Octofriend is a small coding assistant that works with OpenAI-compatible or Anthropic-compatible language-model APIs. It helps developers write and edit code, switch models during conversations, and recover from tool-call or code-edit failures. The catalogue skill supports using Octofriend as a coding-agent workflow.
Borrowing it
Nothing to install: this file belongs to synthetic-lab/octofriend. 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/synthetic-lab/octofriend/main/.agents/skills/pr-review/SKILL.mdgit clone --depth 1 https://github.com/synthetic-lab/octofriendWrote 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/synthetic-lab/octofriend/pr-review)<a href="https://agentmods.dev/skills/synthetic-lab/octofriend/pr-review"><img src="https://agentmods.dev/badge/skills/synthetic-lab/octofriend/pr-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/synthetic-lab/octofriend/pr-review"><img src="https://agentmods.dev/badge/skills/synthetic-lab/octofriend/pr-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00007 | $0.00214 |
| Opus 5 | $0.00003 | $0.00107 |
| Sonnet 5 | $0.00001 | $0.00043 |
| Haiku 4.5 | $0.00001 | $0.00021 |
Grade A, and why
pr-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 13d 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.
What it actually says
To load a Github pull request, run the fetch tool twice:
First fetch
First, load the URL for the PR to understand the author's intent.
Your fetch tool does not execute JavaScript. Note that parts of the Github UI may fail without JS; for example, loading comments might say:
UH OH!
There was an error while loading"
This is okay and expected. Don't worry about that.
Second fetch: load the diff
To load the diff for the PR, fetch the PR URL with a .diff
attached to the end. For example, to review
https://github.com/synthetic-lab/octofriend/pull/66, you should fetch:
https://github.com/synthetic-lab/octofriend/pull/66.diff
The diff is the most important part. The author may be incorrect, or have the right idea but the wrong implementation. Focus on whether there are any bugs or unexpected behavior.
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.
- 13d ago First seen · 33 lines · 7 tokens per session scan A 9951f1ee7499
pr-review is a skill published in the GitHub repository synthetic-lab/octofriend (1,009 stars, last pushed yesterday), licensed MIT. It adds 7 tokens to every session and 214 once invoked, about $0.0000 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 2026-08-30.
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