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.

Quality

93%

Does it follow best practices?

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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.

The body is well-structured and highly actionable, with concrete commands, a clear workflow, and properly signaled references to real bundle files. The only slack is some duplication of the distinctiveness framing and a long inlined forcing-question library.

Suggestions

Collapse the 'Distinct from' table or the frontmatter distinction so the neighbor comparison lives in one place, saving tokens.

Add an explicit validate→retry checkpoint in the main Workflow (e.g. 'if saturation_planner flags the method as low-confidence, re-frame the goal before proceeding') to reach the top workflow_clarity anchor.

Consider moving the forcing-question library into a references file and keeping a one-line pointer, since it is the longest inlined section.

Dimension	Reasoning	Score
Conciseness	The body is dense and largely assumes Claude's competence (no explanation of what research is), but the 'Distinct from' table duplicates the frontmatter distinction and the forcing-question library is fairly long, so minor trimming is possible.	4 / 5
Actionability	Provides copy-paste-ready, fully executable commands for every tool — e.g. 'study_designer.py --goal {discovery\|evaluative\|validation} --stage ... --profile ...', 'saturation_planner.py --method {usability\|thematic\|evaluative-coverage} --segments N', and '--sample' quick examples covering the common cases.	5 / 5
Workflow Clarity	A clear five-step sequence (Frame → Pick method → Size → Synthesize → File) with checkpoints (honor the redirect, record the confidence label, treat ANECDOTE flags as signals), but no explicit validate→fix→retry feedback loop in the main workflow, so it sits just below the top anchor.	4 / 5
Progressive Disclosure	Clean overview with one-level-deep, clearly signaled references to verified bundle files (three references/*.md with one-line descriptions, scripts in a table, assets/research_plan_template.md), all of which exist on disk.	5 / 5
	Total	18 / 20 Passed

Description

100%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.

The description is third-person, specific, and comprehensive: it states what the skill does, when to use it, and how it differs from neighboring skills. It is dense but every clause earns its place.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — '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' — giving comprehensive, specific coverage.	5 / 5
Completeness	Explicitly answers both: what ('planning and synthesizing product/user research ... selecting the right method ... computing ... saturation ... synthesizing coded observations') and when (opens with 'Use when planning and synthesizing ...'), with concrete trigger phrasing.	5 / 5
Trigger Term Quality	Covers natural user-facing terms and synonyms — 'product/user research', 'generative interviews', 'usability test', 'concept test', 'validation', 'saturation/sample size', 'coded observations', 'anecdotes' — broadly matching how a user would phrase the need.	5 / 5
Distinctiveness Conflict Risk	States a clear niche — 'the research-ops method + insight-repository layer' — and explicitly distinguishes it from ux-researcher-designer, product-discovery, and experiment-designer, minimizing conflict risk.	5 / 5
	Total	20 / 20 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: research-ops/skills/product-research/SKILL.md
Commit: 19392f7

Reviewed: ~1 hour ago

Table of Contents

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