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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.

68

Quality

83%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is research-lookup in K-Dense-AI/scientific-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 highly actionable with an exemplary validated workflow, but it is a monolithic document: backend variants, quality rules, and citation policy all live inline in SKILL.md instead of being progressively disclosed through reference files, and there is redundant restating of routing behavior plus time-sensitive version pins. Splitting the secondary backend sections into references would fix both the conciseness and disclosure weaknesses.

Suggestions

Move the secondary backend sections ('Explicit Parallel Chat', 'Optional Perplexity fallback', 'Output compatibility') into a single one-level-deep reference file (e.g. references/backends.md) and keep one-line pointers in SKILL.md, since the routing table already summarizes them.

Remove the duplicate routing bullets under 'Important compatibility behavior' — they restate the routing table and the dedicated backend sections.

Relocate the arXiv citation-fetching procedure to a short reference file or an 'old patterns' style appendix, and keep only the ready-to-use citation block inline, to cut time-sensitive and procedural tokens from the main body.

DimensionReasoningScore

Conciseness

The body is mostly efficient — commands, flag tables, and terse rules — but there is padding that could be trimmed: backend behavior is described three times (routing table, 'Important compatibility behavior' bullets, then dedicated sections), the 'Output compatibility' section enumerates result-envelope fields, and the closing 'Citing Scientific Agent Skills' section gives a long fetch-the-arXiv-record procedure. The pinned version ('parallel-web-tools[cli]==0.7.1', 'cli 0.7.1+') is time-sensitive and sits outside any deprecated/old-patterns section. Anchor 3 ('mostly efficient but includes some unnecessary explanation or could be tightened') fits; not 2 because there is no concept-explanation filler, not 4 because the repetition and version pinning are real excess tokens.

3 / 5

Actionability

Every workflow ships copy-paste-ready commands with full flag sets ('--academic --target-references 60 --context-file ... --packet-dir ... --json'), a concrete --context-file JSON example, an enumerated packet output manifest, setup/install commands, and executable inspection commands ('parallel-cli research processors --json'). Covers the common cases (default academic, deep research, chat, perplexity, fast lookup, batch). Matches anchor 5; anchor 4 would leave minor gaps, and none are apparent.

5 / 5

Workflow Clarity

The recommended manuscript workflow is a clearly numbered 5-step sequence with explicit validation checkpoints: coverage.json is inspected on shortfall ('inspect coverage.json; refine the question, date range, terminology, or domains. Do not lower quality merely to reach 60'), unverified records are flagged ('The coverage report will not count search-only records as verified'), a 10-rule reference-quality checklist governs the batch operation, and a dedicated 'Failure handling' section gives error-recovery loops per failure mode. Batch errors are isolated per query. Matches anchor 5 (explicit validation steps, feedback loops, checklists); the batch-operation cap of 3 does not apply because validation is present.

5 / 5

Progressive Disclosure

The bundle contains only executable scripts (scripts/research_lookup.py, scripts/manuscript_packet.py) referenced by working path — no reference or asset files exist, and no detail is offloaded to them. All guidance is inline in a ~336-line body: backend-specific sections ('Explicit Parallel Chat', 'Optional Perplexity fallback'), the reference-quality rules, and the citation-fetching procedure read like content that belongs in one-level-deep reference files. Anchor 3 ('some structure... content that should be separate is inline') fits; not 4 because nothing is actually split out to navigable reference files, not 2 because the section headers make it easy to navigate and the scripts are correctly invoked rather than inlined.

3 / 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.

A strong description that clearly states what the skill does, when to use it, and how backend routing behaves. The what/when pair is explicit with concrete trigger phrases, and the domain is distinct. The only gaps are a few missing natural synonyms and minor overlap with adjacent literature-gathering skills.

DimensionReasoningScore

Specificity

The description names multiple concrete actions with comprehensive coverage: 'Compile current scholarly evidence for a scientific manuscript or research brief', 'gather literature, references, background evidence, competing findings', and enumerates the concrete backend behaviors (Parallel Search 'by default', Parallel Extract 'for source verification', Parallel Research for 'deep/exhaustive work'). Anchor 5 fits best; anchor 4 ('minor gaps in coverage') is weaker because both the actions and their conditional routing are explicitly stated.

5 / 5

Completeness

It explicitly answers both questions: what ('Compile current scholarly evidence for a scientific manuscript or research brief', plus the backend toolchain) and when ('Use when the user explicitly asks to gather literature, references, background evidence, competing findings, or a manuscript research packet') with concrete trigger phrases. Matches the anchor-5 example structure exactly; anchor 4 would require the 'when' clause to be less explicit.

5 / 5

Trigger Term Quality

Good natural-term coverage: 'literature', 'references', 'background evidence', 'competing findings', 'manuscript research packet', 'scientific manuscript', 'research brief'. A few common synonyms users would naturally say are missing (e.g. 'citations', 'papers', 'sources', 'evidence search'), so it falls between anchors 4 and 5, closer to 4. Not 3 because the phrases present are the ones users actually say rather than generic jargon.

4 / 5

Distinctiveness Conflict Risk

Clear niche (manuscript evidence compilation) with distinct triggers like 'manuscript research packet' and 'competing findings', but phrases such as 'gather literature, references, background evidence' have minor overlap risk with a general literature-review or web-search skill. Anchor 4 ('mostly distinct; minor overlap risk with closely related skills') fits; not 5 because of that overlap, not 3 because the manuscript framing is far more specific than 'somewhat specific'.

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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/claude-scientific-writer
Reviewed

Table of Contents

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