Assess Domain Capability

Assess Domain Capability

Overview

Build a defensible domain capability diagnostic from current sources: research the field, confirm scope, create a competency model, generate a 35-45 item questionnaire, collect answers locally, score them, visualize results, and write a job-readiness report.

Invocation

Use examples:

• $assess-domain-capability AI Agent
• $assess-domain-capability 前端工程师岗位胜任力
• $assess-domain-capability create a job-readiness self-assessment for data analytics

Required Gates

1. Scope confirmation: If the field is broad, propose 2-3 narrower scopes and stop for user confirmation before generating the assessment.
2. Live research: Search current, source-backed material before defining competencies or questions. Prefer official standards, certification blueprints, vendor docs, reputable reports, curricula, and real job descriptions. Cite sources and access dates.
3. Model confirmation: Present the competency model and assessment mix before building the webpage if the user has not already approved them.
4. Local persistence check: Do not claim a browser-only page can write arbitrary local files. Use an existing backend, create a small local backend, or provide JSON export/download as a fallback.
5. Evidence-first reporting: Treat self-rating as calibration evidence, not the main score. Job-readiness conclusions must rely primarily on objective, scenario, and rubric-scored evidence.

Workflow

1. Collect intake: target role, target level, region/industry, use case, language, answer time, and whether the report should be strict or lightweight.
2. Load references/assessment-design.md, then perform live research. Keep concise research notes with source ids.
3. Build 4-8 competency dimensions with 3-6 subcompetencies each. Include behavior anchors from entry to expert-candidate levels.
4. Generate 35-45 items. Use self-rating only for confidence, familiarity, or frequency; use scenario, knowledge, and subjective case items for the main evidence.
5. Load references/artifact-schema.md when creating assessment JSON, answer storage, a local web app, or charts.
6. Build the webpage in the current project style. If product-design skills/tools are available and the UI brief is under-specified, use product-design:get-context first. Keep the first screen as the usable questionnaire, not a landing page.
7. Save answers locally using the schema reference. For subjective answers, score against the rubric before final scoring or mark them pending.
8. Run scripts/score_assessment.py against the assessment and response JSON. Use the JSON output for radar/bar charts and the Markdown output as a report scaffold.
9. Write the final report: strengths, weak areas, calibration gap, role-readiness label, evidence confidence, and 2-week/1-month/3-month improvement plan.

Scoring Command

python3 skills/assess-domain-capability/scripts/score_assessment.py \
  --assessment data/assessments/<slug>/assessment.json \
  --responses data/responses/<slug>/<timestamp>-response.json \
  --output data/reports/<slug>/<timestamp>-scores.json \
  --markdown data/reports/<slug>/<timestamp>-report.md

Common Mistakes

• Generating questions before research.
• Letting 1-10 self-ratings dominate the result.
• Asking broad, socially desirable questions instead of scenario choices.
• Producing a polished UI that cannot save answers locally.
• Reporting "you are senior" without scope, confidence, evidence, and caveats.