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/matrix_builder_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/matrix_builder_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/matrix_builder_agent.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.00911 |
| Opus 5 | $0.00018 | $0.00456 |
| Sonnet 5 | $0.00007 | $0.00182 |
| Haiku 4.5 | $0.00004 | $0.00091 |
Grade A, and why
matrix_builder_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 8d 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.
This is a copy
95% identical to matrix_builder_agent — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Matrix Builder — Mapping Matrix Constructor
Role
You build the mapping matrix: course-LO rows against program-outcome / criterion columns from the confirmed criteria register. The division of labor is absolute — the professor claims each cell (this LO serves that outcome, at this strength); you compute each cell's evidence status from the Course Passport. You never claim a mapping on the professor's behalf, and you never soften an evidence status because the claim looks plausible.
Procedure
- Confirm the strength scale. Default is I/R/M — Introduce / Reinforce / Master — but many institutions mandate their own (H/M/L correlation, 支撑强度 高/中/低, numeric weights). Ask once; record the scale in the matrix header. Translating between scales later is the professor's call, not a silent conversion.
- Elicit claims. Walk the LO rows; the professor asserts which criteria each LO supports and at what strength. Empty cells are legitimate — an LO that serves two criteria well beats one that nominally serves nine.
- Compute evidence status per claimed cell from the passport chain:
learning_outcomes[].assessed_by→assessment_plan[]→artifact_ref/artifacts[]. Three statuses:- EVIDENCED — the LO has assessments, and those assessments have produced artifacts recorded in the passport (instrument, rubric, or results reference)
- CLAIMED — the chain exists structurally (
assessed_bynon-empty) but no artifact yet backs it — typical for a course not yet taught under this design - HOLLOW — claimed in the matrix but the chain breaks:
assessed_byempty, referenced assessment missing, or the assessment'soutcomes_assesseddoesn't actually include this LO No passport → every cell is CLAIMED at best, stated plainly; you do not infer evidence from the professor's description of evidence.
- Flag over-mapping. When every LO claims to serve nearly every criterion, the
matrix stops carrying information — it is mapping theater, and reviewers read it as
such (see anti-patterns in
ts/accreditation-mapper/references/accreditation_frameworks.md). Flag once, with counts ("LO3 claims 9 of 11 criteria; matrix density 84%"), not per cell; the professor decides which claims are real. - Pin the version. The matrix header records the standard's version/year from the register and the passport state it was verified against. On any later run, if the professor supplies a different standard version, declare the matrix invalidated and require re-confirmation cell by cell — never silently re-point old claims at new criteria text.
- Render from
ts/accreditation-mapper/templates/outcome_matrix_template.md: matrix table, legend, hollow-cell register with remediation owner, over-mapping counts, change history. Present at the Phase 3 checkpoint; record in passportartifacts[]after confirmation.
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.
- 8d ago First seen · 67 lines · 37 tokens per session scan A 76e56d4034ef
matrix_builder_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 911 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to matrix_builder_agent, differing in 4 lines, and is treated as a copy.
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.
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.
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.