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-execute/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-execute)<a href="https://agentmods.dev/skills/tjmustard/hypergraph-coding-agent-framework/hyper-execute"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-execute/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-execute"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-execute.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 | $0.00038 | $0.00985 |
| Opus 5.5 | $0.00015 | $0.00394 |
| Sonnet 5.5 | $0.00008 | $0.00197 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
execute 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execute
This skill implements a plan precisely, minimally, and in full — adhering to existing conventions, updating progress tracking, and ensuring the hypergraph is kept in sync after every modification.
When to use this skill
- When the user has a compiled
MiniPRD_*.mdinspec/compiled/and is ready to build. - When the user explicitly runs
/hyper-executeor asks to "implement the plan." - After
/hyper-resolvehas compiled the final MiniPRDs and the workspace has been archived.
How to use it
-
Memory Check
-
Read
.agents/memory/activeContext.mdbefore anything else. -
Check whether the target MiniPRD is listed as complete or already audited.
-
If the MiniPRD is marked complete or audited: HALT. Display the warning, then use AskUserQuestion:
⚠️ [MiniPRD name] appears already complete per activeContext.md. Run /hyper-audit to archive it, or move it manually: mv spec/compiled/[MiniPRD].md spec/archive/[MiniPRD]_AUDITED.md Confirm you want to re-implement it? - Option A: Yes, re-implement — proceed even though it appears done - Option B: No, stop — I will archive or review it instead
-
-
Read the MiniPRD
-
Read the target
MiniPRD_*.mdfromspec/compiled/. -
Confirm a Confidence Score (1–10) against the MiniPRD's "Confidence Mandate" section. If below 9, list the clarifying questions, then use AskUserQuestion:
Confidence is [N]/10. How do you want to proceed? - Option A: Answer my questions — provide clarifications so I can proceed - Option B: Proceed anyway — accept the risk and implement with current information - Option C: Stop — halt until the MiniPRD is revised
-
2.5. Codebase Orientation (Haiku Sub-Agent)
- Before implementing, launch a read-only sub-agent to pre-load all relevant file contents:
- Use the Agent tool with
subagent_type: "general-purpose"andmodel: "haiku" - Prompt the sub-agent: "Read
spec/compiled/[MiniPRD filename]in full. From the Implementation Plan task list, identify every file path that must be created or modified. For each file that already exists in the codebase, read its full content and return it verbatim. For new files, list the path and expected purpose only. Return a structured report mapping file paths to their current content."
- Use the Agent tool with
- Wait for the sub-agent result before proceeding to Step 3.
- Use the returned file contents as your working context for implementation.
- Fallback: If the sub-agent returns an error or empty result, proceed to Step 3 and read files directly as normal.
- Do not re-read files the sub-agent already returned unless you need to verify a specific line.
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 · 72 lines · 38 tokens per session scan A 61aad00cdfe9
execute is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 985 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…