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 oyi77/1ai-skills --skill dify-workflowgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/dify-workflow)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/dify-workflow"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/dify-workflow/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/oyi77/1ai-skills/dify-workflow"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/dify-workflow.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 | $0.00037 | $0.01402 |
| Opus 5.5 | $0.00015 | $0.00561 |
| Sonnet 5.5 | $0.00007 | $0.00280 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade B, and why
dify-workflow scanned grade B with 2 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 1mo 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( 'https://api.dify.ai/v1/datasets/{dataset_id}/documents', Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST 'https://api.dify.ai/v1/chat-messages' \ How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Dify is an open-source platform for building LLM applications with a visual workflow builder. It supports chatbots, agents, text generators, and complex workflows with knowledge bases, tools, and conditional logic.
Capabilities
- Build LLM apps visually (chatbot, agent, text generator, workflow)
- Create and manage knowledge bases with document upload
- Design workflows with nodes: LLM, knowledge retrieval, code, HTTP, conditional
- Deploy apps via API, embed, or shareable link
- Manage prompts, variables, and conversation memory
- Integrate with OpenAI, Azure, local models via Ollama
When to Use
Trigger phrases:
-
"dify workflow"
-
"Dify AI workflow platform — LLM apps, knowledge bases, agents, workflow orchestr"
-
Building LLM applications without coding
-
Needing visual workflow design for AI pipelines
-
Wanting knowledge base RAG without infrastructure setup
-
Prototyping AI features quickly
-
Deploying AI apps as APIs
When NOT to Use
- Task is outside your authorization scope
- You need to implement controls (use implementing-* skills)
- Task is about analysis, not action (use analyzing-* skills)
- You don't have access to target systems
- Task requires compliance expertise (consult professionals)
- Task is about defense, not offense (use defensive skills)
Pseudo Code
# Example workflow for this skill
def execute(input_data):
# Step 1: Validate input
if not input_data:
raise ValueError("Input data is required")
# Step 2: Process core logic
result = process(input_data)
# Step 3: Validate output
validate_output(result)
return result
App Types
| Type | Use Case |
|---|---|
| Chatbot | Multi-turn conversation with memory |
| Agent | Tool-using autonomous assistant |
| Text Generator | Single input → output transformation |
| Workflow | Complex multi-step pipeline |
Workflow Node Design
Start → Knowledge Retrieval → LLM → Conditional → HTTP Request → End
↓
Code Processing → End
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.
- 1mo ago First seen · 234 lines · 37 tokens per session scan B 46a3bed2c95e
dify-workflow is a skill published in the GitHub repository oyi77/1ai-skills (16 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 1,402 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
dify
Use when building LLM applications with visual workflow — RAG knowledge bases, AI agents, chatbots with drag-and-drop orchestration. Dify: open-source LLM app platform supporting 30+ models (OpenAI, Claude, DeepSeek, Ollama, Qwen, GLM) with Docker deployment.
ai-skills
Use when building LLM applications, RAG knowledge bases, AI agents, terminal coding agents, multi-model orchestration, plugin-based agent harnesses, or file translation. Index of 14 skills: Dify, LangChain, LlamaIndex, Ollama, Transformers, Hermes Agent, OpenClaw, OpenCode, Pi, DocuTranslate, DeepSeek Harness and more.
agent-framework-azure-ai-py
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
ai-engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
ai-pipeline-orchestration
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.