skills Skill
description: Executes AI tasks with strict resource limits and progress tracking. Use when the user needs to search the web, explore large codebases, or check dependencies. Prevents runaway execution by enforcing timeouts and limiting searches, file reads, and recursion depth.
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Why use this skill
skills is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Generic Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in Done-0/openflow, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: openflow
description: Executes AI tasks with strict resource limits and progress tracking. Use when the user needs to search the web, explore large codebases, or check dependencies. Prevents runaway execution by enforcing timeouts and limiting searches, file reads, and recursion depth.
allowed-tools: Bash(openflow:*)
---
# Controlled AI Task Execution with OpenFlow
The `openflow` command executes AI tasks with built-in resource limits and real-time progress tracking. A structured workflow prevents runaway execution by enforcing timeouts and limiting operations.
## Prerequisites
```bash
openflow --version # Verify installation
```
For setup details, see https://github.com/Done-0/openflow#readme
## Core Workflow
1. **List flows**: `openflow list` — see available workflows
2. **Inspect**: `openflow info <flow-name>` — view flow details and limits
3. **Execute**: `openflow run <flow-name> --input '{"key": "value"}'` — run with parameters
4. **Monitor**: progress displayed in real-time with resource tracking
5. **Complete**: automatic limit enforcement prevents runaway execution
## Available Flows
```bash
openflow list # List all flows
openflow list --category research # Filter by category
```
### search_web
Search the web with strict limits to prevent runaway searches. Uses DuckDuckGo Instant Answer API (free) or Brave Search API (set `BRAVE_API_KEY` env var for better results).
```bash
openflow run search_web --input '{"topic": "AI trends"}'
```
**Use for:** Research, competitive analysis, gathering information fro
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