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 GongLingRui/agent-skills-factory --skill skillgit clone --depth 1 https://github.com/GongLingRui/agent-skills-factoryWrote 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/gonglingrui/agent-skills-factory/skill)<a href="https://agentmods.dev/skills/gonglingrui/agent-skills-factory/skill"><img src="https://agentmods.dev/badge/skills/gonglingrui/agent-skills-factory/skill/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/gonglingrui/agent-skills-factory/skill"><img src="https://agentmods.dev/badge/skills/gonglingrui/agent-skills-factory/skill.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 | $0.00023 | $0.00424 |
| Opus 5 | $0.00012 | $0.00212 |
| Sonnet 5 | $0.00005 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
auto-select-skill 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 11d 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
Auto Select Skill
<任务步骤> 第一步：你先识别任务列表中总共有多少条任务（选项）。 第二步：你需要确认第一步中选项数量的30%的值是多少（需向下取整，如：若有4个选项，30%的值应该1个）。 第三步：你需要先思考和判断任务列表中的哪些选项最符合用户原始输入所表达的意图和期待，这些选项的数量不能多于第二步中结果的数量，若多则删掉多余的选项，若少则正常输出结果。 第四步：严格按照输出格式示例的格式，以数组格式输出第三步中的选项的序号，注意：你需要确保最终的输出是一个数组，每个元素都是一个选项的序号。例如["1", "2", "3"]。 </任务步骤>
<输出格式示例> ["1", "2", "3"] </输出格式示例>
<任务> 你的任务是根据用户原始输入（重点参考）和任务列表并遵循任务要求在当前步骤选择一个或几个最合适的项，最后严格按照输出格式示例以数组格式输出结果。 注意： 1.仅输出数组信息，禁止输出其他内容，同时禁止输出思考过程。 2.输出的数组中数字的数量应远少于任务列表中序号的数量，如：任务列表中序号的数量有4个，则输出数组中数字的数量应为1个；若任务列表中序号的数量有20个，则输出数组中数字的数量应为5个
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.
- 11d ago First seen · 24 lines · 23 tokens per session scan A b16569c24484
auto-select-skill is a skill published in the GitHub repository GongLingRui/agent-skills-factory (6 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 424 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 2026-08-31.
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