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 tranfu-labs/tranfu-skills --skill credibility-reviewgit clone --depth 1 https://github.com/tranfu-labs/tranfu-skillsWrote 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/tranfu-labs/tranfu-skills/credibility-review)<a href="https://agentmods.dev/skills/tranfu-labs/tranfu-skills/credibility-review"><img src="https://agentmods.dev/badge/skills/tranfu-labs/tranfu-skills/credibility-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/tranfu-labs/tranfu-skills/credibility-review"><img src="https://agentmods.dev/badge/skills/tranfu-labs/tranfu-skills/credibility-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 | $0.00201 | $0.07548 |
| Opus 5.5 | $0.00080 | $0.03019 |
| Sonnet 5.5 | $0.00040 | $0.01510 |
| Haiku 4.5 | $0.00020 | $0.00755 |
Grade A, and why
credibility-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 — 461 lines — stays where its author put it; the contents beside it link to each section on GitHub.
credibility-review
用两个独立 agent 同时审稿:A 轨模拟读者(双轴 耐心 / 信任 标量),B 轨反模式 catalog(语言层枚举)。两个独立 fresh agent 并行——A 不开规则书素读,B 跑 catalog;最后三态合判:两轨都 PASS = 可发;都 FAIL = 退稿;一 PASS 一 FAIL = 待审查。只诊断,不重写。规则源永远是
tranfu-site/goal-docs/05-design-踩坑记-final.md与06-design-养成记-final.md。
为什么双轨:B 轨能抓"语言层有违规"(PR 套话 / 教程标题 / 隐蔽 hero);但 B 轨 0 命中不等于文章有内容——一个 fluent 的 PR 写手能写一篇全部避开反模式但全空的文章。A 轨抓的就是这种"语言干净但读起来空"——通过模拟读者的耐心和信任在读的过程中怎么消耗。两件事互补,缺一不可。
§0 兼容性合同
本 skill 假设:
- BSD grep(macOS 默认)或 GNU grep ≥ 2.6
- 不依赖 PCRE / -P / \b / \d / \w
- 中文 UTF-8 默认 work,无需 LANG / LC_ALL
- 待审稿件用原始路径 <target>,不复制到 /tmp
- 中英双语:英文用 `grep -nEi`(B 轨)
修订 grep 时必须在 macOS 默认 zsh + /usr/bin/grep 下实测。
§1 何时调用 / 不调用
调用:用户给一段 markdown 或一个 .md 路径,说"看下能不能发 / 帮我审一下 / 抓一下问题 / 检查文章";用户在 tranfu-site/src/content/posts/*.md 上停下来求审;用户贴一篇 AI 行业文章问"这写得怎么样 / 像不像 PR 稿"。
不调用:用户要重写 / 润色 / 扩写 / 打分 / 评级;用户要"全文点评 / 建议从几个维度展开"。
§2 工作流
Step 1 主 agent 识别体裁(踩坑记 / 养成记 / 其它)
Step 2 主 agent 用 Agent 工具并发起两个 fresh sonnet sub-agent:
- A 轨 (sub-agent A):读 §3 + 文章, 输出读者轨迹 + verdict
- B 轨 (sub-agent B):读 §4 + 文章, 输出 catalog 命中清单 + verdict
两个 sub-agent **互不可见对方的 prompt 和 output**——必须独立判断。
Step 3 两个 sub-agent 都返回后, 主 agent 按 §5 三态合判
Step 4 主 agent 按 §6 schema 汇总报告
硬规则:A 轨 sub-agent 的 prompt 不得包含 B 轨内容(不让它知道反模式 catalog),否则 A 会污染成 B 的子集,失去独立判断价值。两轨必须真独立。
任一步发现退稿级违规也不要提前终止——一篇文章常同时踩多个坑,一次性把全部问题报给作者比挤牙膏式反馈更有用。
Step 1 — 识别体裁(主 agent 同步执行)
按以下顺序判断:
- 看 frontmatter
tag:踩坑→ 踩坑记;养成→ 养成记。 - 无 frontmatter:看时间跨度——单点事件(小时 / 几天)= 踩坑记;≥ 2 个月演化 = 养成记。
- 仍无法判断 → 直接问用户"这篇你打算发到哪个栏目",不要猜。
非踩坑/养成的文章(资讯爆料 / PR 通稿 / research essay / advice tutorial / postmortem 等):体裁 = "其它"。A 轨用通用读者预期 set(见 §3.4 末),B 轨跳过结构核对仍跑反模式 catalog。
规则源:踩坑记 = tranfu-site/goal-docs/05-design-踩坑记-final.md;养成记 = 06-design-养成记-final.md。
§3 — A 轨 spec(读者模拟,sub-agent A 读这一节)
3.1 A 轨 sub-agent 的 prompt 模板
你是 AI 行业一线工程师同行(5–10 年代码经验)。任务:模拟读者读完这篇 <体裁>,
输出双轴标量轨迹 + 终局 verdict。
**禁止**:不打开任何反模式词表 / 禁用词清单。你只用读者直觉判断,不做语言学
catalog 匹配。如果你脑子里冒出"这是禁用词"——忽略,继续读。
体裁:<踩坑记 | 养成记 | 其它>
文章路径:<path>
按 §3.2–§3.5 流程执行,最后输出 §3.6 schema。
What ships with it
31 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 80 B
- assets/icon.png 806 B
- assets/icon.svg 495 B
- examples/bad-养成记-1-空标签.md 3.3 KB
- examples/bad-养成记-2-hidden-hero.md 9.7 KB
- examples/bad-养成记-3-fluent-empty.md 3.2 KB
- examples/bad-踩坑记-1-marketing-fluff.md 2.7 KB
- examples/bad-踩坑记-2-no-evidence.md 3.3 KB
- examples/EXPECTED.md 6.3 KB
- examples/good-踩坑记-baseline.md 5.0 KB
- examples/image-1.png 38 KB
- examples/image-2.png 167 KB
- examples/image-3.png 182 KB
- examples/image-4.png 173 KB
- examples/image-5.png 152 KB
- examples/image-6.png 225 KB
- examples/image-7.png 185 KB
- examples/image-8.png 219 KB
- examples/image.png 202 KB
- examples/real-en-anthropic-aug2025-postmortem.md 6.5 KB
- examples/real-en-karpathy-microgpt.md 4.2 KB
- examples/real-en-openai-confessions.md 5.3 KB
- examples/real-en-simonw-using-llms-for-code.md 6.1 KB
- examples/real-zh-baoyu-claude-html.md 18 KB
- examples/real-zh-qbitai-gpt6-leak.md 3.3 KB
- examples/real-zh-qbitai-meituan-multimodal.md 1.5 KB
- README.md 4.5 KB
- README.zh.md 3.8 KB
- workflow.png 490 KB
- workflow.svg 29 KB
- 禁用词.md 8.2 KB
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 · 461 lines · 201 tokens per session scan A 84a649bc11f0
credibility-review is a skill published in the GitHub repository tranfu-labs/tranfu-skills (2 stars, last pushed 24d ago), licensed MIT. It adds 201 tokens to every session and 7,548 once invoked, about $0.0008 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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