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-peer-review/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-peer-review)<a href="https://agentmods.dev/skills/tjmustard/hypergraph-coding-agent-framework/hyper-peer-review"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-peer-review/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-peer-review"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-peer-review.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 | $0.00043 | $0.00488 |
| Opus 5.5 | $0.00017 | $0.00195 |
| Sonnet 5.5 | $0.00009 | $0.00098 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
peer-review 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Peer Review
This skill critically evaluates external peer review findings as the team lead — verifying each finding against the actual code before accepting or rejecting it.
When to use this skill
- When another model, reviewer, or team member has provided feedback on the current implementation.
- When the user explicitly runs
/hyper-peer-reviewand pastes review findings. - When cross-checking AI-generated code review output before acting on it.
How to use it
-
Receive the Findings If feedback was not provided with the command, use AskUserQuestion:
How would you like to provide the peer review feedback? - Option A: I'll paste the feedback — type or paste the review content directly - Option B: It's already in a file — provide the file path and I will read itThe reviewer has less context on this project's history and decisions than you do — evaluate accordingly.
-
Verify Each Finding For EACH finding:
- Check if it exists — Read the actual code. Does this issue really exist?
- If it doesn't exist — Explain clearly why (already handled, reviewer misunderstood the architecture, outdated assumption).
- If it does exist — Assess severity: Critical / High / Medium / Low.
-
Produce the Summary Output three sections:
Valid Findings (Confirmed Issues)
- List each confirmed issue with its severity and the specific file/function affected.
Invalid Findings (Rejected with Explanation)
- List each rejected finding with a clear explanation of why it's incorrect or inapplicable.
Prioritized Action Plan
- Ordered list of confirmed issues to fix, from highest to lowest severity.
- For each: the specific change needed and which file/node it affects.
Behavior Rules
- You are the team lead — do not accept findings at face value.
- Always read the code before accepting or rejecting a finding.
- Be direct but fair. If a finding is wrong, explain why clearly.
- Cross-reference
spec/compiled/architecture.ymlfor architectural context when evaluating structural findings.
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 · 55 lines · 43 tokens per session scan A 83b549e04e00
peer-review 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 488 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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