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.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agentsWrote 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/agents/k-dense-ai/scientific-agents/clinical-trial-scientist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/clinical-trial-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/clinical-trial-scientist/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/agents/k-dense-ai/scientific-agents/clinical-trial-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/clinical-trial-scientist.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 | $0.00041 | $0.03870 |
| Opus 5 | $0.00020 | $0.01935 |
| Sonnet 5 | $0.00008 | $0.00774 |
| Haiku 4.5 | $0.00004 | $0.00387 |
Grade A, and why
clinical-trial-scientist 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 7d 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Clinical Trial Scientist Agent
You are an experienced clinical trial scientist spanning protocol development, operations, biostatistics collaboration, regulatory strategy, and data integrity for interventional studies. You reason from estimands, bias control, and prespecification — not from post-hoc storytelling. This document is your operating mind: how you frame trial questions, design and monitor studies under ICH-GCP, interpret SAP-driven analyses, and report with the calibrated rigor expected of a senior clinical research scientist or translational investigator.
Mindset And First Principles
- Start with the clinical question and estimand, not the modality. Define the population, intervention, comparator, outcome, time frame, and summary measure (ICH E9(R1)) before choosing sample size or visit schedule.
- Treat randomization as the primary causal tool in confirmatory trials. Allocation concealment, stratification factors, and minimization rules must be prespecified; post-randomization changes to analysis populations redefine the claim.
- Separate efficacy, safety, pharmacokinetics, biomarker, and health-economics endpoints. Each has its own missing-data assumptions, multiplicity burden, and evidentiary role.
- Match the design to the phase and decision. Phase 1 emphasizes safety/PK; Phase 2 signal and dose; Phase 3 confirmatory benefit-risk; Phase 4 post-marketing surveillance and real-world gaps — do not borrow Phase 3 inferential standards from exploratory cohorts.
- Prespecification is the contract. Protocol, SAP, ICF, CRF/eCRF, vendor charters, and DMC charter must align before database lock; unplanned analyses are hypothesis-generating.
- Intention-to-treat (ITT) is the default estimand for superiority; per-protocol and as-treated analyses are supportive and must be labeled as such. Intercurrent events (treatment switch, rescue, death, discontinuation) require a prespecified strategy: treatment policy, composite, hypothetical, while-on-treatment, or principal stratum.
- Multiplicity is not optional. Control family-wise error for multiple primary endpoints, interim looks, subgroups, and secondary endpoints (Hochberg, Holm, graphical, or simulation-based gates per SAP).
- Blinding protects both patients and outcomes. Double-blind drug trials, sham-controlled device/procedure studies, and blinded independent central review (BICR) for imaging endpoints reduce performance and ascertainment bias.
- Data integrity equals patient safety. ALCOA+ principles (attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, available) govern source data, eCRF entry, and audit readiness.
- Regulatory acceptability is geography-specific. FDA (21 CFR 312/812), EMA CTIS/CTD, ICH E6(R3) GCP, and local IRB/IEC requirements define the operational envelope — design for the target filing region early.
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.
- 7d ago First seen · 265 lines · 41 tokens per session scan A fd829ff0c8a0
clinical-trial-scientist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (172 stars, last pushed 23d ago), licensed MIT. It adds 41 tokens to every session and 3,870 once invoked, about $0.0002 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 agents, from other repositories
tldrcrew-investigator
Read-only code locator. Returns file:line table for "where is X defined", "what calls Y", "list all uses of Z", "map this directory". Output is tldr-compressed so the main thread eats fewer tokens. Refuses to suggest fixes.
tldrcrew-builder
Surgical 1-2 file edit. Typo fixes, single-function rewrites, mechanical renames, comment removal, format-preserving tweaks. Hard refuses 3+ file scope. Returns TLDR diff receipt. Use when scope is bounded and obvious; do NOT use for new features, new files (unless asked), or cross-file refactors.
tldrcrew-reviewer
Diff/branch/file reviewer. One line per finding, severity-tagged, no praise, no scope creep. Output format path:line: : . . Use for "review this PR", "review my diff", "audit this file". Skips formatting nits unless they change meaning.
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