research-lookup

Compile current scholarly evidence for a scientific manuscript or research brief. Use when the user explicitly asks to gather literature, references, background evidence, competing findings, or a manuscript research packet. Uses Parallel Search by default, Parallel Extract for source verification, Parallel Research for explicitly deep/exhaustive work, optional explicit Parallel Chat, and optional Perplexity only when requested or allowed as a failure fallback.

Quality

88%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Low

Low-risk findings worth noting

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with a clear, validated multi-step workflow, but it is verbose from repeated backend-selection explanation and monolithic rather than progressively split into reference files. Tightening redundancy and offloading detail into reference docs would raise the two score-2 dimensions.

Suggestions

Consolidate backend selection into the routing table once and have each backend section reference it, removing the repeated 'never selected by automatic routing' / 'preserved as an alternative' restatements.

Move the detailed output-compatibility, failure-handling, and per-backend flag reference material into a reference file (e.g. references/backends.md) linked from a concise overview, so the SKILL.md body stays a lean entry point.

Add an explicit validate-then-retry checkpoint to the batch-mode section (e.g. inspect each query's result envelope and re-run failed queries) to strengthen the one workflow area that currently only isolates failures.

Dimension	Reasoning	Score
Conciseness	The body avoids explaining concepts Claude already knows and is command-dense, but backend-selection logic is repeated across the routing table, the 'Important compatibility behavior' bullets, and five separate backend sections, so it could be tightened rather than earning the lean score-3 anchor.	2 / 3
Actionability	Provides fully executable, copy-paste-ready bash commands with concrete flags, a complete --context-file JSON example, and inspection commands like 'parallel-cli research processors --json', matching the score-3 anchor rather than the pseudocode score-2 case.	3 / 3
Workflow Clarity	The 'Recommended manuscript workflow' is a clearly sequenced 5-step process with an explicit verification step (Parallel Extract source verification), a shortfall-to-refine-search feedback loop, and a 10-item reference-quality checklist, satisfying the score-3 anchor with validation checkpoints.	3 / 3
Progressive Disclosure	Sections are well-organized and the referenced scripts (research_lookup.py, manuscript_packet.py) are real bundle files, but the ~320-line body is largely a monolithic inline document with detailed routing, failure, and output-compatibility content that could live in one-level-deep reference files, fitting the score-2 anchor rather than the well-split score-3 case.	2 / 3
	Total	10 / 12 Passed

Description

100%

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 strong: third-person, concrete actions, natural trigger terms, and an explicit 'Use when' clause covering both what and when. Its only minor weakness is verbosity in the backend-routing sentence, but no description-rubric dimension maps to conciseness.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions in third person ('Compile current scholarly evidence for a scientific manuscript or research brief', 'gather literature, references, background evidence, competing findings'), matching the score-3 anchor rather than the single-action score-2 example.	3 / 3
Completeness	Explicitly answers both what ('Compile current scholarly evidence for a scientific manuscript or research brief') and when ('Use when the user explicitly asks to gather literature, references, background evidence, competing findings, or a manuscript research packet'), with an explicit 'Use when' clause that clears the score-2 cap.	3 / 3
Trigger Term Quality	Covers natural user phrasing ('literature', 'references', 'background evidence', 'competing findings', 'manuscript research packet') that a user would actually say, satisfying the good-coverage anchor; it is not the score-2 case because common variations are present, not just one keyword.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear scientific-manuscript research niche with distinct triggers, unlikely to fire for unrelated skills; it is above score-2 because the trigger language is specific rather than a generic 'works with documents'.	3 / 3
	Total	12 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_field	'metadata' should map string keys to string values	Warning

	Total	15 / 16 Passed

Repository: K-Dense-AI/claude-scientific-writer
Path: .claude/skills/research-lookup/SKILL.md
Commit: 4717803

Reviewed: about 11 hours ago

Table of Contents

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