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 agentmods add skills/agentsope/skillalchemy/agentsop-difynpx skills add agentsope/SkillAlchemy --skill agentsop-difygit clone --depth 1 https://github.com/agentsope/SkillAlchemyWhat 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 | $0.00069 | $0.08550 |
| Opus 5 | $0.00034 | $0.04275 |
| Sonnet 5 | $0.00014 | $0.01710 |
| Haiku 4.5 | $0.00007 | $0.00855 |
Grade A, and why
agentsop-dify 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 3d 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 — 539 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dify SOP — LLM Application Platform, Visual-First with Code Escape Hatches
框架定位: "An open-source platform for building agentic workflows" — visual workflow + RAG + agent + monitoring + deploy in one box, self-hostable. [docs.dify.ai/en/introduction], [github.com/langgenius/dify]
"Dify is the only tool that gives you data ingestion, RAG, an API, and a polished, shareable web UI in one click." [learnwithparam.com/blog/batteries-included-rag-platforms-dify-ragflow-onyx]
1. 何时激活 (When to Activate)
1.1 直接信号 (Direct triggers)
- 用户说 "我需要个 LLM 应用 / 客服 bot / 知识库问答 / 文档处理流"，且希望几天内交付
- 用户说 "我们在用 Dify / 自己部署了 Dify / 想自己 host LLM 平台"
- 团队有非工程师参与编排逻辑（PM / 运营 / 业务方画 chatflow）
- 需要一站式：模型管理 + RAG + 工具调用 + 用户访问 + API + 监控 + 评估
- 需要多租户 / workspace 权限（典型 SaaS / 企业内多团队场景） [blog.elest.io/dify-vs-langflow-vs-flowise]
1.2 反向信号 (Skip Dify when)
- 高吞吐: 单 pod ~10 QPS 上限，每个 workflow 节点单独打 DB [memo.d.foundation/breakdown/dify]
- 实时低延迟: workflow 引擎开销 + 同步 DB 调用，sub-second 场景不适用
- 需要 pause-and-wait-for-user: 审批流 / 用户多步交互 → Dify workflow 不支持，issue #21455 已关 "not planned"
- 纯工程团队 + 已有 LangChain/LangGraph 投入: Dify 的可视化层成为负担而非杠杆
- 训练 / 微调 workflow: Dify 是 inference + orchestration，不碰训练
- 极致 RAG: 需要 KG-RAG、deep parsing、混合检索调优 → RAGFlow 更专 [sider.ai/blog/ai-tools/dify-vs-ragflow]
- AI 代码已经能写: 业内已有声音 "既然在 Dify 里也要写 Python，何不直接 Python？" [zhuanlan.zhihu.com/p/1947389040702781389]
1.3 心智门槛 (Mental check)
Dify 的核心价值是 "把 LLM 应用工程的脚手架打平"——auth、API、UI、向量库、模型 provider、日志、版本——而不是替代 LLM 编排框架本身的表达力。
判断公式:
- 如果你的瓶颈是 "组装周边设施" → 用 Dify
- 如果你的瓶颈是 "逻辑表达力 / 状态管理 / 极致性能" → 用 LangGraph / 直接代码
2. 核心心智模型 (Mental Model)
2.1 五层架构 (The 5-layer stack)
[Studio] ← Visual canvas (workflow / chatflow / agent / chatbot / text-gen)
↓ 编排
[Apps] ← 5 种 app 类型，全部跑在统一 Graph Engine 上
↓ 依赖
[Knowledge] ← RAG pipeline (ingest → chunk → embed → index → retrieve → rerank)
↓ + 调用
[Tools/Plugins] ← Marketplace: Models / Tools / Agent Strategies / Extensions / Bundles
↓ 观察
[Monitoring] ← 内建 logs + 外接 LangSmith / Langfuse / Arize Phoenix / Opik
参考: [docs.dify.ai/en/introduction], [dify.ai/blog/dify-plugin-system-design-and-implementation]
What ships with it
7 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.
- 3d ago First seen · 539 lines · 69 tokens per session scan A 295904f9dd0b
agentsop-dify is a skill published in the GitHub repository agentsope/SkillAlchemy (342 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 8,550 once invoked, about $0.0003 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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ccg-docs
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Use when querying, ingesting, or maintaining a local RAG MCP corpus for semantic document retrieval with privacy controls.