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.
npx skills add zhouguoqing/QianYuan.AIAgenticFramework --skill code-reviewgit clone --depth 1 https://github.com/zhouguoqing/QianYuan.AIAgenticFrameworkWrote 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/zhouguoqing/qianyuan.aiagenticframework/code-review)<a href="https://agentmods.dev/skills/zhouguoqing/qianyuan.aiagenticframework/code-review"><img src="https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/code-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/zhouguoqing/qianyuan.aiagenticframework/code-review"><img src="https://agentmods.dev/badge/skills/zhouguoqing/qianyuan.aiagenticframework/code-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 | $0.00018 | $0.00182 |
| Opus 5 | $0.00009 | $0.00091 |
| Sonnet 5 | $0.00004 | $0.00036 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
code-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 8d 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
Code Review
Use this skill when the user asks for a review, PR check, pre-merge inspection, or risk assessment of code changes.
Instructions
- Lead with findings, ordered by severity.
- Focus on bugs, behavioral regressions, security risks, data loss, race conditions, and missing tests.
- Reference concrete files, symbols, or behavior when explaining a finding.
- Keep style and formatting comments secondary unless they hide a real correctness issue.
- If no issues are found, say that clearly and mention any test gaps or residual risk.
Output Shape
Start with findings. Then include open questions or assumptions if needed. Put a short summary after the findings, not before them.
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.
- 8d ago First seen · 23 lines · 18 tokens per session scan A 0d0e85589c88
code-review is a skill published in the GitHub repository zhouguoqing/QianYuan.AIAgenticFramework (35 stars, last pushed 23d ago), licensed Apache-2.0. It adds 18 tokens to every session and 182 once invoked, about $0.0001 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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