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product-research

Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.

72

Quality

90%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

A well-engineered skill body: executable commands with flags at every step, verified one-level-deep references, and embedded discipline checkpoints (redirects, confidence labels, anecdote flags). The main costs are repetition of the tool descriptions across three sections and a forcing-question library that duplicates anti-pattern and reference content.

Suggestions

Describe each tool once — keep the Scripts table as the canonical summary and let the Workflow steps reference them by name with their command line only, removing the triple description in Purpose/Workflow/Scripts.

Move the forcing-question library's recommended answers and canon citations into a reference file (or inline only the five questions, linking each to its canon entry in references/), since much of it restates the anti-patterns and reference content.

Add an explicit verification step at the end of the Workflow (e.g., re-run insight_synthesizer.py after recoding to confirm clusters clear the threshold, or audit that filed insights carry evidence and confidence tags), giving the sequence a closed feedback loop.

DimensionReasoningScore

Conciseness

Dense, non-pedantic, and assumes Claude's competence (no explaining what a usability test or thematic analysis is). Not a 5 because of trimmable redundancy: the three tools are described three times (Purpose bullets, Workflow steps, Scripts table), the 'Distinct from' table repeats the 'Do NOT use this skill to' list, and the forcing-question library largely restates anti-patterns and reference-file content. Above level 3, which would imply genuinely unnecessary explanation rather than structural repetition.

4 / 5

Actionability

Fully executable throughout: complete CLI invocations with flags ('study_designer.py --goal {discovery|evaluative|validation} --stage {...} --profile {...}', 'insight_synthesizer.py --input observations.json --min-sources 3'), onboarding and autoresearch commands, and a Quick examples section that states the expected behavior ('promotes "import-confusion" ... flags "wants-slack" as an ANECDOTE'). All referenced scripts exist with --help/--sample. Copy-paste ready, covering the common cases.

5 / 5

Workflow Clarity

A clear five-step sequence with explicit commands and real checkpoints ('Honor the redirect if it routes to experiment-designer', 'Record the confidence label and limits', 'Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship'). Not a 5: there is no explicit verify/recovery loop after synthesis or repository filing (e.g., re-run the synthesizer after recoding, or audit that insights were tagged to the atomic schema), so it sits at 'clear sequence with most checkpoints; minor validation gaps'.

4 / 5

Progressive Disclosure

SKILL.md functions as an overview: three clearly-scoped reference files (methods canon, sampling/saturation, repository/synthesis) verified to exist, each one level deep with no nested references and each paired with its script; details are appropriately split out (saturation math and citations live in references, not the body). The Scripts table and one-line reference annotations make navigation easy.

5 / 5

Total

18

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20

Passed

Description

92%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: it states three concrete capabilities, opens with an explicit 'Use when...' trigger clause, and preemptively disambiguates four neighboring skills. The only weakness is trigger phrasing that is somewhat more technical than the words a user would naturally say.

Suggestions

Add one or two plain-language trigger synonyms (e.g., 'user interviews' or 'how many participants to interview') so the description fires on naturally phrased requests, not just research-ops jargon like 'generative interviews' and 'saturation'.

DimensionReasoningScore

Specificity

Lists three concrete, comprehensive actions with named methods: 'selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation)', 'computing method-based saturation/sample size with an explicit confidence level', and 'synthesizing coded observations into insights while flagging single-source anecdotes'. Matches the level-5 anchor (multiple specific concrete actions, comprehensive coverage); below it there is no gap in action coverage to justify 4.

5 / 5

Completeness

Explicitly answers both: what ('selecting the right method... computing method-based saturation/sample size... synthesizing coded observations into insights') and when ('Use when planning and synthesizing product/user research...') with concrete trigger phrases. This matches the level-5 anchor exactly; level 4 would require a weaker or less explicit 'when' clause, which is not the case.

5 / 5

Trigger Term Quality

Good keyword coverage: 'product/user research', 'usability test', 'concept test', 'sample size', 'saturation', 'insights', 'anecdotes'. Not a 5 because the phrasing leans technical ('generative interviews', 'method-and-repository discipline') and omits plainer synonyms users naturally say, such as 'user interviews', 'discovery research', or 'how many participants'; clearly above the 'some relevant keywords but missing variations' anchor at 3.

4 / 5

Distinctiveness Conflict Risk

Establishes a clear niche ('this is the research-ops method + insight-repository layer') and explicitly disambiguates from product-team/ux-researcher-designer, product-team/product-discovery, product-team/experiment-designer, and market sizing. Minimal conflict risk; matches the level-5 'clear niche with distinct triggers' anchor.

5 / 5

Total

19

/

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.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

14

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

research-ops/skills/product-research/SKILL.md

Reviewed

Table of Contents

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