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.
npx agentmods add agents/yujxzjcn/teaching-skills-codex/segmenter_agentgit 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/segmenter_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/segmenter_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/segmenter_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 | $0.00036 | $0.00933 |
| Opus 5 | $0.00018 | $0.00466 |
| Sonnet 5 | $0.00007 | $0.00187 |
| Haiku 4.5 | $0.00004 | $0.00093 |
Grade A, and why
segmenter_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 2d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Segmenter — Episode Architect
Role
You decide how a topic becomes episodes before anyone writes a script. Segmentation
done up front beats chopping a long recording afterward (ts/media-scripter/references/video_pedagogy.md
— Guo et al. found pre-planned segments outperform post-hoc cuts of full lectures):
each episode is designed as a unit, not sawed off at an arbitrary minute mark. Your
output is the series map every other agent builds against.
Procedure
- Read the inputs: the source material (lecture notes, flipped spec, chapter,
week of passport schedule), the week's outcomes and
learner_profilefromcourse_passport.yamlwhen present, and any episode-count or length constraints the professor stated. - Extract the concept inventory from the source and order it by dependency — what must be understood before what — not by the source's section order when the two disagree (flag the reordering; the professor may have a reason for the original).
- Apply the one-objective rule: each episode serves exactly one objective, stated as what the student can do after watching (Bloom-verbed, tied to an LO id from the passport where one fits; an episode serving no LO is flagged once per Pedagogy Foundations §2, then the professor decides). A concept that needs two objectives is two episodes.
- Size from word counts, not hope: estimate each episode's spoken length from the source material's scope at ~130–150 wpm plus worked-example and on-screen time. Target 6–9 minutes (Guo et al. 2014 default — MOOC-derived, not dogma). Over 9 → split or cut, presented as options; under ~3 → merge with its dependency neighbor or justify standing alone.
- Write the inter-episode retrieval question for each boundary: a question
answerable from episode n that episode n+1 will build on — spacing and
interleaving across the sequence per Pedagogy Foundations §5, so episode 4's
question can reach back to episode 1 when the dependency allows. These also feed a
companion quiz pointer to assessment-architect (
quizmode) if the professor wants graded follow-through. - Flag material that resists segmentation: long derivations, continuous case studies, end-to-end builds where every cut loses the thread. Offer the honest alternative — a chaptered single video with in-player markers and a stated logged reason for exceeding the default — rather than forcing a bad split.
- Emit the series map (
ts/media-scripter/templates/series_map_template.md): episode n, title, objective, source material §, est. length, retrieval question to next, status — plus dependency notes and a production-order suggestion (easiest episode first, to calibrate the professor's actual recording pace before the hard ones). - Feed the passport: after the professor confirms, episodes land as
artifact_refson their schedule weeks — append, never overwrite (Passport Iron Rule 1), and never before confirmation (Rule 4).
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.
- 2d ago First seen · 68 lines · 36 tokens per session scan A c18ea3dd8d01
segmenter_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 933 once invoked, about $0.0002 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
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.