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mlava/scholar-sidekick-python

Resolve scholarly identifiers (DOI including shortDOI aliases, PMID, PMCID, ISBN, arXiv, ISSN, ADS bibcode, WHO IRIS URL) into formatted citations (10,000+ CSL styles) and bibliography exports (BibTeX, RIS, EndNote, CSV…), verify a claimed citation against the record its identifier actually resolves to, and audit a reference list of any length for fabricated and retracted entries — from Python via the `scholar-sidekick` package. Wraps the public REST API; needs Python ≥3.9 but no API key for the free tier. Use when the agent runs Python (a script, a notebook, a data pipeline) and the user wants to cite, export, verify, or audit references by DOI, PMID, PMCID, ISBN, arXiv ID, ISSN, ADS bibcode, or WHO IRIS URL — especially a bibliography longer than 25 entries, which this client chunks and paces automatically.

76

Quality

96%

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SecuritybySnyk

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Overview
Quality
Evals
Security
Files

Quality

Content

96%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 dense, executable reference for a thin client library: copy-paste code for every method, explicit error-recovery for the batch audit, and a self-test section. The structure is strong; the only gap is that all reference material (Errors table, Pitfalls, auth options) is inlined rather than split into a separate file.

Suggestions

Move the Errors exception table, the Authentication/limits options, and the Pitfalls list into a separate REFERENCE.md linked from a short 'Reference' section, keeping SKILL.md as a lean overview with the procedure examples.

Add a one-line 'Quick start' at the top (install + one format() call) so the most common path is reachable before the API surface table.

DimensionReasoningScore

Conciseness

Lean and skill-specific throughout — it documents method signatures, verdict semantics, strict claim-dict rules, and pitfalls Claude would not already know, while assuming Python competence and never explaining what a DOI or library is.

5 / 5

Actionability

Fully executable, copy-paste-ready examples cover every common case — verify(), audit_bibliography() with progress, format()/export(), the error-handling try/except, async/context-manager usage, and a health-check self-test.

5 / 5

Workflow Clarity

The batch audit operation has an explicit feedback loop — failed chunks are recorded not raised, `report.complete` is checked, `report.errors` iterated, and `stop_on_error=True` is offered — plus a dedicated Verification section with a mismatch assertion, so the batch-validation cap does not apply.

5 / 5

Progressive Disclosure

Well-organized with clear section headers (When to Use, API surface, Procedure, Errors, Pitfalls, Verification) and good navigation, but everything lives inline in one ~230-line file with no one-level-deep references to split out the detailed reference material.

4 / 5

Total

19

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20

Passed

Description

100%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.

An information-dense, third-person description that concretely states the package's actions, gives explicit Python-scoped use-when triggers with synonyms and identifier types, and is clearly distinguishable from sibling-transport skills. Its only weakness is density — the field is long, though every clause carries information rather than padding.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Resolve scholarly identifiers ... into formatted citations', 'bibliography exports', 'verify a claimed citation against the record its identifier actually resolves to', and 'audit a reference list ... for fabricated and retracted entries' — with comprehensive coverage of the package's capabilities.

5 / 5

Completeness

Explicitly answers both what (resolve/format/export/verify/audit) and when ('Use when the agent runs Python ... and the user wants to cite, export, verify, or audit references by ... — especially a bibliography longer than 25 entries') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage with synonyms — 'cite, export, verify, or audit references', 'bibliography', 'reference list' — plus the full set of identifier names (DOI, PMID, PMCID, ISBN, arXiv ID, ISSN, ADS bibcode, WHO IRIS URL) a user would actually name.

5 / 5

Distinctiveness Conflict Risk

Clear niche — scholarly citation verification/audit 'from Python via the scholar-sidekick package' — with a Python-scoped trigger that distinguishes it from the api/cli/mcp sibling skills, giving minimal conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

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

Warning

Total

13

/

16

Passed

Reviewed

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