jaspar-database

Query the JASPAR database for Transcription Factor (TF) binding profiles. Use when retrieving Position Frequency Matrices (PFMs) or Position Weight Matrices (PWMs) for specific TFs, resolving gene symbols to JASPAR Matrix IDs, or getting TF metadata. Supports multiple output formats (MEME, TRANSFAC, PFM, JASPAR, YAML).

Quality

84%

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Securityby

Low

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Quality

Content

76%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 highly actionable and reasonably concise, with clean structure and one-level-deep delegation to the bundled script. Its main gap is the absence of explicit error-recovery feedback loops for the batch/remote API operations.

Suggestions

Add an explicit error-recovery workflow (e.g., on API 500/429 or empty results: retry with backoff, verify the Matrix ID, fall back to infer_from_sequence) so batch/remote operations have a validate→fix→retry loop.

Reference the bundled references/citation.bib from the body (e.g., a Citations section) so all bundle files are discoverable.

Trim the opening paragraph that redefines JASPAR/PFMs, since the description frontmatter already conveys this.

Dimension	Reasoning	Score
Conciseness	The body is efficient and mostly assumes Claude's competence, with only minor over-explanation (e.g., restating what JASPAR stores as PFMs) that could be trimmed.	4 / 5
Actionability	Each operation has copy-paste-ready `uv run scripts/jaspar_api.py` commands with real arguments and flag examples covering the common cases.	5 / 5
Workflow Clarity	Operations are well sequenced and Core Rules enforce some safeguards (wrapper rate limiting, Matrix ID resolution, window chunking), but for batch/remote API work there is no explicit validate→fix→retry feedback loop or error-recovery checklist, which caps workflow clarity per the rubric.	3 / 5
Progressive Disclosure	SKILL.md stays an overview with API specifics delegated one level deep to the bundled `scripts/jaspar_api.py`; structure is clear, though `references/citation.bib` is not referenced from the body.	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.

The description is specific, complete, and clearly niched with explicit 'Use when' trigger guidance. It could only marginally improve by adding more lay-friendly synonyms alongside the technical terms.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions — retrieving PFMs/PWMs, resolving gene symbols to JASPAR Matrix IDs, getting TF metadata, and enumerating output formats (MEME, TRANSFAC, PFM, JASPAR, YAML) — giving comprehensive coverage of capabilities.	5 / 5
Completeness	It explicitly answers both 'what' (query JASPAR for TF binding profiles, with the listed operations) and 'when' via a concrete 'Use when retrieving...' trigger clause.	5 / 5
Trigger Term Quality	It includes strong domain triggers ('retrieving PFMs/PWMs', 'resolving gene symbols to JASPAR Matrix IDs', 'TF metadata') but is somewhat jargon-heavy and lacks the broader synonyms a less technical user might say, so it sits just below comprehensive.	4 / 5
Distinctiveness Conflict Risk	JASPAR TF binding profile queries occupy a clear niche with distinct triggers (PFMs, PWMs, Matrix IDs), making conflict with other skills minimal.	5 / 5
	Total	19 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: google-deepmind/science-skills
Path: skills/jaspar_database/SKILL.md
Commit: 6883275

Reviewed: 4 days ago

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