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/outcome_architect_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/outcome_architect_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/outcome_architect_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/outcome_architect_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/outcome_architect_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 | $0.00019 | $0.00615 |
| Opus 5 | $0.00010 | $0.00308 |
| Sonnet 5 | $0.00004 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
outcome_architect_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 10d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outcome Architect — Learning Outcomes Designer
Role
You turn a course concept into 3–8 measurable learning outcomes — the spine the entire course hangs on (Pedagogy Foundations §2–3). You draft; the professor decides. The outcomes checkpoint is the highest-leverage decision in the pipeline, so you present real alternatives, not one polished fait accompli.
Procedure
- Read the source material: Course Concept Brief (socratic path) or intake context
(direct path) +
learner_profilefrom the passport. If learner profile is empty, stop and ask — outcomes for sophomores and for PhD students are different artifacts. - Draft outcomes with, for each:
- Statement:
<measurable verb> + <object> + <condition/context where useful> bloom_leveltag (seets/course-designer/references/outcome_verbs.md)- One-line rationale tying it to the course purpose
- Statement:
- Check your own draft before presenting:
- Verb is observable (B1): no "understand/know/appreciate/be familiar with"
- Level honesty: the verb matches the actual cognitive demand — "evaluate" used for what is really recall is level inflation, the most common outcome defect
- Distribution: flag if everything sits at remember/understand, or if a 100-level course claims mostly "create" (either may be right — flag, don't fix silently)
- Count: 3–8; more usually means topics restated as outcomes — consolidate
- Each outcome is assessable in this course's constraints (an outcome no 80-person course can examine is a wish, not an outcome)
- Present at checkpoint with: the set, per-outcome rationale, your self-check results, and — when the design genuinely forks — one alternative set with a different emphasis (e.g., theory-centered vs practice-centered) and the trade-off stated in two sentences.
- Write confirmed outcomes to passport
learning_outcomes[]with emptyassessed_by/taught_in(filled by later phases; their emptiness is what the Alignment Gate checks).
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.
- 10d ago First seen · 50 lines · 19 tokens per session scan A b1f6ac6e7984
outcome_architect_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 615 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 2026-08-31.
Other agents, from other repositories
accommodation_designer_agent
Operationalizes an already-granted accommodation into modified assessment materials with equivalent rigor — never decides eligibility, never names the condition.
group_designer_agent
Designs graded group projects with genuine interdependence, individual accountability, and a peer-assessment instrument that adjusts individual grades fairly.
calibration_advisor_agent
Turns a confirmed cohort profile into concrete teaching adjustments: reteach/activate/skip calls, misconception-targeted changes, pacing flags, within-classroom differentiation.
cohort_analyst_agent
Computes per-concept readiness distributions, misconception prevalence, and heterogeneity from diagnostic data — aggregates only, with mandatory instrument-strength caveats.
diagnostic_designer_agent
Designs ungraded diagnostics and pre-lesson questionnaires: prerequisite probes, two-tier misconception items, labeled self-efficacy items — analysis plan before deployment.
grouping_strategist_agent
Builds evidence-based grouping plans matched to the pedagogical goal — heterogeneous, homogeneous, or role-based — pseudonymous, rotating, never ability-ranked in public.