hire
- Repo stars 39
- Author updated Live
- Author repo awesome-omni-skill
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @diegosouzapw · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: hire
description: Interactive hiring wizard to set up a new AI team member. Guides the user through role design vi…
category: ai
runtime: no special runtime
---
# hire output preview
## PART A: Task fit
- Use case: Interactive hiring wizard to set up a new AI team member. Guides the user through role design via conversation, generates agent identity files, and optionally sets up performance reviews. Use when the user wants to hire, add, or set up a new AI agent, team member, or assistant. Triggers on phrases like "hire", "add an agent", "I need help with X" (implying a new role), or "/hire"..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use / The Interview / 3 core questions, asked one at a time:” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Interactive hiring wizard to set up a new AI team member. Guides the user through role design via conversation, generates agent identity files, and optionally sets up performance reviews. Use when the user wants to hire, add, or set up a new AI agent, team member, or assistant. Triggers on phrases like "hire", "add an agent", "I need help with X" (implying a new role), or "/hire".”.
- **02** When the source has headings, the agent prioritizes “When to Use / The Interview / 3 core questions, asked one at a time:” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/hire`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “When to Use / The Interview / 3 core questions, asked one at a time:”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: hire
description: Interactive hiring wizard to set up a new AI team member. Guides the user through role design vi…
category: ai
source: diegosouzapw/awesome-omni-skill
---
# hire
## When to use
- Interactive hiring wizard to set up a new AI team member. Guides the user through role design via conversation, genera…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use / The Interview / 3 core questions, asked one at a time:” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "hire" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use / The Interview / 3 core questions, asked one at a time:
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} hire
Set up a new AI team member through a guided conversation. Not a config generator - a hiring process.
When to Use
User says something like:
- "I want to hire a new agent"
- "I need help with X" (where X implies a new agent role)
- "Let's add someone to the team"
/hire
The Interview
3 core questions, asked one at a time:
Q1: "What do you need help with?" Let them describe the problem, not a job title. "I'm drowning in code reviews" beats "I need a code reviewer."
- Listen for: scope, implied autonomy level, implied tools needed
Q2: "What's their personality? Formal, casual, blunt, cautious, creative?" Or frame it as: "If this were a human colleague, what would they be like?"
- Listen for: communication style, vibe, how they interact
Q3: "What should they never do?" The red lines. This is where trust gets defined.
- Listen for: boundaries, safety constraints, access limits
Q4: Dynamic (optional)
After Q1-Q3, assess whether anything is ambiguous or needs clarification. If so, ask ONE follow-up question tailored to what's unclear. Examples:
- "You mentioned monitoring - should they alert you immediately or batch updates?"
- "They'll need access to your codebase - any repos that are off-limits?"
- "You said 'casual' - are we talking friendly-professional or meme-level casual?"
If Q1-Q3 were clear enough, skip Q4 entirely.
Summary Card
After the interview, present a summary:
🎯 Role: [one-line description]
🧠 Name: [suggested name from naming taxonomy]
🤖 Model: [selected model] ([tier])
⚡ Personality: [2-3 word vibe]
🔧 Tools: [inferred from conversation]
🚫 Boundaries: [key red lines]
🤝 Autonomy: [inferred level: high/medium/low]
Then ask: "Want to tweak anything, or are we good?"
Model Selection
Before finalizing, select an appropriate model for the agent.
Step 1: Discover available models
Run openclaw models list or check the gateway config to see what's configured.
Step 2: Categorize by tier
Map discovered models to capability tiers:
| Tier | Models (examples) | Best for |
|---|---|---|
| reasoning | claude-opus-, gpt-5, gpt-4o, deepseek-r1 | Strategy, advisory, complex analysis, architecture |
| balanced | claude-sonnet-*, gpt-4-turbo, gpt-4o-mini | Research, writing, general tasks |
| fast | claude-haiku-, gpt-3.5, local/ollama | High volume, simple tasks, drafts |
| code | codex-, claude-sonnet-, deepseek-coder | Coding, refactoring, tests |
Use pattern matching on model names - don't hardcode specific versions.
Step 3: Match role to tier
Based on the interview:
- Heavy reasoning/advisory/strategy → reasoning tier
- Research/writing/creative → balanced tier
- Code-focused → code tier (or balanced if not available)
- High-volume/monitoring → fast tier
Step 4: Select and confirm
Pick the best available model for the role. In the summary card, add:
🤖 Model: [selected model] ([tier] - [brief reason])
If multiple good options exist or you're unsure, ask: "For a [role type] role, I'd suggest [model] (good balance of capability and cost). Or [alternative] if you want [deeper reasoning / faster responses / lower cost]. Preference?"
Notes
- Don't assume any specific provider - work with what's available
- Cheaper is better when capability is sufficient
- The user's default model isn't always right for every agent
- If only one model is available, use it and note it in the summary
Optional Extras
After the summary is confirmed, offer:
"Want to set up periodic performance reviews?"
- If yes: ask preferred frequency (weekly, biweekly, monthly)
- Create a cron job that triggers a review conversation
- Review covers: what went well, what's not working, scope/permission adjustments
- At the end of each review, ask: "Want to keep this schedule, change frequency, or stop reviews?"
Onboarding assignment (if relevant to the role)
- Suggest a small first task to test the new agent
- Something real but low-stakes, so the user can see them in action
What to Generate
Create an agent directory at agents/<name>/ with:
Always unique (generated fresh):
- AGENTS.md - Role definition, responsibilities, operational rules, what they do freely vs ask first
- IDENTITY.md - Name, emoji, creature type, vibe, core principles
Start from template, customize based on interview:
- SOUL.md - Base from workspace SOUL.md template, customize vibe/boundaries sections
- TOOLS.md - Populated with inferred tools and access notes
- HEARTBEAT.md - Empty or with initial periodic tasks if relevant to role
Symlink to shared (default, opinionated):
- USER.md →
../../USER.md(they need to know who they work for) - MEMORY.md →
../../MEMORY.md(shared team context)
Mention to the user: "I've linked USER.md and MEMORY.md so they know who you are and share team context. You can change this later if you want them more isolated."
Naming
Use craft/role-based names. Check TOOLS.md for the full naming taxonomy:
- Research: Scout, Observer, Surveyor
- Writing: Scribe, Editor, Chronicler
- Code: Smith, Artisan, Engineer
- Analysis: Analyst, Assessor, Arbiter
- Creative: Muse, Artisan
- Oversight: Auditor, Reviewer, Warden
Check existing agents to avoid name conflicts. Suggest a name that fits the role, but let the user override.
Team Awareness
Before generating, check agents/ for existing team members. Note:
- Potential overlaps with existing roles
- Gaps this new hire fills
- How they'll interact with existing agents
Mention any relevant observations: "You already have Scout for research - this new role would focus specifically on..."
After Setup
- Tell the user what was created and where
- Add the agent to OpenClaw config with the selected model:
Or guide them to run{ "id": "<name>", "workspace": "/path/to/clawd/agents/<name>", "model": "<selected-model>" }openclaw agents add - If monthly reviews were requested, confirm the cron schedule
- Update any team roster if one exists
Important
- This is a CONVERSATION, not a form. Be natural.
- Infer as much as possible from context. Don't ask what you can figure out.
- The user might not know what they want exactly. Help them figure it out.
- Keep the whole process under 5 minutes for the simple case.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review