CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

75

Quality

93%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is market-research-reports in K-Dense-AI/scientific-agent-skills

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 dense, well-structured body that assumes competence, leads with executable commands and concrete formulas, and uses a clear 10-step workflow backed by validation scripts and a release-gate checklist. Main gaps are the absence of explicit per-step fix-and-retry feedback loops and some long bulleted lists that could be tightened or offloaded to references.

Suggestions

Add explicit feedback-loop language at each validation step (e.g., 'If validate_evidence_ledger.py reports errors, fix the ledger and re-run until clean') to lift workflow_clarity from 4 to 5.

Move the longer enumerations (research-contract clarifications, source-ledger field list, measurement guardrails) into the existing references and keep only the operative subset inline, to improve conciseness.

Cross-link each workflow step to its named reference (e.g., step 5 → data_analysis_patterns.md, step 8 → methods_and_ethics.md) in-line rather than only at the end, to make navigation even more direct.

DimensionReasoningScore

Conciseness

Efficient and assumes Claude's competence — it never explains concepts Claude already knows (no 'what is TAM' or 'what is HHI') — but the ~325-line body carries several extensive bulleted lists (research contract, source-ledger fields, measurement guardrails) that could be trimmed or moved to references, keeping it just below the 'every token earns its place' 5 anchor.

4 / 5

Actionability

Provides copy-paste-ready, executable commands covering the common cases (validate_evidence_ledger.py, audit_claim_citations.py, calculate_market_sizing.py, forecast_sensitivity.py, validate_competitor_matrix.py, check_unit_consistency.py, generate_report_scaffold.py) plus concrete formulas (TAM_top, TAM_bottom, SAM_s, SOM_s) and specific ID conventions (S-001, C-001), matching the 'fully executable, copy-paste ready' anchor.

5 / 5

Workflow Clarity

A clearly sequenced 10-step workflow with explicit validation checkpoints (the validate scripts) and a final 'Release gate' checklist; it stays at 4 rather than 5 because the per-step 'validate → fix → re-validate' feedback loops are implied by the gate rather than spelled out as in the 5 anchor, and validation is present so the destructive/batch cap of 3 does not apply.

4 / 5

Progressive Disclosure

The body is a clear overview pointing to well-signaled, one-level-deep references (references/*.md), templates (assets/*), and tools (scripts/*) — all verified to exist as real files — with a 'Bundled resources' section giving one-line descriptions for easy navigation and no nested reference chains, matching the 5 anchor.

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.

A strong description that follows the canonical 'action + Use for…' structure, lists concrete domain-specific capabilities, and provides explicit, distinctive trigger terms. Voice is third-person/imperative ('Build…') with no first/second-person phrasing, so no specificity penalty applies.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions with comprehensive coverage — 'Build evidence-traceable market research reports', 'assumption-driven market sizing or forecast scenarios', 'TAM/SAM/SOM reconciliation', 'forecast sensitivity', and 'auditable report scaffolds' — matching the comprehensive-coverage anchor rather than the 4 anchor's 'minor gaps'.

5 / 5

Completeness

Explicitly answers both what ('Build evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios') and when ('Use for market definition, … TAM/SAM/SOM reconciliation, forecast sensitivity, …') with concrete trigger phrases, matching the 5 anchor and exceeding the 'Use when' cap of 3 since an explicit trigger clause is present.

5 / 5

Trigger Term Quality

Covers the natural terms a user would actually say — 'market research reports', 'market sizing', 'forecast scenarios', 'competitive landscapes', and 'TAM/SAM/SOM' — including overlapping synonyms; the file-extension criterion is inapplicable to this non-file-processing domain, so it does not reduce the score.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (auditable market research, TAM/SAM/SOM, forecast sensitivity) with distinct, specialized triggers that would not plausibly fire for unrelated skills, matching the 'clear niche with minimal conflict risk' anchor.

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/claude-scientific-writer
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.