Borrowing it
Nothing to install: this file belongs to GraflowAI/graflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/GraflowAI/graflow/main/.claude/skills/graflow-workflow/SKILL.mdgit clone --depth 1 https://github.com/GraflowAI/graflowWrote 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/graflowai/graflow/graflow-workflow)<a href="https://agentmods.dev/skills/graflowai/graflow/graflow-workflow"><img src="https://agentmods.dev/badge/skills/graflowai/graflow/graflow-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/graflowai/graflow/graflow-workflow"><img src="https://agentmods.dev/badge/skills/graflowai/graflow/graflow-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.01422 |
| Opus 5.5 | $0.00029 | $0.00569 |
| Sonnet 5.5 | $0.00015 | $0.00284 |
| Haiku 4.5 | $0.00007 | $0.00142 |
Grade A, and why
graflow-workflow 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 2mo 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graflow Workflow Builder
Build executable task graphs in Python using a structured 3-phase approach.
Workflow: Plan -> Implement -> Review
Phase 1: Plan
Goal: Clarify requirements, create a design document, and get user approval through iterative feedback.
Steps:
Step 1: Requirements Gathering
Ask the user clarifying questions about the workflow:
- What is the workflow's purpose?
- What are the input sources and output destinations?
- Which tasks need to run sequentially vs. in parallel?
- Is LLM integration needed?
- Are there any dynamic/conditional branching requirements?
Step 2: Create Design Document
Create a design document (workflow_design.md) with:
- Workflow overview and purpose
- Task definitions (name, responsibility, inputs/outputs)
- Task graph structure (ASCII diagram)
- Channel data flow
- Error handling strategy
Design Document Template:
# Workflow Design: {workflow_name}
## Overview
{Brief description of what this workflow accomplishes}
## Tasks
| Task ID | Responsibility | Inputs | Outputs |
|---------|---------------|--------|---------|
| task_a | ... | ... | ... |
## Task Graph
source >> (transform_a | transform_b) >> sink
## Channel Data Flow
- `config`: Set by setup, used by all tasks
- `results`: Accumulated by each task
## Error Handling
- {Strategy: fail-fast, best-effort, retry, etc.}
Step 3: Present Design to User
Present the design document to the user with a clear summary:
- Show the task graph diagram
- Highlight key design decisions
- Ask explicitly: "Does this design meet your requirements? Please provide feedback if any changes are needed."
Step 4: Iterate on Feedback
If the user provides feedback:
- Update
workflow_design.mdwith the requested changes - Summarize the changes made
- Re-present the updated design
- Repeat until the user is satisfied
Step 5: Confirm Design Approval
Before proceeding to implementation:
- Ask the user to confirm: "Is this design approved? If yes, I'll proceed to implementation."
- Only move to Phase 2 after explicit approval
What ships with it
3 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.
- 2mo ago First seen · 226 lines · 73 tokens per session scan A 405e8148f6a5
graflow-workflow is a skill published in the GitHub repository GraflowAI/graflow (41 stars, last pushed 13d ago), licensed Apache-2.0. It adds 73 tokens to every session and 1,422 once invoked, about $0.0003 per session on Opus 5.5. 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.
graflow-workflow compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| graflow-workflowGraflowAI/graflow | Skill | 41 | 13d ago | Apache-2.0 | A | 73 | Claude Code |
| output-dev-workflow-costgrowthxai/output | Skill | 444 | 4d ago | Apache-2.0 | A | 40 | Claude Code |
| agentproto-llm-endpointagentproto/ts | Skill | 9 | yesterday | Apache-2.0 | A | 77 | Claude Code, Codex |
| flow-nexus-swarmruvnet/ruflo | Skill | 81k | yesterday | MIT | A | 20 | Claude Code |
| inference-serving-sglangdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 76 | Claude Code, Codex |
| open-sourcebrowser-use/browser-use | Skill | 120k | yesterday | MIT | A | 137 | Claude Code |
| data-leakage-detectionTencent/AI-Infra-Guard | Skill | 7.4k | yesterday | Apache-2.0 | A | 34 | Claude Code |