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market-research-reports

Build evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios. Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds.

77

Quality

96%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

92%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-structured, actionable skill body with a complete sequenced workflow, explicit validation checkpoints via the release gate and per-step scripts, and clean one-level-deep progressive disclosure. Conciseness is strong but not maximal given the domain's enumerative detail.

DimensionReasoningScore

Conciseness

Dense, task-oriented, and free of padding that explains concepts Claude already knows; minor stretches (e.g., the enumerated source-ledger and research-contract fields) could be tightened, so it sits above the midpoint but not fully lean.

4 / 5

Actionability

Copy-paste-ready CLI invocations appear at every workflow step (e.g., 'python3 scripts/validate_evidence_ledger.py data/source_ledger.csv'), plus explicit sizing and SAM/SOM formulas — fully executable guidance covering the common cases.

5 / 5

Workflow Clarity

A clearly sequenced 10-step workflow is closed by an explicit 'Release gate' validation checklist, with per-step validation scripts (validate_evidence_ledger, audit_claim_citations, check_unit_consistency) providing the validate-fix-retry feedback loop.

5 / 5

Progressive Disclosure

The body is an overview that signals one-level-deep references inline and indexes them in a 'Bundled resources' section; every referenced path under references/, assets/, and scripts/ was verified to exist, and content is appropriately split across files.

5 / 5

Total

19

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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.

A highly specific, well-triggered description that cleanly answers what the skill does and when to invoke it, with strong natural keywords and a distinct niche. No vague fluff or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions covering the domain comprehensively: 'Build evidence-traceable market research reports', 'assumption-driven market sizing or forecast scenarios', 'TAM/SAM/SOM reconciliation', 'forecast sensitivity', 'auditable report scaffolds'.

5 / 5

Completeness

Explicitly answers both 'what' (Build evidence-traceable market research reports...) and 'when' ('Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds').

5 / 5

Trigger Term Quality

Comprehensive natural terms a user would actually say — 'market research reports', 'market sizing', 'forecast scenarios', 'competitive landscapes', 'TAM/SAM/SOM', 'forecast sensitivity' — with relevant synonyms.

5 / 5

Distinctiveness Conflict Risk

A clear niche — evidence-traceable market research with TAM/SAM/SOM reconciliation and forecast sensitivity — with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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