CtrlK
BlogDocsLog inGet started
Tessl Logo

faers-pharmacovigilance-disproportionality-research-planner

Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious-case filtering, suspect-drug and concomitant-exclusion logic, reporting odds ratio analysis, comparator-drug benchmarking, cross-drug comparison, and cautious signal interpretation without causal overclaiming. Covers five study patterns (single-drug disproportionality workflow, multi-drug class comparison workflow, indication-stratified workflow, comparator-controlled signal screening workflow...

69

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 well-structured, navigable instruction skill with a clearly sequenced workflow, an explicit validation/feedback checkpoint, and excellent progressive disclosure into 8 reference files. Its chief weakness is conciseness: the same requirements are restated across the execution steps, the output-section spec, and the Hard Rules list.

Suggestions

Consolidate the redundancy between the 7 execution steps, the Section A–J output spec, and the 23 Hard Rules — fold each rule into its single canonical location and cross-reference rather than re-stating (e.g. the four-config, Section G, and Dataset Disclaimer rules each appear three times).

Tighten Step 7 and the Hard Rules so that mandatory sections are defined once (in Section A–J) and the Hard Rules list only captures cross-cutting constraints not already implied by the section spec.

Consider moving the long Section J element list and the Step 5 forbidden-claims enumeration into a reference file, keeping the body as a concise overview with one-level-deep links, to further reduce token load.

DimensionReasoningScore

Conciseness

The body avoids explaining concepts Claude already knows and is mostly procedural, but it repeats the same requirements three times — inside the 7 steps, again in the mandatory Section A–J output spec, and a third time in the 23 Hard Rules (e.g. four-config output, Section G mandatory, Dataset Disclaimer) — so it could be tightened considerably.

3 / 5

Actionability

Provides concrete decision tables (study patterns, workload configs), an input-validation formula, forbidden-claims list, and explicit dependency formulas, but the executable per-step detail (8-field template, module/method library) is deferred to reference files, leaving minor gaps in the body itself.

4 / 5

Workflow Clarity

Seven steps are explicitly ordered ("always run in order"), Step 5 is a mandatory dependency-consistency validation checkpoint with a clear revise-before-output feedback loop and checklist, and input validation has a redirect-and-stop rule — matching the top anchor for sequenced validation.

5 / 5

Progressive Disclosure

The body is an overview that links out to 8 real, one-level-deep reference files (study-patterns, workload-configurations, workflow-step-template, analysis-modules, method-library, figure-deliverable-plan, validation-evidence-hierarchy, literature-retrieval-and-citation), each clearly signaled with a descriptive "→" label, yielding easy navigation.

5 / 5

Total

17

/

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, highly specific description that clearly states both its function and its trigger conditions within a well-defined specialized niche. Its main weakness is moderate verbosity and a tail that appears truncated ("comparator-controlled signal screening workflow..."), plus some jargon-heavy trigger phrasing.

DimensionReasoningScore

Specificity

Names many concrete actions across the full pipeline — "MedDRA-based adverse-event extraction", "suspect-drug and concomitant-exclusion logic", "reporting odds ratio analysis", "comparator-drug benchmarking", "cross-drug comparison" — giving comprehensive coverage rather than generic verbs.

5 / 5

Completeness

Explicitly answers both what ("Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification") and when ("Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes solid natural terms ("FAERS", "pharmacovigilance", "safety signal study", "reporting odds ratio", "MedDRA"), but several listed phrases are technical jargon rather than user-spoken triggers and synonyms are absent, so it sits just below the top anchor.

4 / 5

Distinctiveness Conflict Risk

Occupies a narrow, specialized niche (FAERS disproportionality research design) with distinct triggers, making overlap with other skills minimal.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.