fda-database

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

Quality

80%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

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.

The content is well-structured and highly actionable, with executable examples and clean progressive disclosure to verified bundle files. The main improvement is making batch/bulk operation validation feedback loops explicit rather than implied.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with real executable code and limited padding, though a few prose sections (Overview, When to Use, Best Practices) could be tightened slightly.	4 / 5
Actionability	Provides fully executable, copy-paste-ready Python examples across the common cases (queries, counts, pagination, error handling, multi-step analysis patterns).	5 / 5
Workflow Clarity	Multi-step patterns (safety profile, temporal trends, comparative analysis) are well sequenced with error-handling and pagination checkpoints, though validation feedback loops for batch operations are implicit rather than explicit.	4 / 5
Progressive Disclosure	SKILL.md serves as a clear overview with well-signaled one-level-deep references to real bundle files (references/.md and scripts/.py verified present), each category pointing to its dedicated reference.	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.

The description is specific and distinctive, clearly scoping the FDA-regulatory-data niche with concrete capabilities, but it lacks an explicit 'Use when...' trigger clause that would tell Claude when to invoke it. Adding a trigger-when clause would lift completeness and trigger-term quality.

Suggestions

Append a 'Use when...' clause naming concrete user triggers, e.g. 'Use when researching FDA drug safety, adverse events, device recalls, 510(k)/PMA submissions, or UNII substance lookup.'

Add common synonyms/file extensions or user-phrasings (e.g. 'FDA recalls', 'drug adverse-event reports') to broaden natural trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions across domains (query drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII)), giving comprehensive coverage of capabilities.	5 / 5
Completeness	Has a clear 'what' (query openFDA API for listed data types) but no explicit 'when' / 'Use when...' trigger clause, which caps completeness at 3 per the rubric guideline.	3 / 5
Trigger Term Quality	Includes natural keywords like 'drugs', 'devices', 'adverse events', and 'recalls' that users would say, but misses common synonyms and the explicit 'Use when...' phrasing.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear FDA-regulatory-data niche with distinct triggers (openFDA, 510k, PMA, UNII) and minimal risk of overlapping with unrelated skills.	5 / 5
	Total	17 / 20 Passed

Validation

81%

Warnings & errors only

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

Validation — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (518 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/databases/fda-database/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day ago

Table of Contents

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