Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PranitMohnot/repo-learner-suitenpx agentmods add skills/pranitmohnot/repo-learner-suite/exercise-genWrote 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/pranitmohnot/repo-learner-suite/exercise-gen)<a href="https://agentmods.dev/skills/pranitmohnot/repo-learner-suite/exercise-gen"><img src="https://agentmods.dev/badge/skills/pranitmohnot/repo-learner-suite/exercise-gen/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/pranitmohnot/repo-learner-suite/exercise-gen"><img src="https://agentmods.dev/badge/skills/pranitmohnot/repo-learner-suite/exercise-gen.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 | $0.00143 | $0.05326 |
| Opus 5 | $0.00072 | $0.02663 |
| Sonnet 5 | $0.00029 | $0.01065 |
| Haiku 4.5 | $0.00014 | $0.00533 |
Grade A, and why
exercise-gen 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 — 537 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exercise Generator
Produce scaffolded Jupyter notebooks that teach a codebase through hands-on exercises. Each notebook is self-contained, runnable on its own, and progresses from guided usage through independent creation.
This skill is manifest-driven. The manifest at
learn/internals/exercise-plan.md is the source of truth for which exercises
exist, where they slot into curriculum.md, and how each is rendered. Read the
Shared Contracts in repo-learner/SKILL.md before starting.
Read references/exercise-types.md for the exercise taxonomy (use / modify /
debug / create / compare) and when to use each. Read
references/subagent-brief-template.md before fanning out to subagents.
Notebook-first, with narrow inline exceptions
Default emission is .ipynb. Two narrow exceptions allowed:
compareexercises — table or side-by-side prose comparisons fit naturally inline.- Short copy-paste-run blocks — fewer than ~15 lines, no scaffold, no validation, no solution. Pure "run this and observe."
Everything else is a notebook. The long-term direction is "everything becomes a notebook" — treat inline as an exception you should be reluctant to use, not a co-equal option.
Pipeline
Stages 1–2 produce the manifest (if it doesn't already exist). Stages 3–5 consume it.
Stage 1: Candidate Mining
Scan the codebase to identify "exercisable surfaces" — places where a learner can do something concrete that builds understanding.
What to scan for:
- Public API functions with clear I/O. Natural "use" exercises.
- Configuration and setup patterns. "Set up and configure" exercises.
- Extension points. Subclass / register / implement — the best exercises, because they force understanding of the abstraction.
- Workflows visible in examples/tests. The authors already wrote these — extract the pattern, strip to skeleton.
- Error-handling paths and common mistakes. "Debug" exercises.
- Comparison points. Two ways to do the same thing → "compare" exercises.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 537 lines · 143 tokens per session scan A eb24f310f530
exercise-gen is a skill published in the GitHub repository PranitMohnot/repo-learner-suite (4 stars, last pushed 3mo ago), licensed MIT. It adds 143 tokens to every session and 5,326 once invoked, about $0.0007 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 2026-08-31.
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