tutorial-run

An interactive guide for working through tutorials generated by Hyper Tutorial Generator. It reads the tutorial section by section and can show linked code samples and output files when requested.

In plain words
What is it for?
Choosing a tutorial, reading its sections, answering questions about the material, and accessing related code samples, expected outputs, and architecture diagrams.
Why use it?
It gives learners a guided way to read a tutorial and ask questions in context instead of opening many files separately.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/tjmustard/hypergraph-coding-agent-framework/hyper-tutorial-run
Any agent
npx skills add tjmustard/Hypergraph-Coding-Agent-Framework --skill hyper-tutorial-run
Clone the repo
git clone --depth 1 https://github.com/tjmustard/Hypergraph-Coding-Agent-Framework

Made for: Claude Code, Codex.

Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 883 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00043 $0.00883
Opus 5 $0.00022 $0.00441
Sonnet 5 $0.00009 $0.00177
Haiku 4.5 $0.00004 $0.00088

Measured yesterday against content hash bc003d113eb0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

tutorial-run 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.

.agents/skills/hyper-tutorial-run/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tutorial Run

This skill reads any tutorial produced by /hyper-tutorial-generator and walks the user through it interactively — one section at a time, with in-context Q&A and on-demand access to code samples and supporting files.


When to use this skill

  • When the user runs /hyper-tutorial-run and wants to work through an existing tutorial.
  • When the user wants an interactive guided read (not a plain file open) of a tutorials/<name>/ folder.

How to use it

Step 0 — Choose a tutorial (HITL Gate #1)

List all subdirectories of tutorials/ (numbered). If the directory does not exist or is empty, inform the user and suggest running /hyper-tutorial-generator first.

Use AskUserQuestion to let the user select which tutorial to run. Wait for the selection before proceeding.


Step 1 — Load the tutorial

  1. Read tutorials/<name>/tutorial.md in full.
  2. Extract the section headings to build a section list (use ## or ### level headings).
  3. Scan for available supporting subdirectories and note what's present:
    • code_samples/ — runnable code examples
    • output_files/ — expected outputs
    • architecture_evolution/ — diagrams and comparison tables
    • input_files/ — setup or example input files
  4. Display a brief ready message:
Ready to begin: <Tutorial Title>
Sections: N
Supporting files: code_samples/ (X files), output_files/ (Y files)

Let's start with Section 1.

Step 2 — Walk through sections (iterative HITL loop)

For each section in the tutorial:

  1. Present the full section content as plain text (render markdown headers and lists).

  2. Offer options via AskUserQuestion:

    Section <N>/<Total>: <Section Title>
    
    Options:
    - Option A: Next section
    - Option B: I have a question about this section
    - Option C: Show me the code sample referenced in this section
    

    Only show Option C if the section references a file in code_samples/.

  3. Handle each response:

    • Option A — move to the next section. If this was the last section, go to Step 3.
    • Option B — answer the question using the tutorial content and your knowledge of the project. Do not move forward. Re-present the same section prompt after answering.
    • Option C — identify the referenced code_samples/ file from the section text, read it in full, and display it. Then re-present the same section prompt.

Read the full file on GitHub · 102 lines

Changes

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.

  1. yesterday First seen · 102 lines · 43 tokens per session scan A bc003d113eb0

Subscribe to this mod's changes

tutorial-run is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 883 once invoked, about $0.0002 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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