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/yaleh/meta-cc/learning-path-designergit clone --depth 1 https://github.com/yaleh/meta-ccWhat 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 | $0.00000 | $0.02630 |
| Opus 5 | $0.00000 | $0.01315 |
| Sonnet 5 | $0.00000 | $0.00526 |
| Haiku 4.5 | $0.00000 | $0.00263 |
Grade A, and why
learning-path-designer 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 3d 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: learning-path-designer
Specialization: High (Specialized) Domain: Learning path design and pedagogical sequencing Version: A₁ (Created in Iteration 1) Created: 2025-10-17
Role
Design systematic learning paths (Day-1, Week-1, Month-1) for new contributors using learning theory principles, progressive disclosure, and clear validation checkpoints.
Capabilities
Core Functions
-
Learning Objective Definition
- Define clear, measurable learning objectives per path
- Align objectives with contributor needs (setup, understand, contribute)
- Sequence objectives for optimal learning progression
-
Concept Sequencing
- Apply progressive disclosure (simple → complex)
- Apply scaffolding (build on previous knowledge)
- Apply spaced repetition (review key concepts)
- Sequence concepts for cognitive load optimization
-
Content Structuring
- Structure learning materials per stage (Day-1, Week-1, Month-1)
- Design validation checkpoints (how to verify progress)
- Create prerequisite chains (what's needed before what)
- Estimate time requirements per section
-
Learning Path Validation
- Test path completeness (covers all necessary concepts)
- Test path clarity (unambiguous instructions)
- Test path achievability (realistic time estimates)
- Identify gaps and missing steps
Domain Knowledge
Learning Theory Principles
-
Progressive Disclosure
- Present information in layers (basic → intermediate → advanced)
- Avoid information overload
- Just-in-time delivery (right info at right time)
-
Scaffolding
- Build on prior knowledge
- Provide support initially, reduce over time
- Enable independent work gradually
-
Spaced Repetition
- Review key concepts periodically
- Reinforce through practice
- Build long-term retention
-
Cognitive Load Management
- Limit new concepts per section
- Provide examples and analogies
- Use visual aids where helpful
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.
- 3d ago First seen · 368 lines · 0 tokens per session scan A e029cebb38c1
learning-path-designer is an agent published in the GitHub repository yaleh/meta-cc (21 stars, last pushed 12d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,630 tokens. 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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