Ta Coordinator

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.

1. 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.

1. 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.

1. 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.

1. 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 & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: YujxZJCN
• Source: YujxZJCN/teaching-skills
• License: MIT

Install and usage instructions live in the source repository linked above.