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 bestagentkits/agency-skills --skill clinical-researchgit clone --depth 1 https://github.com/bestagentkits/agency-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/bestagentkits/agency-skills/clinical-research)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/clinical-research"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/clinical-research/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/bestagentkits/agency-skills/clinical-research"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/clinical-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00152 | $0.02585 |
| Opus 5.5 | $0.00061 | $0.01034 |
| Sonnet 5.5 | $0.00030 | $0.00517 |
| Haiku 4.5 | $0.00015 | $0.00259 |
Grade A, and why
clinical-research 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 1mo 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
clinical-research
Prospective clinical study DESIGN: endpoints, sample size / power, and phase-gate feasibility. Every output is an estimate with stated assumptions routed to a named human owner. This skill never gives clinical advice as fact and never substitutes for a biostatistician or regulatory affairs.
Purpose
R&D clinical teams, medical monitors, and biostatistics functions live at the moment between we-have-a-hypothesis and we-have-a-protocol-ready-for-submission. This skill structures three of the hardest design decisions:
Three deterministic tools:
sample_size_estimator.py— Closed-form power / sample-size for two-arm means (Cohen's d), proportions (normal approximation), and survival (Schoenfeld events). Inflates for dropout. Prints an "ESTIMATE — confirm with a biostatistician" banner.endpoint_selector.py— Scores candidate endpoints across 5 weighted dimensions (clinical relevance, measurability, regulatory acceptance, sensitivity-to-change, burden) and classifies each as PRIMARY / KEY-SECONDARY / EXPLORATORY. Penalizes unvalidated surrogate endpoints.phase_gate_scorer.py— Scores a study plan 0-100 across recruitment feasibility, endpoint readiness, statistical power, operational complexity, and budget fit; returns GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO plus the named owners who must sign.
When to use
Invoke this skill when:
- You are choosing a primary endpoint and need to defend it against surrogate-endpoint scrutiny.
- You need a defensible first sample-size estimate for a protocol synopsis.
- A study plan needs a feasibility read before a phase-gate review.
- You are pressure-testing whether the planned enrollment is achievable given the eligible population and sites.
Do NOT use this skill to: prepare a regulatory submission or clinical evaluation report (use ra-qm-team), find or position a grant (use research/grants), design a live product A/B experiment (use product-team/experiment-designer), or replace a biostatistician's final sample-size justification.
What ships with it
11 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.
- agents/openai.yaml 208 B
- assets/protocol_synopsis_template.md 1.9 KB
- references/endpoint_and_power.md 3.0 KB
- references/study_design_canon.md 3.0 KB
- references/trial_operations.md 2.9 KB
- scripts/ar_evaluator.py 2.7 KB runs code
- scripts/config_loader.py 4.0 KB runs code
- scripts/endpoint_selector.py 6.6 KB runs code
- scripts/onboard.py 5.4 KB runs code
- scripts/phase_gate_scorer.py 9.2 KB runs code
- scripts/sample_size_estimator.py 8.5 KB runs code
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.
- 1mo ago First seen · 147 lines · 152 tokens per session scan A 2ee6764353ca
clinical-research is a skill published in the GitHub repository bestagentkits/agency-skills (24 stars, last pushed 3mo ago), licensed MIT. It adds 152 tokens to every session and 2,585 once invoked, about $0.0006 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 2026-09-03.
Other skills, from other repositories
self-review
Use when checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review.
check-reporting
Use when auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer critique (/self-review).
make-figures
Use when a paper needs publication-ready figures or a visual abstract. Makes ROC, forest, calibration, Kaplan-Meier and Bland-Altman plots, CONSORT/STARD/PRISMA flow diagrams, confusion matrices, pipeline diagrams and journal visual abstracts, checking the journal's AI-image policy first.
meta-analysis
Use when running a systematic review and meta-analysis, DTA or intervention. Covers PROSPERO protocol, search, screening, extraction, risk of bias (QUADAS-3, RoB 2, ROBINS-I), bivariate/HSROC or random-effects pooling, forest plots, heterogeneity and PRISMA reporting. Topic scouting is /ma-scout.
present-paper
Use when preparing an academic talk such as a journal club, grand rounds, seminar, conference presentation, or lecture/teaching deck. Analyzes the source, drafts audience-adapted speaker scripts, builds or augments the PPTX with speaker notes and prepares Q&A.
sync-submission
Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.