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clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

67

Quality

80%

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is clinical-reports in K-Dense-AI/scientific-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

A tightly engineered skill body: an input gate, a routed five-step workflow, fully executable validator commands, real one-level-deep references, and a required handoff checklist. Its only real gaps are a missing failure-handling loop for validator errors and some duplicated boundary language and inline dated version facts that cost tokens.

DimensionReasoningScore

Conciseness

The body uses routing tables, tight bullet lists, and copy-ready commands with no padding or explanation of concepts Claude already knows, so it sits at anchor 4 ('efficient; minor instances that could be trimmed'). Not 5 because boundary language repeats across sections (e.g. 'Never generate findings, impressions, diagnoses...' in Diagnostic scaffolds restates the Non-Negotiable Boundary list) and dated version facts (CONSORT 2025, E2D(R1) adopted 15 September 2025, E6(R3) adopted 16 June 2026) appear inline rather than consolidated in a versions/deprecated section; not 3 because the repetition is limited and every section carries non-obvious constraints.

4 / 5

Actionability

Every script invocation is fully executable as written, e.g. 'PYTHONDONTWRITEBYTECODE=1 python3 scripts/generate_report_template.py --type case-report --output ./case-report-draft.json' and 'PYTHONDONTWRITEBYTECODE=1 python3 scripts/format_adverse_events.py ./aggregate-ae.csv --metadata ./safety-aggregate.json --output ./aggregate-ae-table.md', with concrete template paths (assets/provenance_manifest_template.json) and explicit per-field population rules ('Replace null only when a verified fact ID supports the field'). This matches anchor 5 — copy-paste ready commands covering the common cases; all 15 assets, 11 references, and 8 scripts referenced in the body exist on disk.

5 / 5

Workflow Clarity

A clear 5-step sequence (source-fact manifest → generate template → populate verified fields only → run deterministic checks → apply qualified review) sits behind a 7-condition Input Gate, with an explicit validation stage (step 4's six validators) and a required Final Handoff checklist — matching anchor 4 ('clear sequence with most checkpoints present'). Not 5: there is no feedback loop telling Claude what to do when a validator fails (fix which field, re-run which check), only the boundary-crossing case ('stop the unsafe portion, offer a blank template') is spelled out; not 3 because checkpoints are explicit and gating ('Proceed only when all conditions are true'), not implicit.

4 / 5

Progressive Disclosure

The body is a genuine overview: a routing table that maps each artifact to its guidance file, short sections that each end by pointing to exactly one clearly-named reference ('Read references/privacy_and_deidentification.md', 'Use assets/case_report_template.json and references/case_report_guidelines.md'), plus a consolidated Assets and References listing where every named file exists in the bundle. References are one level deep (references/README.md is itself a file map, not a chain), matching anchor 5 ('clear overview with well-signaled one-level-deep references; easy navigation').

5 / 5

Total

18

/

20

Passed

Description

75%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 precise, well-scoped description that names its actions, artifact types, input classes, and review requirement in third person without padding. Its main limitation is that the 'when' is expressed as an input restriction rather than natural task-trigger phrasing, and a few common synonyms (CSR, medical writing, manuscript) are missing.

Suggestions

Add an explicit task-trigger clause, e.g. 'Use when asked to draft or check a clinical case report, CSR, CONSORT/SPIRIT manuscript, or aggregate safety table' — this would raise completeness from 4 to 5.

Include common user phrasings as trigger terms: 'clinical study report', 'CSR', 'medical writing', 'manuscript', 'CONSORT/SPIRIT checklist' to broaden natural keyword coverage.

Slightly reduce the boundary qualifiers in favor of one artifact-type synonym list; the safety constraints are already enforced in the body and dilute the description's triggering signal.

DimensionReasoningScore

Specificity

Quotes: 'Create safety-bounded draft structures and run local deterministic checks' plus enumeration of 'clinical case, diagnostic, trial, safety, and aggregate research reports' — two concrete, verifiable actions (structure creation, deterministic checking) applied across five named artifact types. It falls between anchor 3 ('names domain and 1-2 concrete actions, but not comprehensive') and anchor 4 ('lists several specific actions'); the action list is short but object coverage is comprehensive, so 4 fits better than 3, while 5 would require more distinct actions than the two stated.

4 / 5

Completeness

The 'what' is explicit (create draft structures, run deterministic checks per report family). The 'when' exists as 'Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests' — an explicit use-condition clause, so it does not hit the 'missing Use when... caps at 3' rule, but it states input preconditions rather than task triggers ('use when asked to draft a case report or CSR'), so it matches anchor 4 ('both what and when; when could be more explicit or specific') rather than the concrete trigger phrasing of anchor 5.

4 / 5

Trigger Term Quality

Natural terms present: 'clinical case', 'diagnostic', 'trial', 'safety', 'aggregate research reports', 'synthetic, de-identified, or aggregate inputs', 'deterministic checks'. Matches anchor 4 ('good keyword coverage; a few natural terms missing') — users would plausibly say 'clinical case report' or 'trial results report', but common phrasings like 'medical writing', 'clinical study report', 'CSR', 'CONSORT', or 'manuscript' are absent, which keeps it below the comprehensive synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

The niche is sharply defined — clinical/regulatory report drafting restricted to synthetic, de-identified, or aggregate data — making wrong-skill triggering unlikely except for closely related medical-writing or document skills. This is anchor 4 ('mostly distinct; minor overlap risk with closely related skills'): the generic word 'reports' and the phrase 'aggregate research reports' could collide with general report-writing skills, preventing the minimal-conflict profile of anchor 5.

4 / 5

Total

16

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/claude-scientific-writer
Reviewed

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