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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.

67

Quality

84%

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SecuritybySnyk

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

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 well-structured, dense with skill-specific guidance, and highly actionable on its face — concrete per-step commands, a deterministic-tools table, onboarding config, and an explicit anti-patterns section. Its main defect is bundle integrity: every referenced script, reference, and asset path is absent from the bundle, making the deferred-detail navigation dead and the commands unexecutable as shipped, with some repetition of the core recurrence rule as a secondary trim opportunity.

Suggestions

Ship the referenced bundle files (scripts/study_designer.py, scripts/saturation_planner.py, scripts/insight_synthesizer.py, scripts/onboard.py, scripts/ar_evaluator.py, assets/research_plan_template.md, and the three references/*.md files) or inline their essential content, so the cited paths resolve and the workflow commands execute.

State the insight-recurrence rule once canonically (e.g., in Purpose) and reference it from the tool description, anti-patterns, and forcing questions instead of restating it four times.

Add an explicit validation checkpoint to the Workflow's final step, e.g., verify synthesizer output tags against the atomic repository schema (and re-tag if mismatched) before filing insights.

DimensionReasoningScore

Conciseness

Dense and skill-specific throughout — the workflow, scripts table, onboarding, and forcing-question sections all carry non-obvious content (method-mapped flags, config scopes, canon citations). However, the core insight-recurrence rule is restated ~4 times ("a single quote is an anecdote", "flags any candidate below the source threshold as an ANECDOTE", "One participant is a signal to probe", "require recurrence across ≥3 sources"), and the intro largely restates the frontmatter description — minor trimming opportunities, matching level 4 rather than the every-token-earns-its-place level 5; well above level 3 since nothing explains concepts Claude already knows.

4 / 5

Actionability

Concrete, complete CLI invocations with all flags: "study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {...}", "saturation_planner.py --method {...} --segments N", "insight_synthesizer.py --input observations.json --min-sources 3", plus runnable quick examples and onboarding commands. Not 5 because the referenced scripts (scripts/study_designer.py, saturation_planner.py, insight_synthesizer.py, onboard.py, ar_evaluator.py) and assets/research_plan_template.md are not present in the bundle, so the copy-paste-ready commands cannot actually execute as shipped; not 3 because the guidance itself is specific, syntactically complete, and covers the common cases.

4 / 5

Workflow Clarity

A clearly sequenced 5-step workflow (frame → pick method → size → synthesize → file in repository) with a concrete command per step and useful checkpoints ("Honor the redirect if it routes to experiment-designer", "Record the confidence label and limits", "Treat ANECDOTE-flagged clusters as signals, not findings"). Not 5 because there is no explicit validate-output/recovery step (e.g., verifying repository tags before filing); not 3 because checkpoints are mostly present and the sequence has no gaps.

4 / 5

Progressive Disclosure

The body is well-sectioned with a dedicated References section giving one-line descriptions of three reference files, and references are one level deep and clearly signaled. However, none of the referenced bundle files exist — there is no scripts/, references/, or assets/ directory in the bundle — so every cited path (3 references, 5 scripts, 1 asset) is dead and navigation to the deferred detail is impossible. This sits between level 3 (structure present but organization compromised) and level 4; the dead references keep it at 3 rather than 4, while the clean internal signaling keeps it above 2.

3 / 5

Total

15

/

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: third-person voice, explicit Use-when triggers, enumerated concrete capabilities, and explicit disambiguation from three neighboring skills. The only gap is a handful of missing natural synonyms (e.g., "user testing", "research findings") that users might phrase differently.

DimensionReasoningScore

Specificity

Multiple specific concrete actions with comprehensive coverage: "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". Each capability is enumerated with its variations, matching the level-5 anchor; not 4 because coverage spans the skill's full scope with named method types rather than minor gaps.

5 / 5

Completeness

Explicitly answers both what and when: opens with "Use when planning and synthesizing product/user research..." giving concrete trigger situations (method selection, saturation/sample size, synthesis of coded observations), and the what is the three enumerated capabilities. Not 4 because the when-clause is explicit, specific, and loaded with trigger phrases rather than merely adequate.

5 / 5

Trigger Term Quality

Good keyword coverage: "product/user research", "usability test", "interviews", "sample size", "saturation", "synthesizing", "insights", "research repository". A few natural terms users would say are missing (e.g., "user testing", "usability study", "research findings", "discovery interviews"), so it fits the level-4 anchor rather than the comprehensive synonym coverage of level 5; clearly above level 3 since most relevant trigger phrases are present.

4 / 5

Distinctiveness Conflict Risk

Explicitly disambiguates: "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." Named neighbors with scope boundaries give a clear niche with minimal conflict risk, matching the level-5 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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 13 missing

Warning

Total

14

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

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

Reviewed

Table of Contents

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