THE DISHA INTELLIGENCE ENGINE → SKILLS INTELLIGENCE
See What Capability Really Exists.
Map skills to work, evidence, proficiency, adjacency and emerging demand — beyond titles and course lists.
What can a person, team, institution or workforce actually do — and what capabilities are emerging?
Signature: WORK → TASK → SKILL → EVIDENCE → PROFICIENCY → ADJACENCY
One system, six questions — you are here:
Signature explorer — try it
A living capability graph, one node at a time
Nodes are skills, tasks, occupations, roles, projects, credentials, courses, tools, industries and people. Edges say requires, demonstrates, develops, transfers, adjacent-to, emerging-in.
Definition & related
Task decomposition
Breaking a work outcome into the discrete tasks and decisions that produce it.
Adjacent to: Process mapping · Requirements analysis · Work design
Bridges toward process mapping via requirements analysis
Tasks requiring it · evidence types
- · Scoping a platform migration
- · Designing a curriculum from job outcomes
- · Estimating a seasonal workload
- → Shipped scoping documents
- → Facilitated decomposition workshops
- → Before/after delivery metrics
Roles · learning · credentials
Product lead · Workforce planner · Curriculum architect
Learning: Task-Evidence Method Library (Knowledge Hub)
Credentials: No single credential — evidenced through project artifacts
DISHA skills taxonomy v4.2 · crosswalked to ESCO & O*NET (illustrative versioning)
Skill Evidence Mapper
A claim is not a proficiency — evidence decides
Attach or remove evidence below and watch the claim's honest status. Evidence quality, recency and context matter; every mention does not equal proficiency.
Claim under review: “Incident management — proficient”
Honest status with 2 evidence items attached
Claimed
Some evidence exists but is not yet independent or complete. The claim stays visible as claimed — never silently upgraded.
Emerging Skills Radar
Observed and forecast — always labeled, never blended
AI-assisted task decomposition
ObservedAppearing in task lists across 3 tracked settings · population: 240 synthetic work histories · period: last 2 quarters · source: DISHA simulation
Evidence-literate assessor roles
ObservedAssessor-calibration tasks appearing in institution programmes · source: illustrative programme index
Roster-agnostic coordination
ForecastFORECAST — modeled continuation of rotation-tooling trends, not an observation · horizon: 12 months
Crosswalk curation
ForecastFORECAST — maintaining taxonomy crosswalks becomes a named task · confidence: low, flagged as forecast
Every radar row carries population, period and source. Forecasts are forecasts — labeled as such, with confidence stated.
Calculators
Run the numbers — as scenarios, never verdicts
Calculator 1
Skill Gap
Current evidence vs target requirements — the honest distance, with the evidence behind every claim.
Calculator 2
Adjacency Explorer
The bridge skills that connect one capability to a neighboring one.
Calculator 3
Skill Portfolio
Core, transferable, emerging and developing — a portfolio view, not a score.
Every definition and relationship is traceable, evidence is visible, proficiency claims are explainable — and there is no opaque capability score.
Ask the AI Skills Analyst
Reads the graph and the evidence — and says so when the evidence is thin.
- What can I already do — with evidence?
- Which skill bridges two career options?
- What evidence would strengthen this claim?
- Which tasks are changing?
Grounding rules
- Answers cite the authorized records behind them — or say the evidence does not exist.
- Uncertainty is shown, never smoothed away.
- Recommendations are options for a human to judge — never silent decisions.
- Every answer stays within your purpose and access level.
In the live product these conversations stream from your authorized data. Here, the questions show exactly what the analyst is built to answer.
Interconnections
Where skills intelligence hands off next
Conceptual navigation across one intelligence system — links carry your context; none of this implies a causal or predictive pipeline.
Technical architecture — how this page is built
Connected systems
Skills taxonomy service, graph store, evidence store, ontology mapping, proficiency model, semantic normalization, event stream, analytics — multiple taxonomies with explicit crosswalks.
Core objects
Skill, task, occupation, role, project, credential, course, tool, industry, person, edge relation, taxonomy version.
Governance — identical on every Intelligence page
How this system stays honest
These five statement types never blend: everywhere in the engine, you can tell what is measured, what is derived, what is projected and what is advised.
Statement types
Observed, inference, forecast, scenario and recommendation are visually distinguished — never blended.
Illustrative by default
All data, people and outcomes on these pages are labeled illustrative composites.
No fabrication
Never fabricate people, employers, credentials, outcomes or statistics.
Explainable AI
AI explanations expose the evidence and the uncertainty behind them.
Authorized use
Data is used only within its authorized purpose and access level.
Human authority
AI supports judgement; it never replaces human decision authority.
Skills Intelligence adds: Never infer a skill from a job title alone. Course completion is not demonstrated competence unless evidence supports it. People can correct skill inferences about themselves.
The engine behind the pages
Six pages, one interaction contract
Rule 1
Context handoff
Moving between the six pages carries your question and context with you — you never restart from zero.
Rule 2
Shared vocabulary
Skill, evidence, proficiency, readiness and scenario mean the same thing on every page.
Rule 3
Common evidence layer
Every claim carries source, timestamp, provenance, confidence and access level — everywhere.
Rule 4
AI layer boundaries
The AI summarizes, explains, compares and simulates — without inventing facts or silently converting recommendations into decisions.
Rule 5
Progressive disclosure
Overview first; evidence, assumptions and audit trails open on demand.
Journey · Recruit
Journey · Develop
Journey · Move
Journey · Transform
Journey · Institution
Illustrative journeys — conceptual paths across the engine, not claims about how any decision is made.