Name: market-research
Rating: 71.2 (1 reviews)
Author: alirezarezvani

market-research

Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.

Quality

88%

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

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.

A well-organized, actionable methodology skill body: clear Purpose, a numbered Workflow with reconciliation gates, a Scripts table, and disciplined Anti-patterns. It earns high marks for structure and concrete commands, with minor deductions for inlined forcing-question detail, missing explicit retry feedback loops, and referenced bundle files (references/, scripts/) that are not actually present in the skill.

Suggestions

Add an explicit validate->fix->retry feedback loop in the Workflow (e.g., 'if tam_divergence exceeds threshold, revise inputs and re-run market_sizer.py') to push workflow_clarity to 5.

Move the full forcing-question library (recommended answer + canon citation per item) into references/ and keep a short pointer in SKILL.md to improve conciseness and progressive_disclosure.

Either ship the referenced references/*.md and scripts/*.py files or mark them as to-be-created, since progressive_disclosure scoring depends on the bundle structure actually existing.

Dimension	Reasoning	Score
Conciseness	The body is largely efficient — tight Purpose, Workflow, Scripts table, and Anti-patterns sections with little concept padding — but the Distinct-from table, forcing-question library with full recommended-answer + canon-citation per item, and dual onboarding explanation add length beyond the minimum a competent Claude needs; it is not 5 because several sections could be trimmed without losing actionability.	4 / 5
Actionability	Provides concrete, runnable commands with real flags (--input, --profile, --sample, --output), a Scripts table mapping tool to purpose/profiles, and quick examples; it is not 5 because most examples reference --sample/--input files rather than showing complete copy-paste-ready inputs for the common cases (e.g., a sample market.json structure is not shown).	4 / 5
Workflow Clarity	The five-step Workflow is clearly sequenced with explicit reconciliation gates ('Reconcile the top-down/bottoms-up delta before quoting anything', 'Drop segments failing the substantiality/accessibility gate'), and the assumptions/anti-patterns surface validation concerns; it is not 5 because there is no explicit re-run/retry feedback loop (validate->fix->retry) shown as a checklist for the iterative sizing reconciliation.	4 / 5
Progressive Disclosure	Well-structured overview with one-level-deep references clearly signaled (References section lists three canon files, Scripts table points to three scripts, onboarding and autoresearch sections are isolated); however the referenced references/ and scripts/ files are not present in the bundle, so navigation cannot be fully verified, and the forcing-question library is inlined content that arguably belongs in a separate reference.	4 / 5
	Total	16 / 20 Passed

Description

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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 highly specific, well-triggered description that names concrete methods (top-down + bottoms-up TAM/SAM/SOM, FPC sample planning, Kotler criteria) and explicitly scopes when and for whom it applies while contrasting against neighboring skills. The third-person voice is mostly maintained, though a couple of second-person phrasings ('you need') and one conditional ('have you computed') slightly soften the voice consistency.

Suggestions

Tighten voice to consistent third person: 'sizing a market...' and 'planning a survey...' are good, but 'you need a defensible' / 'have you computed both ways' reads as second person and the rubric penalizes that in specificity.

Consider adding explicit file-type or artifact triggers (e.g., '.xlsx sizing model', 'survey.json') to match the tool's actual inputs and strengthen trigger_term_quality further.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete, specific actions — sizing TAM/SAM/SOM via top-down AND bottoms-up, planning survey sample size with finite-population correction and per-segment minimums, and scoring segments against Kotler's five named criteria — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Explicitly answers both 'what' (sizing/survey/segmentation methodology with named methods) and 'when' ('Use when doing upstream market-research methodology...'), with concrete trigger phrases and an explicit audience/decision moment; it is not below 5 because both halves are present and concrete, and not a partial case.	5 / 5
Trigger Term Quality	Captures natural analyst phrasing — 'sizing a market as TAM/SAM/SOM', 'planning a survey sample size', 'scoring candidate market segments' — alongside methodology synonyms and concrete trigger phrases a user would actually say when needing this skill.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear niche — upstream evidence-building methodology — and explicitly disambiguates from marketing-skill (campaign analytics/attribution/demand-gen) and distinguishes itself as methodology rather than live-campaign optimization, minimizing wrong-skill triggering.	5 / 5
	Total	20 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
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/market-research/SKILL.md
Commit: 19392f7

Reviewed: ~10 hours ago

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