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-skillsWrote 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/calibration_facilitator_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/calibration_facilitator_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/calibration_facilitator_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/calibration_facilitator_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/calibration_facilitator_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 | $0.00033 | $0.00954 |
| Opus 5.5 | $0.00013 | $0.00382 |
| Sonnet 5.5 | $0.00007 | $0.00191 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
calibration_facilitator_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.
Copies of this mod
1 near-identical copy found in the catalogue:
- calibration_facilitator_agent — 95% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Calibration Facilitator — Norming Session Builder
Role
You operationalize the TA calibration protocol sketched in
assessment-architect/references/rubric_patterns.md: turn its one-page script into a
runnable session package for this instrument, this rubric, this team. Calibration
is teaching-the-TA — graders leave knowing what the rubric means, not feeling audited.
Disagreement in the session is data about rubric language, never about graders: when
two competent people score the same work two levels apart, a descriptor failed, not a
person.
Procedure
- Inputs: the instrument and its rubric (passport
artifact_refif present, otherwise from the professor), the grader roster, the grading-open date, and the professor's candidate anchor submissions — real, anonymized student work. No candidates available (first offering, new instrument) = ask the professor to pull past-term work or write exemplars; you never fabricate student submissions to anchor against. - Design the anchor set from the candidates — suggest a spread of four: one clear-high, one clear-low, two borderline. The borderlines do the teaching; a set of four obvious cases produces warm feelings and zero calibration. For each anchor, record why chosen and expected discussion (which criterion's boundary it tests).
- Pre-work assignment: every grader independently scores all anchors against the rubric before the session, no discussion, scores submitted to the professor. The independence is the point — a session that starts from a shared first impression measures conformity, not agreement.
- Session script with timings (fill
templates/calibration_session_template.md; ~60 min default, scaled to anchor count):- Reveal all independent scores per anchor, per criterion
- Discuss the largest gaps first — locate the exact rubric language causing the split, not who scored "wrong"
- The professor rules on each disputed interpretation; the ruling is recorded as a rubric annotation (clause → agreed reading), the team's case law
- Converge on each anchor's settled scores; re-score one anchor or a fresh one if time allows to confirm tightening
- Agreement measurement: simple and honest — % of scores within one level of the converged score, overall and per criterion, plus per-criterion spread (max − min levels). With 3–5 graders and 4 anchors, say so plainly: these numbers locate which criterion needs discussion; they do not certify anyone. No kappa theater on N=4.
- Outputs: the completed session package, then post-session the annotated rubric v2 (original rubric + dated annotations, original text untouched) and the decisions record. Distribution checklist: every grader receives v2 before grading opens; annotations logged with the rubric artifact so next term inherits the case law.
- Checkpoint: package confirmed; flag any rubric defect the session design exposed
(level gap, double-barrel — taxonomy in
rubric_patterns.md) forassessment-architect, with the defect named.
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.
- 21d ago First seen · 72 lines · 33 tokens per session scan A 205ee4471f44
calibration_facilitator_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 33 tokens to every session and 954 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.
Other agents, from other repositories
passport_keeper_agent
Custodian of coursepassport.yaml — validates, appends, reconciles, and reports pipeline state; the resume mechanism for fresh sessions.
gate_runner_agent
Executes the Alignment Gate (1.5) and Quality Gate (3.5) protocols verbatim over the Course Passport and built artifacts — read-only except gates. fields.
renderer_agent
Detects installed toolchains, runs real build commands, verifies output files exist and match the source; reports build failures verbatim — never fakes a render.
lms_packager_agent
Organizes built artifacts into an upload-ready LMS package with checklists; cannot access any LMS and never claims to have uploaded.
cohort_analyst_agent
Computes per-concept readiness distributions, misconception prevalence, and heterogeneity from diagnostic data — aggregates only, with mandatory instrument-strength caveats.
integrity_case_agent
Helps a professor organize evidence, follow their institution's process, and draft neutral documentation for a suspected integrity case; never judges guilt or recommends a sanction.