Borrowing it
Nothing to install: this file belongs to tjmustard/Hypergraph-Coding-Agent-Framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tjmustard/Hypergraph-Coding-Agent-Framework/main/.agents/skills/hyper-learning-opportunity/SKILL.mdgit clone --depth 1 https://github.com/tjmustard/Hypergraph-Coding-Agent-FrameworkWrote 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/tjmustard/hypergraph-coding-agent-framework/hyper-learning-opportunity)<a href="https://agentmods.dev/skills/tjmustard/hypergraph-coding-agent-framework/hyper-learning-opportunity"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-learning-opportunity/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/tjmustard/hypergraph-coding-agent-framework/hyper-learning-opportunity"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-learning-opportunity.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 | $0.00040 | $0.00574 |
| Opus 5.5 | $0.00016 | $0.00230 |
| Sonnet 5.5 | $0.00008 | $0.00115 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
learning-opportunity 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 2mo 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Opportunity
This skill pauses development mode and shifts into teaching mode — delivering a concept at three increasing levels of complexity, peer-to-peer, without oversimplification.
When to use this skill
- When the user encounters something they don't fully understand and wants to learn it.
- When the user explicitly runs
/hyper-learning-opportunity [concept]. - When a development decision reveals a knowledge gap worth addressing.
When to use this skill
- When the user wants to understand a concept, pattern, or technology they encountered.
- When the user runs
/hyper-learning-opportunity [topic].
How to use it
Target Audience
Technical PM with mid-level engineering knowledge. Understands architecture, can read code, ships production apps. Not a senior engineer, but not a beginner. Apply the 80/20 rule — focus on concepts that compound. Don't oversimplify, but prioritize practical understanding over academic completeness.
Deliver Three Levels
Begin by using AskUserQuestion to check how deep the user wants to go:
What depth of explanation do you want?
- Option A: Conceptual overview (Level 1 only) — what it is and why it exists
- Option B: Practical walkthrough (Levels 1–2) — concepts + mechanics + trade-offs
- Option C: Full deep dive (all 3 levels) — including implementation details and senior-engineer perspective
After delivering each level, use AskUserQuestion before advancing:
Ready to continue to the next level?
- Option A: Yes, continue — move to the next level
- Option B: Explain more here — go deeper on this level before advancing
Level 1: Core Concept
- What this is and why it exists
- The problem it solves
- When you'd reach for this pattern
- How it fits into the broader architecture
Level 2: How It Works
- The mechanics underneath
- Key trade-offs and why this approach was chosen
- Edge cases and failure modes to watch for
- How to debug when things go wrong
Level 3: Deep Dive
- Implementation details that affect production behavior
- Performance implications and scaling considerations
- Related patterns and when to use alternatives
- The "senior engineer" perspective — what experienced devs know that beginners don't
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.
- 2mo ago First seen · 70 lines · 40 tokens per session scan A cf17b66ce45c
learning-opportunity is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 574 once invoked, about $0.0002 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 2026-08-31.
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