--- 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: ]`, 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_.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_.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` --- Source attribution: YujxZJCN Original project: https://github.com/YujxZJCN/teaching-skills/blob/fd0c486e61cb1f065b88133b599e8806dfaeac12/ta-coordinator/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Jiaxing Yu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: YujxZJCN Original project: https://github.com/YujxZJCN/teaching-skills/blob/fd0c486e61cb1f065b88133b599e8806dfaeac12/ta-coordinator/SKILL.md Attribution basis: Source repository record Fields of work: Education & Research, Human Resources, Project & Program Management Roles: Academic Writer, HR Generalist, Learning & Development, Project Manager, Tutor / Instructor Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: e81c0093cbe42f2bacf6d8850e0dac1e3b02fc74ad1368d2fd590c01bfcc9604