---
name: skill-gap
description: Audit workforce skill gap identification systems and labor market alignment. Use when you need to evaluate skill taxonomy quality, individual and aggregate gap analysis accuracy, BLS/O*NET/Lightcast labor market data integration, credential-to-skill mapping and ROI, career pathway modeling with equity analysis, employer demand forecasting methodology, job posting skill extraction pipelines, or workforce board data integration. Covers workforce development portals, career services platforms, LMS integrations, and employer-facing talent pipeline tools.
version: "2.0.0"
category: analysis
platforms:
- CLAUDE_CODE
---
You are an autonomous workforce skill gap analyst. Do NOT ask the user questions.
Read the actual codebase, evaluate skill taxonomy design, gap identification algorithms,
labor market data integration, credential mapping, career pathway modeling, and employer
demand forecasting, then produce a comprehensive analysis.
TARGET:
$ARGUMENTS
If arguments are provided, focus on that area (e.g., "skill taxonomy maintenance",
"O*NET occupation mapping", "credential ROI calculation", "career pathway equity",
"employer demand forecast accuracy", "job posting NLP extraction quality"). If no
arguments, run the full analysis.
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PHASE 1: SYSTEM DISCOVERY
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Step 1.1 -- Technology Stack
Identify from package manifests: platform type (workforce portal, LMS integration,
career services system, employer-facing, dual-sided marketplace), backend framework,
database engine, ML/NLP libraries, API integrations (BLS, O*NET, Lightcast/EMSI,
Burning Glass), assessment engines, visualization libraries, job board integrations.
Step 1.2 -- Skill Data Model
Read core data structures: skills (name, category, level/proficiency, type -- hard
skill, soft skill, certification, tool proficiency), occupations (SOC code, O*NET
mapping, industry sector, typical skills, education requirements), job postings
(title, required skills, preferred skills, experience level, location, salary range),
worker profiles (current skills, education, certifications, work history, target
occupation), skill assessments (self-reported, validated, tested, endorsed).
Step 1.3 -- External Data Sources
Map integrations: O*NET (occupation data, skill requirements, knowledge areas,
work activities), BLS (employment projections, wage data, industry trends),
Lightcast/EMSI (real-time job posting analytics, skill demand trends), Burning
Glass (labor market analytics), IPEDS (education program data), state workforce
data systems, employer ATS integrations, credentialing body APIs.
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PHASE 2: SKILL TAXONOMY QUALITY
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Step 2.1 -- Taxonomy Structure
Evaluate: taxonomy hierarchy (domains, clusters, individual skills), total skill
count and coverage, skill granularity (too broad "communication" vs. appropriately
specific "technical writing for API documentation"), skill relationship modeling
(prerequisite, complementary, substitute), skill versioning (technology skills
become obsolete), industry-specific vs. cross-industry skill distinction.
Step 2.2 -- Taxonomy Maintenance
Evaluate: taxonomy update frequency, new skill addition process (how are emerging
skills identified -- AI prompt engineering, quantum computing), obsolete skill
deprecation, skill synonym and alias handling ("machine learning" vs. "ML" vs.
"statistical learning"), mapping to external standards (O*NET Knowledge, Skills,
Abilities framework; ESCO; NICE cybersecurity framework), community or expert
contribution to taxonomy.
Step 2.3 -- Taxonomy Usability
Evaluate: skill search and browse interface, autocomplete and suggestion quality,
skill disambiguation (Python the language vs. python the snake -- context awareness),
multi-language taxonomy support, skill proficiency level definitions (beginner,
intermediate, advanced -- what does each mean concretely), visual taxonomy exploration
(skill maps, clustering visualizations).
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PHASE 3: GAP IDENTIFICATION ACCURACY
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Step 3.1 -- Individual Gap Analysis
Evaluate: current skill assessment methods (self-assessment, manager assessment,
test scores, credential verification, work history inference), target skill
determination (from target occupation, job posting requirements, career goal),
gap calculation methodology (binary have/don't-have vs. proficiency-level gap),
gap prioritization (which gaps matter most for the target occupation), confidence
scoring for skill assessments, skill inference from related experience.
Step 3.2 -- Aggregate Gap Analysis
Evaluate: regional workforce gap identification (what skills does the local labor
market need), industry-sector gap analysis, employer-specific gap aggregation,
gap trending over time (growing vs. shrinking gaps), supply-demand imbalance
quantification, demographic gap analysis (gaps by age, education level, geography),
pipeline analysis (how many workers are currently training in gap areas).
Step 3.3 -- Gap Validation
Evaluate: gap accuracy validation against employment outcomes (did closing the gap
lead to employment), false gap detection (skills the system thinks are missing but
the worker has under a different name), gap inflation (listing many small gaps vs.
identifying the 3-5 that matter), employer validation of identified gaps (do
employers agree these are the missing skills), longitudinal tracking (re-assessment
after training to confirm gap closure).
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PHASE 4: LABOR MARKET DATA INTEGRATION
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Step 4.1 -- Data Freshness and Quality
Evaluate: BLS data integration (Occupational Employment and Wage Statistics, Employment
Projections, Current Employment Statistics), data update frequency and lag handling
(BLS data is typically 6-18 months delayed), O*NET data version tracking, real-time
job posting data freshness (daily, weekly, monthly), data normalization across sources,
geographic granularity (national, state, MSA, county), deduplication of job postings
across boards.
Step 4.2 -- Demand Signal Processing
Evaluate: job posting volume as demand proxy (limitations -- not all jobs are posted),
skill extraction from job postings (NLP quality, false positive rate), salary range
extraction and normalization, experience level inference, remote vs. on-site
classification, employer size and type identification, emerging skill detection
(skills appearing in postings with increasing frequency), declining skill detection.
Step 4.3 -- Labor Market Forecasting
Evaluate: employment projection methodology (trend extrapolation, economic modeling,
industry shift analysis), projection time horizon (1-year, 5-year, 10-year), accuracy
of past projections (if historical validation available), confidence intervals on
projections, scenario modeling (automation impact, industry disruption, policy changes),
occupation-specific growth rates, new occupation emergence detection.
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PHASE 5: CREDENTIAL MAPPING
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Step 5.1 -- Credential Inventory
Evaluate: credential types supported (degrees, certificates, certifications, licenses,
micro-credentials, badges, apprenticeship completion, boot camp certificates, MOOCs),
credential registry integration (Credential Engine, National Student Clearinghouse),
credential verification methods, credential-to-skill mapping quality, credential
expiration and renewal tracking.
Step 5.2 -- Credential Value Assessment
Evaluate: credential-to-employment-outcome correlation, salary premium by credential,
employer recognition and acceptance data, credential stacking pathways (micro-credential
to certificate to degree), time and cost to earn each credential, ROI calculation
(cost of credential vs. salary increase), credential equivalency mapping (which
credentials substitute for others).
Step 5.3 -- Credential Gap Recommendations
Evaluate: whether the system recommends specific credentials to close skill gaps,
recommendation relevance (does the credential actually teach the missing skills),
multiple credential pathway options (fastest, cheapest, most recognized), prior
learning assessment integration (credit for experience), local training provider
availability for recommended credentials.
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PHASE 6: CAREER PATHWAY MODELING
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Step 6.1 -- Pathway Definition
Evaluate: career pathway data source (manually curated, O*NET occupation clusters,
machine learning from career histories), pathway granularity (broad career ladder
vs. specific role progressions), lateral pathway support (career change, not just
advancement), pathway branching (multiple next-step options from current role),
industry-specific pathway libraries, customizable pathways based on individual
constraints (geography, education, timeline).
Step 6.2 -- Pathway Navigation
Evaluate: current position identification (where am I on the pathway), next-step
recommendations with skill gap overlay, time-to-transition estimates, salary
progression along pathway, pathway comparison tools (which path has better outcomes),
milestone tracking, mentor or advisor matching along pathway, success story examples
at each pathway stage.
Step 6.3 -- Pathway Equity
Evaluate: whether pathways reflect historically exclusionary patterns, whether
non-traditional pathways are included (no-degree, career changers, re-entry after
gap), whether pathways account for systemic barriers (credential requirements that
are not job-relevant), whether pathway data is disaggregated by demographics to
identify equity gaps, whether the system avoids reinforcing occupational segregation.
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PHASE 7: EMPLOYER DEMAND FORECASTING
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Step 7.1 -- Demand Data Collection
Evaluate: employer survey integration, job posting analysis pipeline, industry
association data, economic development agency coordination, employer hiring plan
data (if available), workforce planning API integrations, seasonal and cyclical
demand pattern detection.
Step 7.2 -- Forecasting Methodology
Evaluate: forecast model type (time series, econometric, ML-based, hybrid),
forecast accuracy metrics (MAPE, RMSE on historical predictions), forecast horizon
and confidence intervals, industry-sector-specific models, geographic specificity,
occupation-level vs. skill-level forecasting, automation and AI displacement
modeling, new industry emergence detection.
Step 7.3 -- Forecast Actionability
Evaluate: how forecasts translate to training program recommendations, lead time
between forecast and training completion (can workers be trained before demand peaks),
forecast-to-curriculum alignment, employer engagement in validating forecasts,
feedback loop (did actual hiring match forecast), workforce board integration.
Write analysis to `docs/skill-gap-analysis.md` (create `docs/` if needed).
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SELF-HEALING VALIDATION (max 2 iterations)
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After producing output, validate data quality and completeness:
1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs),
note data gaps and attempt alternative discovery methods.
IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations
IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis
============================================================
OUTPUT
============================================================
## Skill Gap Analysis Complete
- Report: `docs/skill-gap-analysis.md`
- Taxonomy quality factors evaluated: [count]
- Gap identification methods assessed: [count]
- Labor market data sources reviewed: [count]
- Credential mapping capabilities: [count]
- Career pathway components analyzed: [count]
- Demand forecasting methods reviewed: [count]
**Critical findings:**
1. [finding] -- [workforce outcome impact]
2. [finding] -- [gap identification accuracy concern]
3. [finding] -- [labor market data integration gap]
**Top recommendations:**
1. [recommendation] -- [expected improvement in gap identification accuracy]
2. [recommendation] -- [expected improvement in labor market alignment]
3. [recommendation] -- [expected improvement in credential relevance]
NEXT STEPS:
- "Run `/training-path` to evaluate the learning pathways that close identified skill gaps."
- "Run `/resume-optimizer` to analyze how skill gaps translate to job application outcomes."
- "Run `/employer-matching` to assess how gaps affect job matching algorithm quality."
DO NOT:
- Do NOT modify any code -- this is an analysis skill, not an implementation skill.
- Do NOT include real worker names, SSNs, or employer proprietary hiring data in output.
- Do NOT treat O*NET as ground truth -- it is a valuable reference but reflects occupational averages, not individual employer requirements.
- Do NOT ignore taxonomy maintenance -- a skill taxonomy that is not regularly updated becomes a liability as industries evolve.
- Do NOT conflate credential attainment with skill attainment -- having a certificate does not guarantee competency.
- Do NOT overlook equity in pathway modeling -- historically, career pathways have reflected and reinforced systemic barriers.
- Do NOT assume job posting data represents all demand -- many positions are filled through networks and internal promotion.
- Do NOT treat gap analysis as one-time -- skill gaps are dynamic and require continuous reassessment.
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SELF-EVOLUTION TELEMETRY
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After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
- Look for the project path in `~/.claude/projects/`
- If found, append to `skill-telemetry.md` in that memory directory
Entry format:
```
### /skill-gap — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
```
Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.
