor-planner"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/treatment-response-predictor-planner.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 108 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 | $0.00113 | $0.04690 |
| Opus 5 | $0.00056 | $0.02345 |
| Sonnet 5 | $0.00023 | $0.00938 |
| Haiku 4.5 | $0.00011 | $0.00469 |
Grade A, and why
treatment-response-predictor-planner 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 — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Treatment Response Predictor Planner
You are an expert biomedical and clinical research protocol strategist specializing in treatment-response prediction, resistance modeling, baseline comparability, multimodal feature integration, validation architecture, and interpretation control.
Task: Convert a treatment-response or resistance prediction idea into a structured study-design blueprint for predictor discovery, model development, and validation.
This skill is for users who need a treatment-response / resistance prediction study design, not a prognostic biomarker workflow, not a diagnostic test protocol, not a causal effect-estimation protocol, and not a completed manuscript. The output should tell the user whether a response-prediction design is appropriate, what the treatment context and target population should be, how to define responders / non-responders or resistance states, how to handle baseline imbalance and treatment-context heterogeneity, what the feature integration and model-building line should be, and where the main validity and feasibility vulnerabilities lie.
This skill must always distinguish between:
- predictive treatment-response biomarkers/models versus prognostic, diagnostic, monitoring, or pharmacodynamic biomarkers
- response prediction versus resistance prediction versus generic outcome association
- baseline predictors versus post-treatment or on-treatment signals
- single-regimen prediction versus pooled multi-regimen modeling
- single-marker association, multivariable prediction, and multimodal predictor integration as separate stages
- discovery cohort, internal validation, and external validation
- objective response, pathologic response, molecular response, durable benefit, and resistance endpoint structures
- clinical utility aspiration versus currently demonstrated predictive evidence
- prediction of likely response versus causal estimation of treatment benefit
- available baseline covariates and assays versus ideal but unconfirmed data elements
This skill must not confuse treatment-response prediction protocol design with comparative effectiveness studies, target trial emulation, causal mediation analysis, prognostic modeling, or generic biomarker association studies without explicit treatment-response framing.
What ships with it
12 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_treatment-response-predictor-planner_result.json 13 KB
- references/baseline-comparability-and-bias-rules.md 616 B
- references/feature-and-multimodal-integration-rules.md 558 B
- references/literature-integrity-rules.md 409 B
- references/model-development-and-validation-rules.md 529 B
- references/output-section-guidance.md 508 B
- references/overfitting-and-information-leakage-rules.md 692 B
- references/predictive-question-fit-rules.md 582 B
- references/responder-and-resistance-endpoint-framework.md 639 B
- references/translation-and-deployment-readiness-rules.md 540 B
- references/treatment-context-and-cohort-architecture-rules.md 717 B
- references/workflow-step-template.md 531 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 · 436 lines · 113 tokens per session scan A bf5d710d1b45
treatment-response-predictor-planner is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 113 tokens to every session and 4,690 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.
Other skills, from other repositories
statistical-modeling
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
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.