Ai skill gap analyzer

October 15, 2025

Ai skill gap analyzer

1) What is it?

AI Skill Gap Analyser is a theoretical design for a system that:

Models job roles and skills as a graph/taxonomy
Collects evidence of a person’s current skill proficiency
Compares required vs. demonstrated proficiency
Prioritizes gaps and recommends targeted learning paths

This README focuses on concepts—no code, no vendor lock-ins.

2) Why it exists

Organizations struggle to answer:

“What skills matter for this role?”
“Who’s close to being role-ready?”
“What training actually moves the needle?”

The analyser provides a consistent, evidence-driven way to:

Define role requirements
Measure current capability
Optimize development plans
Track progress over time

3) Core concepts

3.1 Skill ontology / taxonomy

Skill: A measurable capability (e.g., “Prompt Engineering,” “Vector Databases,” “Linear Regression”).
Attributes: name, description, category, subskills, related_skills, version (taxonomy release), evidence_types_supported.
Levels: e.g., 0–4 (Awareness → Expert) or Novice/Intermediate/Advanced/Expert with behavioral descriptors.

3.2 Role profiles

Role: A bundle of skills with target levels and weights.
Example attributes: role_name, mission, skills_required[{skill_id, target_level, weight}], compliance_constraints.

3.3 Evidence of proficiency

Multiple evidence channels (all optional, scored with reliability weights):

Assessments (quizzes, coding tests, case studies)
Work artifacts (repos, notebooks, dashboards)
Usage analytics (tools/libraries used, frequency, recency)
Peer/manager reviews (structured rubrics)
Credentials (certs, course completions)
Performance signals (OKRs, ticket complexity)

Each evidence item has: source, timestamp, skill_mapping, score, confidence, validity_window.

4) Conceptual architecture

Taxonomy Service
- Stores skills, levels, relations, versions.
Role Designer
- Compose roles from skills; set targets/weights.
Evidence Ingestion Layer
- Normalizes inputs; maps evidence → skills; assigns confidence.
Scoring Engine
- Aggregates evidence into a Current Proficiency Vector (CPV) per person.
Gap Analyser
- Compares CPV vs. Role Requirement Vector (RRV); computes gap magnitude.
Recommendation Engine
- Generates learning actions, practice tasks, mentorship matches.
Reporting & Governance
- Dashboards, fairness checks, data quality, audit trails.

5) Scoring & gap computation (theory)

5.1 Proficiency scoring (per skill)

Aggregate function over evidence:
proficiency = Σ (normalized_score_i × confidence_i × decay(age_i))
Decay reduces old evidence impact; confidence reflects source reliability.

5.2 Role readiness

For each skill k:
gap_k = max(0, target_level_k − proficiency_k)
Weighted readiness for a role:
readiness = 1 − ( Σ (weight_k × gap_k) / Σ weight_k )
(Bounded to 0–1; higher is better.)

5.3 Priority index

priority_k = gap_k × weight_k × criticality_k
Sort skills by priority_k to drive learning plans.

6) Recommendations (theory)

Given top gaps:

Learning objects: courses, docs, labs (curated, level-matched, time-boxed).
Deliberate practice: mini-projects, code katas, case prompts aligned to evidence deficits.
Mentorship / pairing: match with peers strong in the target skill.
On-the-job stretch: assign tasks mapped to the gap skills.
Feedback loops: schedule new assessments post-learning; update evidence.

Recommendation ranking considers:

Skill coverage (how much a resource closes the gap)
Time to completion
Credibility/quality
User preferences (format, language)
Organization constraints (budget, compliance)

7) Fairness, ethics, and privacy

Data minimization: store only what’s necessary for skill inference.
Consent & transparency: individuals can see inputs, scores, and how they were derived.
Bias controls:
- No demographic features in scoring
- Monitor disparate impact across groups
- Regular audits of taxonomy language and assessments
Right to challenge: allow users to add or dispute evidence.
Security: RBAC, encryption in transit/at rest, audit logs, TTLs for stale evidence.

8) Quality & reliability

Confidence calibration: align scores with ground truth (SME labels).
Test–retest reliability: stable scores under similar conditions.
Content validity: assessments truly measure the target skill level.
Drift monitoring: taxonomy and role profiles versioned; migration rules.

9) Typical workflows

Define role → pick skills & target levels → publish profile (v1).
Ingest evidence → map to skills → compute CPV.
Analyse gaps → produce readiness score + prioritized gap list.
Plan learning → recommend resources & projects.
Reassess → new evidence updates scores → track improvement over time.

10) Data model (logical, simplified)

Skill(id, name, description, level_descriptors[], category, related[])
Role(id, name, skills[{skill_id, target_level, weight}])
Evidence(id, person_id, skill_id, source, score, confidence, timestamp, ttl)
Person(id, attributes_minimized)
Proficiency(person_id, skill_id, value, last_updated)
Readiness(person_id, role_id, value, breakdown[])
Recommendation(id, person_id, role_id, skill_id, item_ref, expected_impact)

(This is a conceptual schema, not a DB spec.)

11) Limits & assumptions

Evidence mapping is non-trivial; false precision is a risk.
Skill levels must have clear behavioral descriptors to avoid subjectivity.
Context matters: the same “skill” may differ across domains (e.g., LLM ops in healthcare vs. e-commerce).
Cold start: new users lack evidence; start with baseline assessments.

12) Governance & versioning

Taxonomy versions: AI-Skills v2025.10 with change logs.
Role profile versions tied to taxonomy versions.
Migration rules for deprecations/renames.
Review cadence: quarterly SME review; emergency patches for obsolete items.

13) Example (illustrative, not executable)

Role: “AI Product Engineer”

Required skills (targets):
- Prompt Engineering (3), LLM Evaluation (2), Python (3), Vector DBs (2), Experimentation Design (2), MLOps (2)
Person CPV (partial):
- Prompt Eng 2.4, Python 3.1, Vector DBs 1.3, MLOps 1.9
Top gaps by priority: Vector DBs, LLM Evaluation, Experimentation Design
Recommendations: a project to build RAG with eval harness; paired review with SME; short assessments post-project.

14) Roadmap (theory)

v0.1: Manual taxonomy, CSV imports, basic scoring & gap report
v0.2: Recommendation ranking, decay functions, basic dashboards
v0.3: Evidence confidence learning, fairness reports, audit UI
v1.0: Org-wide analytics, role simulation (“what-if” staffing), API surface

15) Glossary

CPV: Current Proficiency Vector (person × skills)
RRV: Role Requirement Vector (role × skills)
Gap: Positive difference between target and current proficiency
Priority index: Gap × Weight × Criticality
TTL: Time-to-live (evidence freshness window)

16) License & attribution (theory)

Cite external taxonomies or frameworks if adopted.

Choose any standard open license (MIT/Apache-2.0) when you implement.

Tags: AIAutomation, AIEngineering, AIProjects, GenAI, HRTech, LangChain, MachineLearning, OpenAIAPI, PromptEngineering, TalentAcquisition AIInnovation

Ai skill gap analyzer