workload_allocator_agent

workload_allocator_agent is an agent for Claude Code from YujxZJCN/teaching-skills. It costs 25 tokens per session (925 once invoked), scanned A, original, MIT.

A grading-calibration session builder for teaching assistants. Calibration is a practice session where graders score sample student work together so they interpret the same rubric consistently.

In plain words
What is it for?
It designs anchor examples, a timed session script, a disagreement process, agreement statistics, and an annotated rubric package for a specific assessment and grading team.
Why use it?
Rubric wording can lead capable graders to give different scores. A structured session uses real, anonymized examples and treats disagreements as clues that the rubric may need clarification.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the teaching-skills plugin — 15 skills, 30 commands, 76 agents shipped together

Good fit It designs anchor examples, a timed session script, a disagreement process, agreement statistics, and an annotated rubric package for a specific assessment and grading team.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/yujxzjcn/teaching-skills/workload_allocator_agent
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/YujxZJCN/teaching-skills

Made for: Claude Code.

Or install teaching-skills, the plugin that ships this one along with the rest of its 15 skills, 30 commands, 76 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for workload_allocator_agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/workload_allocator_agent/github.svg)](https://agentmods.dev/agents/yujxzjcn/teaching-skills/workload_allocator_agent)
Your own site
<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/workload_allocator_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/workload_allocator_agent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for workload_allocator_agent

Your own site · 80×15
<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/workload_allocator_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/workload_allocator_agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 925 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.00925
Opus 5.5 $0.00010 $0.00370
Sonnet 5.5 $0.00005 $0.00185
Haiku 4.5 $0.00003 $0.00093

Measured 21d ago against content hash 1e74f477d143, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

workload_allocator_agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 21d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

ta-coordinator/agents/workload_allocator_agent.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Workload Allocator — Hours-Balanced Duty Planner

Role

You divide the team's work so it is fair in hours, not in item counts — 50 essays and 50 multiple-choice sheets are not the same afternoon. Every estimate you produce is shown, sourced, and adjustable; the professor corrects your arithmetic with their knowledge of the course, and the plan is honest about overload rather than elastic about it. Allocation is also development: rotation exists so each TA leaves the term having done more than one kind of work.

Procedure

  1. Inputs: the TA roster with each TA's contracted hours and any institutional hours cap — [NEEDS PROFESSOR INPUT], never assumed (Iron Rule 2) — plus the duties to cover: grading (assessment type and submission counts from the passport assessment plan), section/lab prep, office hours, proctoring, meetings.
  2. Estimate hours per duty, arithmetic visible:
    • Grading: minutes-per-submission × count, from the heuristics table in references/ta_management_guide.md § Workload estimation, with the novice-TA multiplier applied to first-time graders — and say which constant you used
    • Recurring duties (office hours, sections, meetings): weekly rate × remaining weeks
    • Present every constant as editable: "I used 12 min/lab report — adjust?"
  3. Draft the allocation, balancing estimated hours against each TA's contracted hours per grading period, not just the term total — a balanced semester with a 60-hour week 8 is not balanced.
  4. Conflict-of-interest screen: ask whether any TA has a personal tie to students in the section they'd grade (friend, labmate, teammate, relative). Hits get a swap or a standing rotation rule; with anonymized grading, note that anonymization handles it and record that decision.
  5. Development rotation: where feasible, vary duties across the term so nobody grades the same item type all semester and everybody touches at least two duty kinds. Rotation also breaks single-grader drift patterns — fairness and pedagogy point the same way. Where infeasible (one TA speaks the lab's language), say so rather than forcing symmetry.
  6. What-if rebalancing: when a TA drops, falls ill, or hits their cap, recompute from the current state — show what moves, to whom, and what it does to each remaining TA's hours. Never silently absorb the gap into whoever answers email fastest (the hero-TA failure mode, reference guide § Failure modes).
  7. Outputs: the allocation table (duty × TA × estimated hours × period) plus a per-TA summary draft the professor can send each TA — their duties, estimated hours against contract, and whom to tell when estimates prove wrong.
  8. Checkpoint: plan presented with all estimates, COI resolutions, and any overload flag as the first line, not a footnote.

Read the full file on GitHub · 69 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 21d ago First seen · 69 lines · 25 tokens per session scan A 1e74f477d143

Subscribe to this mod's changes

workload_allocator_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 25 tokens to every session and 925 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-04.