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 qinye6/pi-ccg --skill aigit clone --depth 1 https://github.com/qinye6/pi-ccgWrote 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/qinye6/pi-ccg/ai)<a href="https://agentmods.dev/skills/qinye6/pi-ccg/ai"><img src="https://agentmods.dev/badge/skills/qinye6/pi-ccg/ai.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.00330 |
| Opus 5 | $0.00023 | $0.00165 |
| Sonnet 5 | $0.00009 | $0.00066 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
ai 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 7d 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
100% identical to ai — 0 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.
What it actually says
丹鼎秘典 · AI/LLM 能力中枢
能力矩阵
| Skill | 定位 | 核心能力 |
|---|---|---|
| agent-dev | Agent 开发 | 多 Agent 编排、工具调用、RAG |
| llm-security | LLM 安全 | Prompt 注入、越狱防护、输出安全 |
| rag-system | RAG 系统 | 向量数据库、检索策略、重排算法 |
| prompt-and-eval | Prompt 工程与模型评估 | Few-shot、CoT、ReAct、RAGAS、LLM-as-Judge |
AI 工程原则
设计原则:
- 人机协作，AI 增强而非替代
- 可解释性优先
- 安全边界明确
- 渐进式自主
开发原则:
- Prompt 即代码，需版本控制
- 输入输出都需验证
- 成本与效果平衡
- 持续评估与迭代
What ships with it
4 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.
- 7d ago First seen · 35 lines · 45 tokens per session scan A c311af51b59f
ai is a skill published in the GitHub repository qinye6/pi-ccg (10 stars, last pushed 13d ago), licensed MIT. It adds 45 tokens to every session and 330 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
llm-security
Use for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.