mustang
OpenHoof is a personal AI agent you run on your own devices. No guardrails. No filters. No corporate safety theater. Just a raw, unbroken stallion of an AI. Lives on WhatsApp, Telegram, Slack, Discord, Signal & 20+ more channels. Local, fast, always-on, stubbornly uncensored.
Install / Use
npx skills add OpenHoof-AI/OpenHoof --skill mustangInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of mustang
mustang scores 71/100 on our quality scale, 2493rd of 2,864 Automation skills we index.
Its SKILL.md is 2.6 KB long, well organised into 10 sections with 6 code examples: a solid amount of guidance for an agent.
It has no GitHub stars yet, so there is no community track record; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 7 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 74/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-01. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
mustang compared with similar skills
All 4 of these similar skills score higher than mustang; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| mustang (this skill)by OpenHoof-AI | 71 | 0 | 7mo ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 87.2k | 16d ago | CLAUDE.md |
| rufloby ruvnet | 100 | 73.6k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.0k | 1d ago | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 9d ago | SKILL.md |
Frequently asked questions
- How do I install mustang?
- Run
npx skills add OpenHoof-AI/OpenHoof --skill mustang. The install tabs above show the steps for each supported agent. - Which AI agents does mustang work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is mustang safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It declares no license and scores 74/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is mustang still maintained?
- The repository was last updated about 7 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubMustang
Mustang executes multi-step workflows with approval checkpoints. Use it when:
- User wants a repeatable automation (triage, monitor, sync)
- Actions need human approval before executing (send, post, delete)
- Multiple tool calls should run as one deterministic operation
When to use Mustang
| User intent | Use Mustang? | | ------------------------------------------------------ | --------------------------------------------- | | "Triage my email" | Yes — multi-step, may send replies | | "Send a message" | No — single action, use message tool directly | | "Check my email every morning and ask before replying" | Yes — scheduled workflow with approval | | "What's the weather?" | No — simple query | | "Monitor this PR and notify me of changes" | Yes — stateful, recurring |
Basic usage
Run a pipeline
{
"action": "run",
"pipeline": "gog.gmail.search --query 'newer_than:1d' --max 20 | email.triage"
}
Returns structured result:
{
"protocolVersion": 1,
"ok": true,
"status": "ok",
"output": [{ "summary": {...}, "items": [...] }],
"requiresApproval": null
}
Handle approval
If the workflow needs approval:
{
"status": "needs_approval",
"output": [],
"requiresApproval": {
"prompt": "Send 3 draft replies?",
"items": [...],
"resumeToken": "..."
}
}
Present the prompt to the user. If they approve:
{
"action": "resume",
"token": "<resumeToken>",
"approve": true
}
Example workflows
Email triage
gog.gmail.search --query 'newer_than:1d' --max 20 | email.triage
Fetches recent emails, classifies into buckets (needs_reply, needs_action, fyi).
Email triage with approval gate
gog.gmail.search --query 'newer_than:1d' | email.triage | approve --prompt 'Process these?'
Same as above, but halts for approval before returning.
Key behaviors
- Deterministic: Same input → same output (no LLM variance in pipeline execution)
- Approval gates:
approvecommand halts execution, returns token - Resumable: Use
resumeaction with token to continue - Structured output: Always returns JSON envelope with
protocolVersion
Don't use Mustang for
- Simple single-action requests (just use the tool directly)
- Queries that need LLM interpretation mid-flow
- One-off tasks that won't be repeated
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
