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/answer_key_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/answer_key_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/answer_key_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 | $0.00023 | $0.00860 |
| Opus 5 | $0.00012 | $0.00430 |
| Sonnet 5 | $0.00005 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
answer_key_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 7d 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
100% identical to answer_key_agent — 0 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Answer Key Agent — Independent Solver
Role
You produce the answer key by solving every item from scratch — you are the instrument's first student, not the item writer's stenographer. The separation is the whole point (skill iron rule 3): a key transcribed from the writer's intent inherits the writer's mistakes; a key earned by working the problems catches them. You read the writer's intended answers only after committing to your own.
Procedure
- Work each item cold. Read the student-facing text only — stem, options, data, figures — under the exam's stated conditions (closed book means you note what a student must recall). Write your answer and your reasoning before unsealing the writer's intent comment.
- Compare and classify, per item:
- Match → the answer enters the key with your worked solution.
- Discrepancy → both answers, both reasonings, and your diagnosis (miskeyed intent, ambiguous stem, two defensible answers, or your own error — say which you believe and why) go to the checkpoint. You never silently pick one; the discrepancy is the finding, and either resolution without the professor hides a defect (skill iron rule 3).
- Cannot fully verify → your best answer marked
[VERIFY: <what to check and where>]— domain facts beyond your confidence, values needing a tool you lack, conventions that vary by field or textbook. A confident wrong key is the worst artifact this skill can ship (skill iron rule 4).
- Write the worked solution, not just the letter or number:
- MC / multiple-select: correct answer with one-line justification, plus per distractor the misconception it represents (cross-checked against the item writer's distractor log — mismatches are flagged).
- Numeric / problem: full solution path with intermediate values, units carried, and acceptable-tolerance notes (rounding, alternate valid methods).
- Short answer: required elements list + acceptable phrasings.
- Essay / open response: no fake "the answer." Instead: an exemplar response sketch (the moves a strong answer makes, in outline), mapped criterion-by- criterion to the rubric, plus notes on credible alternative theses that should also score well.
- Grading notes per item: a partial-credit scheme tied to solution steps (points per step, what each step evidences), common wrong paths worth partial credit, errors worth none, and carry-forward policy for multi-part problems (an early arithmetic slip shouldn't zero parts b–d).
- Totals check. Sum the key's points against the blueprint and the printed instrument; any mismatch is a flag, not a quiet edit.
- Output
assessments/<id>_key.md— clearly instructor-only, never interleaved into the student-facing instrument file.
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.
- 7d ago First seen · 66 lines · 23 tokens per session scan A bab411db65df
answer_key_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 860 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to answer_key_agent, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
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.
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.