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literature-review

Find, verify, and synthesize scientific literature — from "what's the seminal paper for X" through full multi-source reviews. Covers grounding claims in real retrieved sources, avoiding fabricated citations, handling retractions, and calibrating confidence to evidence strength.

59

Quality

69%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./configs/microservice/bff-service/configs/agent-skills/claude-science/literature-review/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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-organized, concise, and gives concrete workflow guidance with validation, but its actionability and structure are undermined by the absence of the referenced kernel.py from the bundle, leaving the central executable component missing.

Suggestions

Ship the referenced kernel.py (with verify_dois, crossref_lookup, search_openalex, expand_citations, extract_dois, style_pass) in a scripts/ directory so the exec-based setup and helper calls actually work.

Re-express the retrieve → expand → synthesize → style_pass → save flow as a short numbered checklist with explicit validation checkpoints (verify DOIs, check retractions, run style_pass once) to lift workflow clarity to the top anchor.

Tighten the discursive analogy passages (e.g. 'a paragraph that hasn't been written yet') to recover tokens without losing the actionable diagnostic.

DimensionReasoningScore

Conciseness

Lean and efficient text that assumes Claude's competence — it never explains what a DOI, PDF, or citation graph is — with a few discursive passages (e.g. the annotated-bibliography analogy) that could be trimmed, sitting just below the score-5 'every token earns its place' anchor.

4 / 5

Actionability

Provides concrete helper names, an exact exec command, and a precise inline-citation format, but the repeatedly referenced 'kernel.py' is not present in the bundle (no scripts/ directory exists), so the core executable code the instructions depend on is missing — a key gap that leaves the guidance incomplete.

3 / 5

Workflow Clarity

A clear retrieve-then-write sequence runs through the body with validation baked in (verify_dois, retraction checks, a single style_pass lint before save), but it is presented as flowing prose rather than numbered steps with explicit checkpoints, placing it just below the score-5 anchor.

4 / 5

Progressive Disclosure

The body is well-sectioned and short (~55 lines) with no nested references, but it repeatedly signals a one-level reference to 'kernel.py' / 'this skill's directory' that does not exist in the bundle, so the primary referenced file is missing — a structural gap that prevents a higher score.

3 / 5

Total

14

/

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, distinct, and rich in concrete capabilities, but it omits an explicit 'Use when...' trigger clause, leaving the activation guidance only weakly implied and capping completeness.

Suggestions

Add an explicit 'Use when ...' clause naming natural trigger phrases (e.g. 'Use when the user asks for the seminal paper for a topic, a literature review, evidence on a question, or to compare methods across studies').

Broaden trigger-term coverage with synonyms users actually say — 'literature search', 'find papers', 'systematic review', 'survey the evidence' — alongside 'scientific literature' and 'seminal paper'.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Find, verify, and synthesize scientific literature', 'grounding claims in real retrieved sources', 'avoiding fabricated citations', 'handling retractions', 'calibrating confidence to evidence strength' — giving comprehensive coverage of what the skill does.

5 / 5

Completeness

The 'what' is clear and detailed, but there is no explicit 'Use when...' trigger clause; the 'when' is only weakly implied via the 'from ... through full multi-source reviews' phrasing, so per the rubric guideline completeness is capped at 3.

3 / 5

Trigger Term Quality

Includes natural user phrases like 'what's the seminal paper for X', 'multi-source reviews', 'scientific literature', 'citations', and 'retractions', but lacks the synonym/extension breadth (e.g. 'literature search', 'papers', 'systematic review') that the score-5 anchor requires.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche — retrieval-grounded scientific literature synthesis with retraction handling and confidence calibration — with distinct triggers and minimal overlap risk against other skills.

5 / 5

Total

17

/

20

Passed

Validation

87%

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

Validation14 / 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

Total

14

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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