market-research-report-generator

Generates professional market research reports by analyzing business intent, decision levels, and conducting multi-source data retrieval (Web, PubMed, Clinical Trials).

Quality

56%

Does it follow best practices?

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Securityby

Low

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tessl review fix ./scientific-skills/Other/market-research-report-generator/SKILL.md

Quality

Content

60%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 skill body has a clear, well-sequenced workflow with executable commands and properly signaled one-level-deep references to real bundle files. Its main weakness is verbosity: a large share of the content is generic templated boilerplate that does not add skill-specific value.

Suggestions

Remove or drastically condense the generic boilerplate sections ('When to Use', 'Required Inputs', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling') that merely restate skill-shell conventions, keeping only what is specific to market research reports.

Delete the redundant 'Implementation Details / See ## Workflow above' duplication and merge any unique points back into the Workflow section.

Add an inline validation checkpoint inside the Data Aggregation step (e.g. cross-reference then re-query on conflict) to turn the separated validation notes into a true validate→fix→retry loop.

Dimension	Reasoning	Score
Conciseness	Roughly half the body is generic templated boilerplate ('When to Use', 'Required Inputs', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling') that adds no skill-specific value and a redundant 'Implementation Details / See ## Workflow above', making it noticeably verbose with several padded sections.	2 / 5
Actionability	Provides concrete executable commands (`python scripts/research_orchestrator.py --help` and the clinical search invocation with a JSON array argument) plus a concrete 5-step workflow, with only minor gaps in the higher-level steps like report drafting.	4 / 5
Workflow Clarity	A clearly numbered 5-step workflow (Intent Analysis → Question Generation → Data Collection → Aggregation → Report Generation) is paired with validation guidance (Quality Rules, Quick Validation, Failure Handling); checkpoints exist though they sit somewhat apart from the main flow rather than as inline validate→fix→retry loops.	4 / 5
Progressive Disclosure	Detail is split into real one-level-deep bundle files that are clearly signaled and verified to exist (`references/intent_classification.md`, `references/decision_level.md`, `scripts/research_orchestrator.py`), with good navigation, though generic inlined boilerplate dilutes the overview.	4 / 5
	Total	14 / 20 Passed

Description

53%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 gives a clear, third-person statement of what the skill does and lists concrete data sources, but it omits any explicit 'when to use it' trigger guidance, capping completeness. Trigger-term coverage is decent but not comprehensive.

Suggestions

Add an explicit 'Use when...' clause naming concrete triggers, e.g. 'Use when the user requests a market research report, competitive landscape analysis, or industry/clinical data synthesis.'

Broaden trigger terms with common synonyms users actually say ('competitive analysis', 'industry report', 'market sizing', 'due diligence report').

Make the actions more concrete ('produce a Markdown report with Executive Summary, Competitive Landscape, and Strategic Recommendations sections') instead of abstract 'analyzing'.

Dimension	Reasoning	Score
Specificity	Names the domain (market research reports) and several actions ('analyzing business intent, decision levels' and 'conducting multi-source data retrieval'), but the actions are somewhat abstract rather than fully concrete, matching the '1-2 concrete actions, not comprehensive' anchor.	3 / 5
Completeness	The description clearly states what the skill does ('Generates professional market research reports...') but has no 'Use when...' or equivalent explicit 'when' clause, so per the cap it cannot exceed 3.	3 / 5
Trigger Term Quality	Contains relevant natural terms like 'market research reports', 'PubMed', and 'Clinical Trials', but misses common variations/synonyms (e.g. 'competitive analysis', 'industry report') and lacks a natural trigger phrasing.	3 / 5
Distinctiveness Conflict Risk	The pairing of market-research report generation with clinical/medical databases (PubMed, Clinical Trials) carves a fairly distinct niche with only minor overlap risk against general research skills.	4 / 5
	Total	13 / 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: aipoch/medical-research-skills
Path: scientific-skills/Other/market-research-report-generator/SKILL.md
Commit: 63c61d3

Reviewed: 11 hours ago

Table of Contents

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