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multi-database-literature-collector

Collects candidate biomedical literature across multiple databases, adapts search logic by database, preserves source metadata, and organizes results into a structured, screening-ready candidate pool. Always use this skill when a user wants cross-database literature collection, search strategy construction, candidate paper aggregation, or first-pass evidence organization before deduplication, screening, layered reading, or review planning. Requires real and verifiable literature records only. Every formal literature item must include a real link and DOI when available; never fabricate citations, titles, authors, years, journals, abstracts, PMIDs, or DOIs. If a DOI is unavailable or cannot be verified, state that explicitly rather than inventing one.

66

Quality

80%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./awesome-med-research-skills/Evidence Insight/multi-database-literature-collector/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%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 body is a well-structured, actionably-specified instruction skill with excellent progressive disclosure via real reference modules and a clear sequenced workflow. Its main liability is structural redundancy — the fabrication/verification constraints are repeated across many sections, inflating token cost without adding guidance value.

Suggestions

Consolidate the repeated anti-fabrication and DOI/verification rules into a single authoritative 'Hard Rules' section and reference it once from Step 5 and the formatting standards instead of restating it five more times.

Add an explicit feedback loop to the verification step (e.g., 'if a candidate record cannot be verified, retry the lookup once; if still unverified, label DOI not available / not verified and continue') to strengthen the batch-collection validation path.

Trim or merge 'What This Skill Should Not Do' and 'Quality Standard' with the existing 'Hard Rules' and 'This skill must / must never' lists, since they restate the same constraints in different wording.

DimensionReasoningScore

Conciseness

The body assumes Claude's intelligence (no concept over-explanation), but the anti-fabrication/verification rules are restated roughly six times across 'This skill must never', Step 5's Hard verification rule, Required Formatting Standards, Hard Rules, and 'What This Skill Should Not Do', which is notable redundancy that could be tightened.

3 / 5

Actionability

Concrete, specific guidance is present throughout — mandatory output sections A–J with per-section requirements, an explicit minimum record field format, defined priority tiers, and hard verification rules — with the bulk of executable detail correctly delegated to reference modules rather than inlined.

4 / 5

Workflow Clarity

A clearly ordered 7-step workflow with validation at entry (input validation redirect-and-stop) and at record output (the hard verification rule for links/DOIs); the only gap is an explicit validate→fix→retry feedback loop, since failures are instructed to be reported rather than retried.

4 / 5

Progressive Disclosure

SKILL.md acts as a clear overview pointing to nine one-level-deep, well-labeled reference modules, all of which exist as real files; navigation is easy and content is appropriately split with no nested reference chains.

5 / 5

Total

16

/

20

Passed

Description

88%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 concrete, third-person, and explicitly pairs capabilities with 'Use when' trigger phrases, cleanly answering both what and when. The main weakness is the absence of concrete database names that users would naturally say, leaving trigger coverage just short of comprehensive.

Suggestions

Add concrete database names (e.g., 'PubMed, Google Scholar, Web of Science, Embase, Cochrane, ClinicalTrials.gov, preprint servers') to the trigger clause so users naming a specific source match this skill.

Tighten the anti-fabrication clauses in the description — they repeat constraints already implied by 'real and verifiable literature records only' and add length without adding trigger value.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Collects candidate biomedical literature across multiple databases, adapts search logic by database, preserves source metadata, and organizes results into a structured, screening-ready candidate pool' — covering the full collection pipeline comprehensively.

5 / 5

Completeness

Explicitly answers both what (first sentence of capabilities) and when ('Always use this skill when a user wants cross-database literature collection, search strategy construction, candidate paper aggregation, or first-pass evidence organization before deduplication, screening, layered reading, or review planning').

5 / 5

Trigger Term Quality

Strong natural terms ('cross-database literature collection', 'search strategy construction', 'candidate paper aggregation', 'first-pass evidence organization'), but specific database names a user would name (PubMed, Google Scholar, Web of Science) are absent from the description.

4 / 5

Distinctiveness Conflict Risk

The biomedical cross-database framing and explicit pipeline position ('before deduplication, screening, layered reading, or review planning') carve a clear niche, with only minor overlap risk against general literature-search or systematic-review skills.

4 / 5

Total

18

/

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

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