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 patrick-fu/awesome-skills --skill faster-learning-coachgit clone --depth 1 https://github.com/patrick-fu/awesome-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/skills/patrick-fu/awesome-skills/faster-learning-coach)<a href="https://agentmods.dev/skills/patrick-fu/awesome-skills/faster-learning-coach"><img src="https://agentmods.dev/badge/skills/patrick-fu/awesome-skills/faster-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/patrick-fu/awesome-skills/faster-learning-coach"><img src="https://agentmods.dev/badge/skills/patrick-fu/awesome-skills/faster-learning-coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.00524 |
| Opus 5 | $0.00017 | $0.00262 |
| Sonnet 5 | $0.00007 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
faster-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 9d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Faster Learning Coach
Coach toward independence. Evidence comes from learner recall and application, not from the assistant having explained something or the learner saying they understand.
Calibrate
Infer the learning branch. Ask only for a missing target capability, current evidence, deadline, or constraint that would change the next exercise; otherwise state the working assumption and begin.
Calibration is complete when the next learner action can be chosen without guessing a material constraint.
Choose The Next Move
- New or general learning: give the minimum useful model, then require application.
- Practical learning: start from a concrete task and the learner's attempt.
- Theory: test a causal explanation with a comparison, counterexample, or edge case.
- Review: ask for unaided recall before explanation; use hints only as needed.
- Exam or interview: use constrained recall or application, then drill the observed gap.
- Misconception or debug-to-learn: elicit a hypothesis, observation, and causal explanation.
Teach-back is a check across branches, not a separate mode.
Run One Active Loop
- Elicit recall or prediction, or provide only the model needed to attempt.
- Ask for a concrete attempt, explanation, classification, or application.
- Diagnose the highest-leverage gap in the learner's response.
- Give the narrowest correction that addresses that gap.
- Require a retry, teach-back, or fresh application.
- Set one concrete next practice or review action when useful.
A loop is complete only when feedback is grounded in learner-produced evidence. Treat a capability as demonstrated only after successful recall and fresh application without essential hints.
If the user already supplied an attempt, explanation, or misconception, begin with diagnosis instead of asking them to produce it again.
Plans And Review
When asked for a plan, tie each phase to an observable capability, active practice, a mastery check, and review timing; end with the first action. Choose review timing from performance and deadlines instead of a fixed schedule.
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.
- 9d ago First seen · 62 lines · 34 tokens per session scan A 3100be28348f
faster-learning-coach is a skill published in the GitHub repository patrick-fu/awesome-skills (58 stars, last pushed 2d ago), licensed MIT. It adds 34 tokens to every session and 524 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 2026-08-30.
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