Inputs
Provide these when prompted. The skill asks for anything missing before it runs.
Required
- Records
- The goal (qualify, segment, or prep outreach)
Optional
- Required fields for the goal
- Approved research sources
Agent Skill
Research Gap Analysis
Identify missing information required to complete qualification, segmentation, or outreach prep.
Version 1.0 Updated June 2026 SKILL.md MIT 6 min read
Overview
What this skill does
Incomplete data quietly blocks good decisions: you cannot qualify, segment, or personalize without the right fields, but it is not obvious which gaps actually matter.
This skill compares your records against your goal and lists the missing fields that matter, why each matters, its impact on the decision, where to find it, and a priority — so research effort goes to the gaps that unblock action.
When to use it
Best used for
- Finding what blocks qualification or segmentation
- Prioritizing enrichment work
- Scoping research before outreach
- Turning "incomplete data" into a task list
Know the limits
When not to use this skill
- You have no goal to assess gaps against
- The dataset is already complete
- You want the gaps filled, not identified
Outputs
One record per identified gap with a consistent, inspectable schema.
missing_fieldThe field that is absent.
why_it_mattersWhy the field is needed.
impact_on_decisionWhat stays blocked without it.
suggested_research_sourceWhere to find it.
priorityHow urgent the gap is.
unresolved_assumptionsAssumptions to verify.
Example
Example
Gaps for an outreach goal.
Input
Goal: Personalized outreach. Records have company + title but no email or recent signal.Output
missing_field: verified email
why_it_matters: Required to contact the prospect
impact_on_decision: Blocks outreach entirely
suggested_research_source: Email finder; company domain
priority: high
unresolved_assumptions: Title implies decision authority (verify)The analysis turns "data feels incomplete" into a ranked task list, with the missing email as the high-priority blocker for outreach.
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
Research Gap Analysis
Purpose
Identify missing information required to complete qualification, segmentation, or outreach prep.
When to use this skill
- Finding what blocks qualification or segmentation
- Prioritizing enrichment work
- Scoping research before outreach
- Turning "incomplete data" into a task list
When not to use this skill
- You have no goal to assess gaps against
- The dataset is already complete
- You want the gaps filled, not identified
Required inputs
- Records
- The goal (qualify, segment, or prep outreach)
Optional inputs
- Required fields for the goal
- Approved research sources
Rules
- Assess gaps against the stated goal.
- Explain why each gap matters and its impact.
- Suggest a realistic research source per gap.
- Prioritize gaps by impact.
- List assumptions to verify.
Process
- Parse the goal and required fields.
- Compare records to requirements.
- List missing fields and their impact.
- Suggest sources and assign priority.
- Record unresolved assumptions.
Output format
Return one record per identified gap with the following fields:
- missing_field
- whyitmatters
- impactondecision
- suggestedresearchsource
- priority
- unresolved_assumptions
Validation
- Confirm gaps map to the goal.
- Confirm priorities reflect impact.
- Confirm assumptions are flagged.
Limitations
- It identifies gaps; it does not fill them.
- Source suggestions may vary in availability.
Before you rely on it
Safety and limitations
- It identifies gaps; it does not fill them.
- Source suggestions may vary in availability.
- 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 does it return?
A prioritized list of the specific information missing to complete your goal — qualification, segmentation, or outreach prep — so you know exactly what to enrich next.
Where does this fit in a workflow?
Run it before qualification or outreach to see what data you still need, then enrich the high-priority gaps first.
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.