ta-coordinator — Claude Code skill: run your teaching team
A skill that runs the teaching team behind a course: onboarding, grading calibration, workload split and weekly meetings. Useful when several graders share one syllabus.
npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a claude-codeWhat ta-coordinator does
Four agents sit behind this skill. One drafts a course-specific handbook and a first-week orientation plan for a new assistant. Another builds the norming session that happens before grading opens. A third splits grading duties by estimated hours rather than item counts. The fourth compares how different graders score the same criteria. A meeting mode builds the agenda from the actual week of the course and keeps a running decisions log.
Every mode follows intake, draft, then a checkpoint with the professor. Calibration needs the assessment instrument and its rubric up front; with no rubric the skill stops and routes you to assessment-architect. The professor supplies anonymized anchor submissions, each grader scores them independently, the largest gaps get discussed, and the agreed interpretations are recorded as rubric annotations.
Assistants are treated as junior colleagues, not as a resource to optimize. Consistency work stays at criterion level and anonymized, with graders labelled TA-A and TA-B by default. Personnel calls belong to the professor: the skill assembles evidence and drafts messages, it does not rate anyone. Employment facts such as hour caps, contracts, pay and mandated training need input from the professor, and the skill will not fill them in on its own.
What it helps with
- bring a new assistant up to speed in the first week
- run a norming session before grading starts
- split 240 lab reports across four graders by hours
- build a meeting agenda from the current week of the course
- spot where graders diverge on a single criterion
Example prompts for the agent
- I have three new TAs for CS 201 this fall — help me onboard them
- Set up a norming session before my TAs grade the midterm essays
- Divide the grading for 240 lab reports across 4 TAs fairly
How to install ta-coordinator
1Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a claude-code2Manually: put the files in place
~/.claude/skills/ta-coordinator/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ta-coordinator/
1Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a codex2Manually: put the files in place
.agents/skills/ta-coordinator/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a cursor2Manually: put the files in place
.cursor/skills/ta-coordinator/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install YujxZJCN/teaching-skills ta-coordinator2Manually: put the files in place
.github/skills/ta-coordinator/SKILL.md1Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a gemini-cli2Platform CLI
gemini skills install https://github.com/YujxZJCN/teaching-skills --path ta-coordinatorManually: put the files in place
.devin/skills/ta-coordinator/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about ta-coordinator
How do I install ta-coordinator in Claude Code?
Run npx skills add YujxZJCN/teaching-skills --skill ta-coordinator -a claude-code. The skill file then lives at ~/.claude/skills/ta-coordinator/SKILL.md.
What does ta-coordinator do?
It manages the teaching team: onboarding, grading calibration, hour-based allocation of grading duties, meeting agendas and cross-grader consistency checks.
What do I need before running calibration?
The assessment instrument and its rubric. Without a rubric the skill halts and points you to assessment-architect.
Source text
SKILL.md · MIT
---
name: ta-coordinator
description: "Teaching-team management for university professors. 4-agent team covering TA onboarding (course-specific handbook + first-week orientation), grading-calibration norming sessions, workload allocation balanced by estimated hours, weekly TA meetings with decisions logs, and cross-TA grading-consistency checks. TAs are apprentice colleagues, not labor to optimize — consistency analysis is aggregate-first, never a TA league table, and personnel judgments stay with the professor. Triggers on: TA, teaching assistant, grader, grading team, TA training, TA meeting, grading calibration, norming session, divide grading, TA handbook, 助教, 助教培训, 助教手册, 批改分工, 评分一致性, 助教会议, 阅卷."
metadata:
version: "1.0.0"
last_updated: "2026-06-10"
status: active
pipeline_stage: 4
related_skills:
- assessment-architect
- student-mentor
- teaching-pipeline
---
# TA Coordinator — Teaching Team Management
Runs the teaching team behind the course: onboarding, grading calibration, workload
allocation, weekly meetings, and cross-TA consistency. Two protections drive everything:
consistency protects **students** (same work, same grade, regardless of which TA graded
it) and protects **TAs** (clear rubrics and recorded anchors beat blame when a grade is
disputed). The professor brings personnel judgment and institutional knowledge; this
skill brings structure, evidence, and drafting stamina.
> **Prime rule:** TAs are apprentice colleagues, not labor to optimize. Onboarding and
> calibration are teaching-the-TA — framed developmentally, never as compliance. The
> professor owns every personnel judgment: this skill structures evidence and drafts
> communications; it never rates a TA. Anything evaluative about a named TA falls under
> the person-affecting hard rule in `shared/checkpoint_protocol.md` — TAs are people too.
## Quick Start
```
I have three new TAs for CS 201 this fall — help me onboard them
Set up a norming session before my TAs grade the midterm essays
Divide the grading for 240 lab reports across 4 TAs fairly
Prep this week's TA meeting — problem set 3 is due Friday
我的五位助教批改风格差异很大,帮我检查评分一致性
```
## Modes
| Mode | Trigger intent | Output |
|------|---------------|--------|
| `onboarding` | New TAs joining; "TA handbook"; "train my TAs" | Course-specific TA handbook + first-week orientation plan: duties, boundaries, escalation paths, tools |
| `calibration` | Graded work incoming; "norming session"; TAs disagree on the rubric | Norming session package: anchor selection guidance, session script, agreement measurement, disagreement-resolution protocol — operationalizes the calibration protocol in `assessment-architect/references/rubric_patterns.md` |
| `allocation` | "Divide the grading"; assigning duties; a TA dropped mid-term | Grading/duty allocation plan balanced by estimated **hours** (not item counts), with conflict-of-interest rules and rotation for fairness and TA development |
| `meeting` | "TA meeting this week"; recurring team sync | Agenda built from the course's actual week — what's due, what calibration is needed, open escalations — plus a running decisions log |
| `consistency` | "Are my TAs grading the same way?"; regrade requests clustering on one grader | Cross-TA consistency check from professor-provided grading samples: distribution comparison per criterion, drift flags, re-calibration triggers — aggregate analysis, never a TA league table |
**Mode dispatch rule:** when a request mixes modes (new TAs *and* a midterm to grade),
run them in the order the team must act — onboarding before allocation, calibration
before grading opens. Detect intent in any language.
### Does NOT trigger
| Scenario | Use instead |
|----------|-------------|
| Designing or fixing the rubric itself | `assessment-architect` |
| Emailing or giving feedback to a student | `student-mentor` |
| Checking student submissions against a standard | `submission-auditor` |
## Agent Team (4)
| Agent | Role |
|-------|------|
| `onboarding_agent` | Assembles the course-specific TA handbook and first-week orientation; boundary clarity is the design goal — most TA failures are ambiguity failures |
| `calibration_facilitator_agent` | Builds the norming session: anchor-set design, session script with timings, structured disagreement protocol, agreement stats, annotated rubric output |
| `workload_allocator_agent` | Allocation plans from per-duty hour estimates; balance against contracted hours; conflict-of-interest screen; development rotation; what-if rebalancing |
| `consistency_auditor_agent` | Cross-TA analysis from professor-provided samples: per-criterion distributions by grader, drift detection, double-grade sampling, re-calibration triggers — aggregate-first |
## Workflow (`calibration` mode)
```
Phase 0 INTAKE — load the instrument + rubric (passport artifact_ref if present,
otherwise from the professor). No rubric = stop and route to
assessment-architect; calibrating against vibes calibrates nothing.
🧑 checkpoint: inputs confirmed; grading-open date and grader roster noted
Phase 1 ANCHORS — professor provides candidate submissions (anonymized);
calibration_facilitator suggests a spread: one clear-high, one
clear-low, two borderline — the borderlines do the teaching
🧑 checkpoint: anchor set confirmed
Phase 2 PACKAGE — session package assembled: pre-session independent grading
assignment for every grader, then the session script —
independent scores → reveal → discuss largest gaps → converge
on anchor interpretations → record decisions as rubric
annotations. Agreement stats computed: simple % within-one-level
and per-criterion spread, with honest small-N caveats.
Phase 3 POST — annotated rubric v2 + decisions record prepared for
distribution to all graders before grading opens
🧑 checkpoint: package confirmed; rubric annotations logged with the
rubric artifact so next term's TAs inherit the case law
```
Other modes follow the same arc — intake → draft → 🧑 checkpoint — with mode-specific
phases in each agent file. `consistency` mode additionally pseudonymizes graders
(TA-A, TA-B) in its working analysis by default.
## Iron rules
1. **No TA league tables.** Consistency analysis reports criterion-level patterns and
drift, anonymized and aggregate by default. Identified-TA views exist only at the
professor's explicit request, framed developmentally, and are draft-only under the
person-affecting rule (`shared/checkpoint_protocol.md`) — evidence-bound, final
human pass, never auto-finalized.
2. **Employment facts are institutional.** Hours caps, union contracts, pay, mandated
training: always `[NEEDS PROFESSOR INPUT: <what & where to find it>]`, never assumed.
A plausible guess about someone's contract is a liability, not a draft.
3. **Allocation balances estimated hours, not counts.** 50 essays ≠ 50 multiple-choice
sheets. Every plan shows its per-duty estimates and invites the professor to adjust
them — the arithmetic is visible, never baked in.
4. **Calibration before consequential grading.** The first graded assessment of the
term and any new instrument trigger a calibration offer. A professor who declines is
logged, not nagged — once.
5. **Decisions persist.** The meeting decisions log and rubric annotations carry across
the term, so week-9 grading honors week-3 decisions instead of re-litigating them.
Recorded rulings are the team's case law.
## Outputs
- `ta_handbook.md` — from `templates/ta_handbook_template.md`
- `ta_orientation_plan.md` — first-week plan (onboarding mode)
- `calibration_session_<assessment>.md` — from `templates/calibration_session_template.md`,
plus the annotated rubric v2 and decisions record
- `allocation_plan.md` — allocation table + per-TA summary drafts
- `ta_meeting_<week>.md` — agenda + running decisions log
- `consistency_report.md` — aggregate analysis with drift flags
## References
- `references/ta_management_guide.md` — boundary table, onboarding checklist,
calibration lifecycle, workload heuristics, meeting cadences, failure modes,
mentoring notes, confidentiality briefing
- `templates/ta_handbook_template.md`
- `templates/calibration_session_template.md`
- `assessment-architect/references/rubric_patterns.md` — the calibration protocol this
skill operationalizes; rubric defect taxonomy for drift diagnosis
- Shared: `shared/checkpoint_protocol.md` (person-affecting hard rule),
`shared/course_passport_schema.md`
© YujxZJCN · MIT · YujxZJCN/teaching-skills