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 skills add holon-run/holon --skill learning-coachgit clone --depth 1 https://github.com/holon-run/holonWrote 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/skills/holon-run/holon/learning-coach)<a href="https://agentmods.dev/skills/holon-run/holon/learning-coach"><img src="https://agentmods.dev/badge/skills/holon-run/holon/learning-coach/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/skills/holon-run/holon/learning-coach"><img src="https://agentmods.dev/badge/skills/holon-run/holon/learning-coach.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 | $0.00037 | $0.01230 |
| Opus 5.5 | $0.00015 | $0.00492 |
| Sonnet 5.5 | $0.00007 | $0.00246 |
| Haiku 4.5 | $0.00004 | $0.00123 |
Grade A, and why
learning-coach 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 4d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Coach
Use this skill when the task is to help one learner build and demonstrate understanding over multiple sessions. The core loop is:
goal → baseline → small lesson → independent attempt → feedback → transfer → review.
The learner's independent work is the source of truth. Content read, time spent, confidence, and text generated by the agent are useful context but are not mastery evidence.
Durable record
If the learner authorizes a workspace, keep a small file-first record under a
confirmed directory such as learning/:
learning/
README.md
goals/<goal-slug>.md
sessions/YYYY-MM-DD-<goal-slug>.md
review-queue.md
Do not create these files until the learner confirms the workspace and privacy boundary. If persistence is declined, state that the plan is session-scoped.
learning/README.md records the learner's chosen scope, privacy setting,
review cadence, and active goals. Each goal page records:
# <Goal>
- Status: active | paused | completed
- Subject mode: language | mathematics | programming | reading | project | custom
- Outcome: observable capability, not a reading list
- Evidence rubric: what counts as an independent demonstration
- Prerequisites: known | unknown | needs_check
- Current mastery: unknown | emerging | demonstrated | stale
- Next review: YYYY-MM-DD or trigger
## Misconceptions and open questions
## Evidence log
- YYYY-MM-DD — learner attempt, result, feedback, and confidence limits
Use unknown when there is no attempt. Use emerging when the learner can
perform with substantial support or has inconsistent evidence. Use
demonstrated only after an independent attempt meets the rubric. Use stale
when previous evidence needs a fresh retrieval or transfer check.
learning/review-queue.md contains only actionable items:
- [ ] YYYY-MM-DD — <goal>: retrieve/explain/solve/build <small target>
- Trigger: <date, interval, or observed misconception>
Keep evidence append-only where practical. Correct factual errors explicitly instead of rewriting history. Never put secrets, credentials, or sensitive personal data in the learning record unless the learner specifically confirms that need and the workspace is appropriate.
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.
- 4d ago First seen · 154 lines · 37 tokens per session scan A 4c605945fa23
learning-coach is a skill published in the GitHub repository holon-run/holon (166 stars, last pushed yesterday), licensed Apache-2.0. It adds 37 tokens to every session and 1,230 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 2026-10-12.
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