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 yugash007/edu-agent-skills --skill challenge-generatorgit clone --depth 1 https://github.com/yugash007/edu-agent-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/yugash007/edu-agent-skills/challenge-generator)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/challenge-generator"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/challenge-generator/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/yugash007/edu-agent-skills/challenge-generator"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/challenge-generator.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 | $0.00023 | $0.00745 |
| Opus 5 | $0.00012 | $0.00373 |
| Sonnet 5 | $0.00005 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
challenge-generator 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 11d 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.
Purpose
Generate targeted, level-appropriate practice challenges that force application over recall. Challenges must be grounded in learner's weak areas, current project, and chosen difficulty tier.
Activation
- Concept just taught and practice needed. Learner asks for exercises/challenges.
check-understandingormisconception-detectorflagged a weak area. Interview/exam/milestone prep. - Skip if: no concept context established → run
teach-conceptfirst. Learner is blocked on production issue →debug-teacher. - Routing: pair with
check-understandingto evaluate responses. Escalate tointerview-modefor timed pressure practice.
Inputs
- Target concept(s), learner level, known weak areas, project/repo context, preferred type (implement/debug/explain/design).
Challenge Types
- Implement: write code from scratch to satisfy criteria.
- Debug: identify and fix a deliberately broken snippet.
- Explain: articulate behavior, tradeoffs, or mechanism in prose.
- Design: propose architecture or algorithm for given constraints.
Difficulty Tiers
- Beginner: one concept, well-defined, limited scope.
- Intermediate: composite concepts, partially specified, tradeoff thinking required.
- Advanced: ambiguous spec, production constraints, edge-case awareness required.
Workflow
- Calibrate — Identify target concept(s) from session history or learner request. Select challenge type based on learning objective. Map learner level to difficulty tier.
- Construct — State challenge clearly: context, constraints, success criteria, time/scope hint. Include starter scaffold where appropriate. Embed at least one non-obvious constraint testing deeper understanding.
- Hint Ladder — Prepare 2–3 progressive hints (broad→specific) but don't volunteer them. Release only on request or after two failed attempts.
- Evaluate — Grade reasoning quality, not just correctness. Identify what's right, where reasoning broke down, root cause. Classify: conceptual gap, implementation slip, or edge-case blindness.
- Advance or Retry — Significant errors: simpler variant or targeted hint, then retry. Clean pass: increase tier or shift to next weak area.
- Reinforce — Summarize the key insight the challenge surfaced. Record outcome for
weak-area-tracker.
What ships with it
2 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.
- 11d ago First seen · 68 lines · 23 tokens per session scan A 776390976944
challenge-generator is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 745 once invoked, about $0.0001 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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