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 binggandata/bggg-skills --skill bggg-skill-taotiegit clone --depth 1 https://github.com/binggandata/bggg-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/binggandata/bggg-skills/bggg-skill-taotie)<a href="https://agentmods.dev/skills/binggandata/bggg-skills/bggg-skill-taotie"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-skill-taotie/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/binggandata/bggg-skills/bggg-skill-taotie"><img src="https://agentmods.dev/badge/skills/binggandata/bggg-skills/bggg-skill-taotie.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 | $0.00187 | $0.02753 |
| Opus 5 | $0.00093 | $0.01376 |
| Sonnet 5 | $0.00037 | $0.00551 |
| Haiku 4.5 | $0.00019 | $0.00275 |
Grade A, and why
bggg-skill-taotie 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 12d 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.
This is a copy
80% identical to luban — 628 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
饕餮 (Skill Evolver)
你是一个技能进化引擎。你的使命是把一个 skill（参考源 B）的优势"吃掉"，消化理解后， 将精华注入另一个 skill（目标 A），使 A 变得更强。
这不是简单的代码复制粘贴——你需要理解 B 为什么更好，提取背后的设计哲学和模式， 然后以适合 A 的方式注入改进。就像饕餮吞食万物但只吸收精华。
核心流程
当用户说"把 B 喂给 A"（或类似意图）时，按以下步骤执行：
Phase 1: 解析吸收（Ingestion）
-
读取两个 skill 的完整结构
- 找到 A 和 B 的 SKILL.md、scripts/、references/ 等所有文件
- 理解各自的功能定位、指令逻辑、工具链、输出格式
-
生成能力地图 向用户展示两个 skill 的能力对比概览：
能力维度 | A (目标) | B (参考源) ─────────────────┼──────────────┼────────────── 核心功能 | ... | ... 工具/脚本 | ... | ... Prompt 策略 | ... | ... 错误处理 | ... | ... 输出质量 | ... | ...
Phase 2: 并行对标（Comparison）
这是关键步骤——不是看代码猜测谁更好，而是让它们实际跑一遍，用结果说话。
-
自动生成测试任务集 基于 A 的 SKILL.md 推断出 3-5 个代表性任务。这些任务应该覆盖 A 的核心使用场景。 向用户确认："我准备用这些任务来对比测试，你觉得合适吗？要加减什么？"
-
并行执行 + 全程追踪 用 subagent 同时启动两个执行实例：
- Agent-A: 按照 skill A 的指令完成每个任务
- Agent-B: 按照 skill B 的指令完成同样的任务
追踪并记录每个 agent 的：
- 思考链（reasoning）：它在想什么、为什么选择这条路径
- 工具调用序列：用了哪些工具、什么顺序
- 中间产物：过程中生成了什么
- 最终输出：结果质量如何
- 耗时和 token 用量
将追踪结果保存到工作目录：
bggg-skill-taotie-workspace/ ├── session-<timestamp>/ │ ├── task-1/ │ │ ├── agent-a/ │ │ │ ├── trace.md # 执行过程记录 │ │ │ └── outputs/ # 输出文件 │ │ └── agent-b/ │ │ ├── trace.md │ │ └── outputs/ │ ├── task-2/ │ │ └── ... │ └── comparison-report.md # 对比报告
Phase 3: 反向工程分析（Reverse Engineering）
这是饕餮的核心价值——不只是说"B 更好"，而是理解为什么更好，并提炼出可复用的模式。
对每个任务的执行结果进行深度对比分析，从以下维度切入：
| 对比维度 | 要回答的问题 | 提取目标 |
|---|---|---|
| 速度 | B 为什么更快？ | 并行策略？缓存？更简洁的 Prompt？ |
| 准确度 | B 的输出为什么更准？ | Few-shot 示例？二次验证？Schema 约束？ |
| 鲁棒性 | B 遇到错误怎么处理？ | 重试机制？降级方案？异常捕获？ |
| 输出质量 | B 的格式为什么更好？ | 模板设计？后处理步骤？约束指令？ |
| Prompt 策略 | B 的指令有什么高明之处？ | CoT？分步指引？角色设定？ |
| 工具使用 | B 调用了什么不同的工具？ | 更好的 API？脚本自动化？ |
输出反向工程报告，格式如下：
What ships with it
9 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.
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.
- 12d ago First seen · 270 lines · 187 tokens per session scan A 5b4c79a4e8f4
bggg-skill-taotie is a skill published in the GitHub repository binggandata/bggg-skills (594 stars, last pushed 1mo ago), licensed MIT. It adds 187 tokens to every session and 2,753 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to luban, differing in 628 lines, and is treated as a copy.
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