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-init/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-init)<a href="https://agentmods.dev/skills/tjmustard/hypergraph-coding-agent-framework/hyper-init"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-init/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-init"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-init.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 | $0.00035 | $0.00681 |
| Opus 5.5 | $0.00014 | $0.00272 |
| Sonnet 5.5 | $0.00007 | $0.00136 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
hyper-init 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 1mo 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hyper-Init (Repository Scaffolding Agent)
This skill initializes a standard repository structure for a new project within the Hypergraph framework. It interviews the user to collect key variables and then templates out standard repository files (README, CHANGELOG, CONTRIBUTING, etc.).
When to use this skill
- When the user starts a new project and needs to scaffold standard documentation.
- When the user explicitly runs
/hyper-init.
Step 1 — Interview the User
Use the AskUserQuestion tool to collect the following metadata variables. Ask the questions sequentially or in one batch, but do not proceed until you have all of them:
{{PROJECT_NAME}}: The human-readable name of the project (e.g., "Google Docs Annotator").{{REPO_NAME}}: The machine-readable repository name (e.g., "mcp-gdoc-annotated").{{GITHUB_USERNAME}}: The GitHub username or organization name.{{AUTHOR_NAME}}: The author's full name.{{AUTHOR_EMAIL}}: The author's email address.{{PROJECT_DESCRIPTION}}: A short, 1-2 sentence summary of what the project does.
Step 2 — Read Templates
Once the variables are collected, read the generic templates from the .agents/schemas/project-templates/ directory:
CHANGELOG.mdCITATION.cffCODE_OF_CONDUCT.mdCONTRIBUTING.mdDEVELOPMENT.mdREADME.mdSECURITY.mdpyproject.toml
Step 2b — Scaffold pyproject.toml
Read .agents/schemas/project-templates/pyproject.toml and substitute:
{{REPO_NAME}}→ the machine-readable repository name from Step 1{{PROJECT_DESCRIPTION}}→ the project description from Step 1{{PACKAGE_NAME}}→{{REPO_NAME}}with hyphens replaced by underscores (e.g.,my-project→my_project)
Write the result to pyproject.toml in the project root.
Then run:
uv add --dev ruff
uv add --dev pytest
This installs the required dev tooling and generates uv.lock.
Step 3 — Template Replacement and Output
For each of the 7 documentation files (not pyproject.toml — already handled above):
- Replace all placeholder variables (e.g.,
{{PROJECT_NAME}}) with the user's provided answers. - Use the Write File tool to save the modified content directly to the project's root directory (e.g.,
/README.md,/SECURITY.md).
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.
- 1mo ago First seen · 80 lines · 35 tokens per session scan A 8ec0112069dd
hyper-init is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 681 once invoked, about $0.0001 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 2026-09-28.
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