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 skills/tjmustard/hypergraph-coding-agent-framework/hyper-tutorial-auditnpx skills add tjmustard/Hypergraph-Coding-Agent-Framework --skill hyper-tutorial-auditgit clone --depth 1 https://github.com/tjmustard/Hypergraph-Coding-Agent-FrameworkWhat 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 | $0.00063 | $0.01619 |
| Opus 5 | $0.00032 | $0.00809 |
| Sonnet 5 | $0.00013 | $0.00324 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
hyper-tutorial-audit 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 yesterday.
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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tutorial Audit
This skill is designed to run after /hyper-tutorial-run in the same conversation. It reads the tutorial, reviews what failed during the walk-through, interviews the user about their goal and constraints, and generates a structured goal prompt that can be pasted at the start of a fresh Claude Code session to drive an automated fix loop.
Do not re-run tutorial commands. This skill is read-only and interview-driven. The fix work happens in the new session the goal prompt creates.
Step 1 — Locate Tutorial
- If an argument was passed (tutorial name or path), resolve
tutorials/<name>/tutorial.md. - Otherwise, scan the current conversation for mentions of a tutorial name or path from a recent
/hyper-tutorial-runsession and use that. - If still ambiguous, list all
tutorials/*/tutorial.mdfiles found in the project. Use AskUserQuestion to let the user select one.
Step 2 — Read Tutorial
Read the full tutorial.md. Extract and hold in memory:
- Title (from the
#heading) - Overview / learning objective (the first paragraph or
## Overviewsection) - All section headings (
##) with their ordinal numbers - Any prerequisite declarations (package names, versions, tools)
Step 3 — Review Conversation Context
Scan the current conversation (everything visible, not just recent messages) for evidence from the /hyper-tutorial-run session:
- Which sections the user reached or attempted
- Error messages, stack traces, or failure descriptions that appeared in chat
- Steps the user skipped, flagged as broken, or noted needed help
Build an internal summary of failures — do not ask the user to re-describe or re-paste anything already visible in context.
Step 4 — Interview: Goal (HITL Gate #1)
Use AskUserQuestion with at most 2 questions:
Question 1 — What are you trying to accomplish? (multi-select)
- Learn the concept the tutorial teaches
- Get the feature working in my project
- Use as reference for a future task
- Reproduce a bug or a demo
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.
- yesterday First seen · 166 lines · 63 tokens per session scan A 874fa840ce52
hyper-tutorial-audit is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,619 once invoked, about $0.0003 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.
Other skills, from other repositories
article-writing
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
a-evolve
Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on…
hive.chart-creation-foundations
Required reading whenever any chart tool is available. Teaches the one-tool embedding contract (call chartrender → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no…
📝 任务完成后归档
重要提醒: 每次完成复杂调试或开发任务后，主动执行此流程！ 将学到的经验归档为 skill，供以后参考。不要等用户提醒。.
deck-course-module
暖纸背景 + Playfair, 左侧学习目标常驻, 含 MCQ 自测页.
code-documenter
Use when adding docstrings, creating API documentation, or building documentation sites. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, tutorials, user guides.