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research

Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research, deepread for evidence-first reading of supplied documents) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Use when the user makes any research request that doesn't obviously match a more-specific specialist skill (e.g., "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]"). Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.

68

Quality

83%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is research in alirezarezvani/claude-skills

SKILL.md
Quality
Evals
Security

Quality

Content

70%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 well-structured with a clear phased workflow, explicit validation, and concrete inline code. Its weaknesses are repetition of the routing-transparency message across sections and a broken progressive-disclosure structure where referenced scripts and reference docs are cited as required but not actually shipped.

Suggestions

Ship the referenced bundle files — scripts/classifier.py, routing_transparency_logger.py, fallback_decomposer.py and references/hybrid_router_architecture.md, deterministic_classification_canon.md, fallback_workflow_canon.md — since they are cited as required but absent, breaking progressive disclosure and making the 'Use it; don't re-implement' instruction unactionable.

Consolidate the 'never delegates silently' / routing-transparency message, which is restated in Hybrid Architecture, Agent Integrity Rules, the Routing Transparency Protocol, and Anti-Patterns, into a single section to tighten conciseness.

Move the full SIGNALS map and scoring algorithm into scripts/classifier.py and keep only a brief summary in SKILL.md, so the inline content does not duplicate the script the body already directs Claude to use.

DimensionReasoningScore

Conciseness

Mostly efficient and operational, but the routing-transparency / 'never delegate silently' message is restated across Hybrid Architecture, Agent Integrity Rules, the Routing Transparency Protocol, and Anti-Patterns, and the Failure Modes and Anti-Patterns sections overlap.

3 / 5

Actionability

Provides concrete executable inline Python for the SIGNALS map and scoring algorithm plus specific script invocations (e.g. 'scripts/classifier.py --question "..." --output json'); the minor gap is that the referenced script files are absent, though their logic is reproduced inline.

4 / 5

Workflow Clarity

A clearly phased sequence (intake Q1–Q4 with skip/stop conditions, deterministic classification, 8-step fallback) with explicit validation and feedback loops: confirm response before next call, retry policy, 3-consecutive-failure stop+alert, and a three-count audit log.

5 / 5

Progressive Disclosure

Well-sectioned with clearly signaled one-level-deep reference pointers, but the referenced files (scripts/*.py and references/*.md) are absent from the empty bundle, and the full SIGNALS map plus algorithm are inlined rather than living in the script the body tells you to use.

3 / 5

Total

15

/

20

Passed

Description

96%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, complete description that concretely states capabilities, gives comprehensive natural-language triggers, and explicitly pairs a 'what' with a 'when'. Its only soft spot is distinctiveness, since the generic research triggers overlap with the specialist skills it routes between.

DimensionReasoningScore

Specificity

Lists multiple concrete actions comprehensively: deterministic classification, delegation to seven named specialists, a plan-decompose-search-synthesize-cite fallback, routing transparency, and markdown/.docx output with citations and audit log.

5 / 5

Completeness

Explicitly answers both what (hybrid router that delegates or runs fallback; outputs briefing or .docx with citations and audit log) and when (a 'Use when…' clause with concrete trigger phrases for non-specialist-matched research requests).

5 / 5

Trigger Term Quality

Covers natural phrases users actually say — 'research [topic]', 'look into [topic]', 'what do we know about [topic]', 'investigate [topic]', 'find me information on [topic]', 'do some research on [topic]', 'I need to understand [topic]' — with synonym coverage.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche as the default research entry point, but generic triggers like 'research [topic]' and 'look into [topic]' overlap with the specialists it routes to and with the similarly-named autoresearch-agent, leaving minor conflict risk.

4 / 5

Total

19

/

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

referenced_paths_exist

Referenced path issues: 9 missing

Warning

Total

15

/

16

Passed

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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