Individual skill
cohort-analyst
Learner and cohort analysis (学情分析) for university professors — cross-cutting support for design, builds, and the weekly loop. 4-agent team turning professor-held student data — ability lists, pre-course diagnostics, pre-lesson questionnaire results — into evidence-based teaching decisions: ungraded diagnostic design, aggregate readiness profiles, lesson calibration, evidence-based grouping, and mid-term trajectory re-analysis. Cohort aggregates only — no individual-level output, ever. Triggers on: student readiness, pre-assessment, diagnostic quiz, pre-lesson questionnaire, prior knowledge survey, learning analytics, ability levels, class profile, differentiation, grouping, 学情分析, 学情, 摸底, 前测, 预习问卷, 课前问卷, 学生基础, 分层教学, 分组.
cohort-analyst/SKILL.md
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- MIT
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- ai-agents Repository topic
- assessment Repository topic
- claude Repository topic
- claude-code Repository topic
- course-design Repository topic
- edtech Repository topic
- higher-education Repository topic
- pedagogy Repository topic
- syllabus Repository topic
- teaching Repository topic
- education Derived category
- data Derived category
- design Derived category
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SKILL.mdcore · 10,481 bytesagents/calibration_advisor_agent.mddoc · 4,991 bytesagents/cohort_analyst_agent.mddoc · 5,156 bytesagents/diagnostic_designer_agent.mddoc · 4,947 bytesagents/grouping_strategist_agent.mddoc · 4,737 bytesreferences/analytics_honesty.mddoc · 9,889 bytesreferences/diagnostic_design_guide.mddoc · 8,775 bytestemplates/cohort_profile_template.mddoc · 4,952 bytestemplates/diagnostic_template.mddoc · 3,995 bytes
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# Cohort Analyst — Learner Evidence Team
Turns the student data a professor already holds — ability lists, pre-course
diagnostics, pre-lesson questionnaire results — into teaching decisions with evidence
behind them. Cross-cutting: a pre-term profile informs Stage 0/1 design
(`course-designer` reads `learner_profile`), pre-lesson results calibrate Stage 2
builds (`lesson-builder`), and the Stage 4 weekly loop re-runs the cycle as the
cohort moves. The professor knows the discipline and the students; this skill brings
instrument craft, aggregation honesty, and the discipline to say what a 5-item quiz
cannot say.
> **Prime rule — the privacy architecture:** the unit of analysis is the **cohort**.
> The Course Passport receives aggregates only — distributions, prevalence
> percentages, heterogeneity measures — written into `learner_profile` and shown to