Skill Gap Analysis
Define a skill-gap register with supplied levels, priorities, an owner, a target date and training follow-up.
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Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-skill-gap-analysis | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Skill Gap Analysis
Define a skill-gap register with supplied levels, priorities, an owner, a target date and training follow-up. Requested empty artifacts share one field list. Use it when capability gaps are known but ownership, deadlines or training priorities are missing.
What it does
Skill Gap Analysis defines a register for skill identification and training needs. Record the gap, required level and target date, assign ownership and link the follow-up to a training request. Severity is useful when it determines priority. The workflow starts from an assessment supplied by the business, then captures the gap, severity, owner, target date and training request. Current and required levels use the same vocabulary so expectations remain comparable. The recommended training and course-request fields preserve follow-up without implying that a course has been delivered.
The default deliverable is a short recommendation. An explicit setup, build or conversion request produces artifacts after essential missing facts are resolved. Advice receives a direct answer, while a review works from the material provided. This keeps the size of the proposed setup tied to the requested outcome.
When to use - and when NOT to
Use it when capability gaps are known but ownership, deadlines or training priorities are missing. It does not assess skills, deliver training or compute KPIs. Record verified assessments rather than generating a suitability judgment.
This is an empty-template workflow, not processing of real employee or customer records. Automation, reminders and synchronization require the separate integration layer. Sensitive decisions and adjustments require qualified human review before anything is acted on.
Inputs and outputs
Establish team versus individual scope, roles and headcount; current and target levels and assessor; training versus reassignment, funding and deadline; existing review and training links; and whether a gap register, action plan or both are needed. Questions are asked one at a time, excluding anything already answered in earlier messages. Unknown facts stay unknown. Intent, confirmed facts, open questions and requested formats remain internal unless the user asks to inspect them; no missing answer is invented.
The field model includes Skill Gap Record (text); Assessment Date (date); Current Level (select); Department (text); Employee Name (text); Gap Severity (select); Notes (long_text); Recommended Training (text); Course Requests (text); Required Level (select); Skill Area (text); Skill ID (id); Status (select); Target Date (date).
Current Level starts with 1 - Beginner, 2 - Basic, 3 - Proficient, 4 - Advanced, 5 - Expert. Gap Severity starts with Low, Medium, High, Critical. Required Level starts with 1 - Beginner, 2 - Basic, 3 - Proficient, 4 - Advanced, 5 - Expert. Status starts with Identified, Planned, In Training, Closed.
The starting level vocabulary runs from 1 - Beginner to 5 - Expert. Add an explicit owner to the chosen workflow even though the starter field list does not contain a dedicated owner property.
Requested CSV, SQL DDL, JSON Schema and Notion mappings derive from one field list. Display names remain identical across CSV and JSON, and normalized SQL identifiers are documented. Relation fields keep their relational meaning, dates remain dates and money remains currency. Machine-readable data is separated from explanation, with validation failures and limitations reported separately.
One supplied SQL example is:
CREATE INDEX idx_skill_gap_analysis_status ON skill_gap_analysis (status);
Excel-compatible CSV uses UTF-8 with a byte order mark. Because CSV has no column types, numeric, date and currency formatting is identified after the data. A CSV is not an .xlsx workbook; the latter is generated only when requested. Example rows require an explicit request and must be clearly synthetic. Sensitive identifiers are not unnecessarily repeated in a review, and generated templates remain empty.
Integrations
Notion output goes to notion-manual-import for the CSV, property mapping, import steps and verification checklist. Manual mappings require no connection. Requested workspace changes require verified tool access, the actual target and authorization; a claimed connection is insufficient evidence. Notion relation columns resolve only after both databases have been imported. Select options are an initial vocabulary that can be renamed to match the business. people-directory supplies the related employee master record; notification-reminder-hub is the separate due-date reminder module. Personal identifiers and sensitive financial details stay out of generated templates.
Source README
Works out the smallest useful Skill Gap Analysis setup for the business in front of it, then
builds it only when asked. The default output is a short recommendation, not a
spreadsheet. Artifacts - CSV, SQL DDL, JSON Schema, Notion mapping - are produced on
request, from one field list so they cannot drift apart.
Discussion
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