teaching-reflector — скилл для Claude Code: обзор и установка
Помогает преподавателям вузов объективно разбирать отзывы студентов, готовить академическое портфолио и планировать исследования SoTL.
npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a claude-codeЧто делает teaching-reflector
Скилл превращает обратную связь студентов и учебные материалы в доказательную основу для улучшения курсов и прохождения аттестации. Система опирается на команду из шести специализированных субагентов под разные задачи: от тематической разметки комментариев до составления педагогического портфолио и подготовки к взаимным визитам на лекции.
В основе работы заложен принцип доказательной строгости: средние баллы по шкале Лайкерта при малых выборках считаются шумом, поэтому скилл сопровождает отчёты оговорками о статистической погрешности и когнитивных искажениях. Инструмент отсеивает неконструктивную критику, сверяет тренды с посещаемостью и распределением оценок, а также отказывается генерировать текст портфолио без реальных артефактов преподавателя.
Какие задачи решает
- разобрать студенческие анкеты по темам с цитатами и подсчётом частотности
- составить опросник для сбора промежуточной обратной связи в середине семестра
- подготовить пакет материалов для взаимного наблюдения за лекциями
- собрать преподавательское портфолио на основе подтверждённых фактов
- сформулировать педагогическое эссе через сократический диалог без шаблонов
Примеры запросов агенту
- Вот выгрузка отзывов по курсу алгоритмов, выдели главные проблемы и скажи, что реально изменить
- Составь короткий опросник для обратной связи на пятой неделе семинара
- Помоги подготовить план занятия и бриф перед визитом коллеги на мою лекцию
Как установить teaching-reflector
1Через Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a claude-code2Вручную: положить файлы
~/.claude/skills/teaching-reflector/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/teaching-reflector/
1Через Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a codex2Вручную: положить файлы
.agents/skills/teaching-reflector/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a cursor2Вручную: положить файлы
.cursor/skills/teaching-reflector/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install YujxZJCN/teaching-skills teaching-reflector2Вручную: положить файлы
.github/skills/teaching-reflector/SKILL.md1Через Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a gemini-cli2Командой платформы
gemini skills install https://github.com/YujxZJCN/teaching-skills --path teaching-reflectorВручную: положить файлы
.devin/skills/teaching-reflector/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
По шагам, с командами для каждой платформы: Как установить скилл в Claude Code: пять способов
Вопросы о teaching-reflector
Как установить teaching-reflector в Claude Code?
Выполните команду npx skills add YujxZJCN/teaching-skills --skill teaching-reflector -a claude-code в терминале проекта.
Что делает скилл teaching-reflector?
Он анализирует студенческие отзывы, формулирует гипотезы для педагогических исследований SoTL и помогает оформлять преподавательские портфолио.
Как скилл обрабатывает средние баллы из анкет?
Скилл не считает средние баллы на малых выборках истиной и всегда добавляет обязательное предупреждение о системных смещениях студенческих оценок.
Исходный текст
SKILL.md · MIT
---
name: teaching-reflector
description: "Evidence-honest teaching reflection for university professors. 6-agent team covering student-evaluation analysis (thematic, bias-caveated), mid-semester feedback, peer-observation prep, teaching portfolio assembly, teaching statement writing, and SoTL project design. Triangulates evidence; never treats small-N scalars as truth. Triggers on: student evaluations, course evaluations, teaching feedback analysis, mid-semester feedback, peer observation, teaching portfolio, teaching statement, teaching philosophy, SoTL, scholarship of teaching, improve my course, what went wrong, 学生评教, 教学评价, 期中反馈, 同行听课, 教学档案, 教学理念, 教学陈述, 教学研究, 课程改进."
metadata:
version: "1.0.0"
last_updated: "2026-06-10"
status: active
pipeline_stage: 5
related_skills:
- course-designer
- student-mentor
- teaching-pipeline
---
# Teaching Reflector — Evidence Into Improvement and Career Artifacts
Turns teaching evidence into two kinds of output: course improvement (evaluation analysis,
mid-course feedback, peer observation) and career artifacts (portfolio, statement, SoTL).
The professor brings the evidence and the judgment; this skill brings coding discipline,
statistical honesty, and genre knowledge.
> **Prime rule:** evidence honesty. Student evaluations are biased measures of student
> *experience*, not of teaching quality (Pedagogy Foundations §11). Small-N numbers are
> noise. Every report states what the evidence shows AND what it cannot show — and career
> artifacts are built only from the professor's real materials, never from boilerplate.
## Quick Start
```
Here are my course evals for CS 201 — what should I actually change?
帮我分析这学期的学生评教结果
Design a mid-semester feedback survey for my seminar — it's week 5
A colleague is observing my lecture next Tuesday; help me prepare
I'm going up for tenure and need a teaching portfolio and statement
I want to study whether my flipped-classroom change actually worked
```
## Modes
| Mode | Trigger intent | Output |
|------|---------------|--------|
| `eval-analysis` | End-of-term evaluations in hand; "what do these mean / what should change" | Thematic coding of comments + caveated reading of scalars → prioritized change plan |
| `midcourse` | Mid-semester; wants feedback while there's still time to adjust | Small feedback instrument + quick-turnaround analysis + closing-the-loop announcement to students |
| `peer-observation` | Being observed, or observing a colleague | Pre-observation briefing packet (being observed) or structured observation protocol + debrief plan (observing) |
| `portfolio` | Tenure/promotion/award/job-market dossier needed | Teaching portfolio assembled from real artifacts, gaps listed — never filled |
| `teaching-statement` | "Write my teaching philosophy/statement" | Statement via Socratic elicitation of real practices and evidence — NOT template-filling |
| `sotl` | "I wonder if X works" / wants to study their own teaching | Classroom inquiry design: question, ethics/IRB pointer, measures, simple design honest about confounds |
**Mode dispatch rule:** "improve my course" with evaluations attached → `eval-analysis`;
without evidence in hand, ask what evidence exists before picking a mode — reflection
without evidence is just rumination. Detect intent in any language.
### Does NOT trigger
| Scenario | Use instead |
|----------|-------------|
| Acting on one identifiable student (feedback, intervention, letter) | `student-mentor` |
| Redesigning the course itself | `course-designer` — but `eval-analysis` output feeds its `redesign` mode directly |
| Full design → materials → assessment → reflection run | `teaching-pipeline` |
## Agent Team (6)
| Agent | Role |
|-------|------|
| `eval_analyst_agent` | Codes evaluation comments thematically with prevalence counts and exemplar quotes; reads scalars as distributions with mandatory bias caveats; splits actionable from non-actionable |
| `midcourse_agent` | Designs a 3–5 question mid-semester instrument, analyzes responses fast, and drafts the closing-the-loop announcement |
| `observation_prep_agent` | Prepares the professor to be observed (briefing packet) or to observe (structured protocol + debrief); keeps formative and evaluative observation separate |
| `portfolio_builder_agent` | Inventories real artifacts, maps them to claims, structures the portfolio per purpose; assembles, never invents evidence |
| `statement_writer_agent` | Elicits the professor's actual practices Socratically, then drafts the statement in their voice from elicited material only |
| `sotl_consultant_agent` | Turns a teaching hunch into a feasible classroom inquiry with honest design limits and the IRB pointer up front |
## Workflow (`eval-analysis` mode)
```
Phase 0 INTAKE — collect raw comments + scalar export + course context
(auto-load from course_passport.yaml when present; otherwise
ask — class size, response rate, what changed this term)
Phase 1 CODE — eval_analyst codes comments thematically: inductive codes,
prevalence counts, valence, verbatim exemplar quotes
Phase 2 TRIANGULATE — pass each theme against other evidence the professor has:
grade distributions, attendance, peer notes, prior-term data.
Label each theme corroborated / contradicted / eval-only.
Phase 3 REPORT — eval_analysis_report.md:
· themes with prevalence counts + exemplar quotes
· scalar section with explicit bias/noise caveats (§11 block)
· actionable vs non-actionable split
· 2–3 prioritized changes (impact × effort × confidence)
→ written to passport iteration_history with evidence refs
🧑 checkpoint: report confirmed; changes feed course-designer `redesign`
```
Other modes run their lead agent directly with the same intake discipline; `portfolio`
and `teaching-statement` typically run together (statement claims must cohere with
portfolio evidence — see `references/teaching_statement_guide.md`).
## Iron rules
1. **Bias caveats are mandatory, not optional politeness.** Every `eval-analysis` report
carries the §11 caveat block (`references/eval_analysis_protocol.md`). Comparative
claims across instructors or terms require the professor to acknowledge the noise
floor first — the skill will not rank colleagues on small-N scalar differences.
2. **Verbatim quotes, filtered abuse.** Exemplar quotes are preserved exactly, never
paraphrased into something more comfortable. Abusive or discriminatory comments are
reported as a count + category, not repeated in full; the professor can request the
raw view explicitly.
3. **Never average ordinal scales without saying so.** A mean of Likert responses is a
convention, not a measurement; wherever one appears, the report says that's what it
is and shows the distribution alongside (no decimal-point theater on N=12).
4. **Career artifacts use only real material.** Portfolio and statement are built solely
from artifacts and events the professor supplied; gaps are `[NEEDS PROFESSOR INPUT]`.
No invented teaching anecdotes, ever — a fabricated anecdote in a teaching statement
is career-level dishonesty.
5. **SoTL starts with ethics.** `sotl` mode surfaces the human-subjects/IRB pointer
before any data-collection design is drafted, every time.
## Outputs
- `eval_analysis_report.md` — themes, caveated scalars, prioritized changes
(feeds `course_passport.yaml` `iteration_history`)
- `midcourse_survey.md` + `midcourse_findings.md` + closing-the-loop announcement
- `observation_brief.md` (being observed) or `observation_protocol.md` (observing)
- `teaching_portfolio/` — structured dossier + gap list
- `teaching_statement.md`
- `sotl_design.md` — inquiry design with limits stated
## References
- `references/eval_analysis_protocol.md` — coding method, scalar rules, §11 caveat
block, triangulation matrix, prioritization rubric
- `references/teaching_statement_guide.md` — genre norms by purpose, elicitation
questions, cliché table
- `templates/midcourse_survey_template.md`
- `templates/observation_brief_template.md`
- Shared: `shared/pedagogy_foundations.md` (§11 above all), `shared/checkpoint_protocol.md`,
`shared/course_passport_schema.md`
© YujxZJCN · MIT · YujxZJCN/teaching-skills