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clinical-research

Use when designing a prospective clinical study before submission — selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-endpoint flagging), estimating sample size and power for two-arm designs (means / proportions / survival), or scoring a study plan for feasibility and a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision. Every output is an ESTIMATE plus a named human owner (clinician / biostatistician / regulatory owner) — never clinical fact, never a finished protocol. Distinct from ra-qm-team, which handles the regulatory/QM submission (ISO 13485, EU MDR, FDA 510(k)/PMA/QSR), not the study design.

68

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is clinical-research in alirezarezvani/claude-skills

SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Highly actionable content with an explicit sequenced workflow and clearly signaled one-level-deep references. The main costs are repetition of the estimate/owner and surrogate-endpoint caveats across sections, a "Distinct from" table that duplicates the "Do NOT use" list, and referenced bundle paths that are not verifiable in this review environment.

Suggestions

Merge the "Do NOT use this skill to" list and the "Distinct from" table into a single boundary section — they state the same neighbor-skill distinctions twice.

State the "every output is an estimate with a named owner who must sign" caveat once (e.g., in Purpose) instead of repeating it in the intro, anti-patterns, and forcing-question 5; similarly consolidate the surrogate-endpoint warning, which currently appears in four sections.

Move the ~25-line forcing-question library to a reference file (e.g., references/forcing_questions.md) and keep a one-line pointer plus the walk-order rule in SKILL.md, tightening the token budget of the always-loaded overview.

DimensionReasoningScore

Conciseness

Mostly efficient (commands, flags, config paths, canon citations all earn their place), but the "estimate + named human owner" caveat is repeated in the intro ("Every output is an estimate with stated assumptions routed to a named human owner"), the anti-patterns ("Presenting a power estimate as fact. Every output is an estimate with a named owner who must sign."), and forcing-question 5; the surrogate-endpoint warning appears in four separate sections ("Penalizes unvalidated surrogate endpoints", "unvalidated serum-cytokine surrogate (cannot be primary)", "An unvalidated surrogate cannot anchor a PRIMARY endpoint", "Anchoring a primary on an unvalidated surrogate"); and the "Distinct from" table substantially duplicates "Do NOT use this skill to". This fits the anchor 'mostly efficient but includes some unnecessary explanation or could be tightened' — more than the 'minor instances' of the 4 anchor.

3 / 5

Actionability

Copy-paste-ready commands with real flag values throughout: "python3 scripts/sample_size_estimator.py --design proportions --p1 0.30 --p2 0.45 --dropout 0.15", "phase_gate_scorer.py --input study.json --profile <same> --phase {1|2|3|4}", onboarding ("python3 scripts/onboard.py --show"), and a full "/ar:setup --domain custom --name trial-feasibility ..." invocation. The "--sample" quick examples cover the common cases, matching the anchor 'fully executable; copy-paste ready code or commands; specific examples cover the common cases'.

5 / 5

Workflow Clarity

A clear 5-step numbered workflow (draft synopsis → select endpoint → estimate sample size → score feasibility → route for sign-off), each step with its exact command, plus conditional handling ("If >1 primary, plan multiplicity control") and a human sign-off checkpoint in step 5. Not 5 because there is no explicit error-recovery loop (e.g., what to revise and re-score when the verdict is REDESIGN/NO-GO is left implicit); not 3 because sequence and most checkpoints are present. These are analysis tools, not destructive/batch operations, so the workflow cap does not apply.

4 / 5

Progressive Disclosure

Good structure: a References section clearly signals three one-level-deep files with content summaries ("references/study_design_canon.md — ICH E8(R1)... ICH E9 + E9(R1) estimand addendum; CONSORT 2010..."), and a Scripts table maps each tool to its purpose. However, no bundle directories (references/, scripts/, assets/) exist alongside SKILL.md in this review, so the referenced paths (e.g., "assets/protocol_synopsis_template.md") cannot be verified as real, and the ~25-line forcing-question library is inlined detail that could live in a reference file. Fits the anchor 'good structure; most content is appropriately placed; references mostly clear; minor organization gaps' rather than the fully verified, cleanly split structure of the 5 anchor.

4 / 5

Total

16

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete multi-action coverage with parameters, an explicit "Use when" trigger clause, and explicit boundary-setting against the sibling regulatory skill. Only weakness is a few missing natural synonyms ("clinical trial", "protocol", "power analysis").

DimensionReasoningScore

Specificity

Three concrete, parameterized actions are listed: "selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-endpoint flagging)", "estimating sample size and power for two-arm designs (means / proportions / survival)", and "scoring a study plan for feasibility and a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision". This matches the anchor 'lists multiple specific concrete actions; comprehensive coverage' rather than the 4 anchor, since coverage spans all three tools with concrete classification schemes and output types, leaving only minor gaps.

5 / 5

Completeness

It explicitly answers both: what (endpoint selection/classification, sample-size and power estimation for means/proportions/survival, feasibility scoring with named verdicts) and when ("Use when designing a prospective clinical study before submission — ... or scoring a study plan for feasibility..."). Matches the anchor 'clearly and explicitly answers both what AND when with concrete trigger phrases'; the when-clause is explicit and enumerated, so it exceeds the 4 anchor ('when' could be more explicit).

5 / 5

Trigger Term Quality

Natural phrases a clinical-research user would say are present: "designing a prospective clinical study", "sample size and power", "primary", "feasibility", "phase-gate decision", "surrogate-endpoint". Not 5 because a few common variations are missing ("clinical trial", "protocol", "power analysis"); not 3 because the included keywords are natural domain phrasing, not jargon-only.

4 / 5

Distinctiveness Conflict Risk

The niche (prospective clinical study design pre-submission) is clear and explicitly disambiguated from the nearest neighbor: "Distinct from ra-qm-team, which handles the regulatory/QM submission (ISO 13485, EU MDR, FDA 510(k)/PMA/QSR), not the study design". Matches the anchor 'clear niche with distinct triggers; minimal conflict risk' — the 4 anchor's 'minor overlap risk with closely related skills' is not triggered.

5 / 5

Total

19

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 14 missing

Warning

Total

14

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

.gemini/skills/clinical-research/SKILL.md

Reviewed

Table of Contents

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