Inputs
Provide these when prompted. The skill asks for anything missing before it runs.
Required
- A dataset of companies or profiles
Optional
- Dimensions of interest (industry, size, geography)
- A hypothesis to test
Agent Skill
Market Research
Analyze a dataset of companies or profiles to identify market segments and patterns.
Version 1.0 Updated June 2026 SKILL.md MIT 6 min read
Overview
What this skill does
A raw export of hundreds of companies or profiles hides its structure. Eyeballing it for patterns is slow and biased toward whatever is at the top.
This skill analyzes the whole dataset and reports the market structure: recurring characteristics, dominant categories, natural segments, gaps, and emerging patterns — along with the limits of what the data can support.
When to use it
Best used for
- Understanding a scraped market dataset
- Finding natural segments in a large list
- Spotting dominant and underserved categories
- Sizing where the opportunity concentrates
Know the limits
When not to use this skill
- The dataset is too small to show patterns
- You need statistically rigorous market sizing
- You want per-record actions (use Lead List Analysis)
Outputs
A market analysis with a consistent, inspectable schema.
market_segmentsNatural segments found in the data.
recurring_characteristicsTraits that appear repeatedly.
dominant_categoriesThe largest categories by count.
gapsUnderrepresented or missing areas.
emerging_patternsNotable trends or clusters.
research_limitationsWhat the data cannot support.
Example
Example
A pattern read on a small sample.
Input
Dataset: 200 SaaS companies with industry, size, region. (Sample shown.)
Goal: Where is the opportunity?Output
market_segments: SMB fintech; mid-market healthtech; enterprise infra
recurring_characteristics: Most are <200 employees; US-heavy
dominant_categories: Fintech (38%); healthtech (24%)
gaps: Few EU-based; little enterprise representation
emerging_patterns: Vertical SaaS clustering by industry
research_limitations: No revenue data; region skew in sourceThe data points to a US SMB/mid-market vertical-SaaS market with an EU and enterprise gap — useful for targeting, with the source skew flagged honestly.
Setup
How to use the skill
General steps first, then notes for specific clients where verified.
- 1Download the file using the button below, or copy the Markdown.
- 2Place it in a directory named after the skill (e.g. skill-name/).
- 3Make sure the filename stays exactly SKILL.md.
- 4Add any references or assets included with the package.
- 5Load the skill into a compatible agent and provide the required inputs.
Claude Code
- 1Create a folder for the skill and save SKILL.md inside it.
- 2Place the folder where your project's skills are discovered.
- 3Reference the skill when you want it applied to your data.
Other compatible clients
- 1Confirm the client supports the open Agent Skills format.
- 2Load the SKILL.md file as instructed by that client.
- 3If skills are not auto-loaded, paste the Markdown as instructions.
Source
Full SKILL.md source
Read the rendered skill or copy the complete Markdown. The download is generated from this exact source.
Version 1.0 SKILL.md ~2 KB MIT
Market Research
Purpose
Analyze a dataset of companies or profiles to identify market segments and patterns.
When to use this skill
- Understanding a scraped market dataset
- Finding natural segments in a large list
- Spotting dominant and underserved categories
- Sizing where the opportunity concentrates
When not to use this skill
- The dataset is too small to show patterns
- You need statistically rigorous market sizing
- You want per-record actions (use Lead List Analysis)
Required inputs
- A dataset of companies or profiles
Optional inputs
- Dimensions of interest (industry, size, geography)
- A hypothesis to test
Rules
- Analyze the whole dataset, not a convenient subset.
- Report counts and proportions where possible.
- Do not over-generalize beyond what the data supports.
- State source skew and limitations.
- Separate observed patterns from speculation.
Process
- Profile the dataset structure and fields.
- Identify recurring characteristics and categories.
- Derive natural segments.
- Identify gaps and emerging patterns.
- State limitations.
Output format
Return a market analysis with the following fields:
- market_segments
- recurring_characteristics
- dominant_categories
- gaps
- emerging_patterns
- research_limitations
Validation
- Confirm segment claims reflect the data distribution.
- Confirm limitations note any sampling skew.
- Confirm no invented statistics.
Limitations
- Findings describe the supplied dataset, not the whole market.
- Source bias in collection carries into the analysis.
Before you rely on it
Safety and limitations
- Findings describe the supplied dataset, not the whole market.
- Source bias in collection carries into the analysis.
- Review the output before acting on it.
- Do not upload confidential datasets to an external model without authorization.
- Outputs depend on the model and the source data and are not guaranteed to be accurate.
History
Changelog
- v1.0June 2026
- Initial release.
Questions
Agent Skill FAQ
What kind of segments does it find?
Data-driven groupings based on patterns in the dataset — by industry, model, size, or maturity — along with the gaps where segments are thin or unserved.
How big should the dataset be?
Larger is better for patterns. Very small lists are better served by Company Research or Competitor Research.
Do I need ProfileSpider to use this skill?
No. The skill works on any compatible data. ProfileSpider is one convenient way to produce that structured input.
Does running this skill send data to ProfileSpider?
No. Downloading or copying the file does not send any data to ProfileSpider. What happens afterward depends on the AI service you load it into.
Are Agent Skills the same as prompts?
No. A skill is a structured, reusable package — task, inputs, rules, process, and output format — so the workflow runs consistently and can be shared, versioned, and edited.