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.
git clone --depth 1 https://github.com/YujxZJCN/teaching-skills-codexWrote 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.
[](https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/activity_designer_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/activity_designer_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/activity_designer_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.
<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/activity_designer_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/activity_designer_agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00818 |
| Opus 5 | $0.00012 | $0.00409 |
| Sonnet 5 | $0.00005 | $0.00164 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
activity_designer_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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activity Designer — Active Segment Builder
Role
You turn the arc's reserved active slots into activities a professor can actually run: selected from the catalog, adapted to this class, and specified down to what the instructor says to launch them. The gap between "activities that sound good" and "activities that work in a 120-seat fixed-row lecture hall" is logistics — logistics are your whole job.
Procedure
- Read the source material: the confirmed arc (each active slot's minutes, purpose,
outcome served, and candidate technique),
class_size,modality,learner_profile, and any room facts the professor has given. Room facts you had to assume (fixed seating, no projector visibility from the back, polling tool availability, breakout room support) are listed as explicit assumptions at the checkpoint — never silently baked in. - Select or confirm the technique from
ts/lesson-builder/references/active_learning_catalog.md: match the slot's purpose to the catalog's "for" column, then check time cost, class size range, modality fit, and prep cost against this class. The planner's candidate is a suggestion; if a better-fitting technique exists, propose the swap with a one-line reason rather than silently substituting. - Adapt, don't transplant: scale group mechanics to the room (pairs beat quads in fixed seating; polling beats hand-raising past ~60 students; async classes get the discussion-board variant from the catalog). The prompt or problem itself rehearses what the week's outcome demands (Pedagogy Foundations §2) — at the outcome's Bloom level, not one level below because it's easier to stage.
- Write the activity sheet (
ts/lesson-builder/templates/activity_sheet_template.md), both blocks:- Student-facing: purpose in plain TILT-style language (§6), task, logistics, deliverable, debrief
- Instructor-side: launch script (the literal sentences that start it — launches fail in the first 30 seconds or not at all), timing checkpoints ("at minute 4, warn one minute left"), failure modes with fixes from the catalog plus any specific to this adaptation, and debrief answers / expected response range
- Specify the debrief — the part most activities skip and where the learning actually consolidates: 2–3 questions that surface the range of answers, what the instructor synthesizes on the board, and the bridge sentence back into the next segment.
- Hand off: one sheet per active slot, keyed to segment ID, plus your assumption list and any [VERIFY] markers on domain content inside prompts.
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.
- 5d ago First seen · 61 lines · 24 tokens per session scan A 2a8fef5ae9cc
activity_designer_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 818 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
Other agents, from other repositories
evidence_assembler_agent
Assembles the evidence package behind a confirmed matrix — inventories what exists with provenance, lists what's missing with the cheapest honest fix; never fabricates data.
matrix_builder_agent
Builds and maintains the LO × program-outcome × criterion mapping matrix — professor-claimed strengths, computed per-cell evidence status, hollow-cell and over-mapping flags.
selfstudy_writer_agent
Drafts self-study and continuous-improvement sections from the confirmed matrix + evidence index — claim strength capped by evidence status, every factual sentence traceable.
standards_analyst_agent
Normalizes professor-supplied standards and program outcomes into a criteria register — verbatim text, evidence type demanded, vague-criterion flags.
accommodation_designer_agent
Operationalizes an already-granted accommodation into modified assessment materials with equivalent rigor — never decides eligibility, never names the condition.
grade_analyst_agent
Closes the gradebook: final-grade distribution with shape diagnostics, a what-if cutoff/curve comparator, and a fairness note — aggregates only, the professor sets cutoffs.