agent-creator
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Interview-based system for creating specialized Claude Code agents. Use when users want to create a Claude Code agent, need help defining agent roles and responsibilities, mention "create an agent" or "build an agent", or want to generate a CLAUDE.md file for agentic coding workflows. Guides through interactive interview to gather requirements and generates complete agent configurations following Claude Code best practices.
26 other projects use a version of this.
Adopted by
Most recently taken up by
- Arcanea-Labs/Starlight-Intelligence-Systemtheir own version, first seen Nov 8, 2026
- krzysztofsurdy/code-virtuosotheir own version, first seen Nov 1, 2026
- coroboros/agent-skillstheir own version, first seen Oct 29, 2026
- 10CG/aria-plugintheir own version, first seen Oct 20, 2026
- Ronnasayd/aipptheir own version, first seen Oct 20, 2026
- AllanSantos-DV/skill-kittheir own version, first seen Oct 15, 2026
Used with
Projects with it also use security, skill-creator and 2 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
0xSeroSkillsecurity
Also in 4 projects that use this
SentrySkillskill-creator
Create new agent skills following the Agent Skills specification. Use when asked to "create a skill", "add a new skill", "write a skill", "make a skill", "build a skill", or scaffold a new skill with SKILL.md. Guides through requirements, writing, registration, and verification.
Also in 10 projects that use this
SentrySkillsecurity-review
Finds exploitable application security vulnerabilities in code changes. Use for Warden security scans, appsec review, OWASP-style checks, authentication or authorization bugs, injection, XSS, SSRF, path traversal, secrets, unsafe crypto, webhook verification, open redirects, or sensitive data exposure.
Also in 3 projects that use this
GoogleSkillcode-review
Adversarial systems, concurrency, memory-model, and architecture code review protocol. Use this skill whenever asked to review code, PRs, diffs, or branches in Filament.
Also in 5 projects that use this
Other versions26 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 26
- OpenHands/extensions★ 167same name
- 9thLevelSoftware/legion★ 76same name
- DGouron/review-flow★ 42same name
- monaccode/astromesh★ 39same name
- krzysztofsurdy/code-virtuoso★ 36same name
- different-ai/openwork-hub★ 35same name
- HashLips/agent-skills★ 29same name
- mhylle/claude-skills-collection★ 20same name
- WalletConnect/skills★ 14same name
- Arcanea-Labs/Starlight-Intelligence-System★ 10same name
16 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository3 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- book-reportSkill
- prompt-evaluationSkill
The file
We split this skill into the rules it gives an agent
10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 24 edited versions we read keep these.
10
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Create specialized Claude Code agents through an interactive interview process.
- Distinctive: line 12
Follow this 7-phase interview to gather requirements, then generate a complete agent configuration:
- Distinctive: line 49
Low: Only analyzes and advises, never modifies
- Distinctive: line 162
Write "what" not "how" (Claude already knows how to code)
- Distinctive: line 163
Use examples from actual codebase, not generic examples
- Distinctive: line 164
Document only what Claude doesn't already know
- Distinctive: line 167
Put frequently-needed info at top of CLAUDE.md
- Distinctive: line 169
Use XML tags to create scannable sections
- Distinctive: line 178
Use actual code from the codebase when possible
- Distinctive: line 179
Prefer specific examples over abstract rules
“Common” is measured: kept by at least 6 in 10 of the 24 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Creator for Claude Code
Create specialized Claude Code agents through an interactive interview process. Generates complete CLAUDE.md configurations with workflows, constraints, and quality gates tailored to specific roles.
Interview Process
Follow this 7-phase interview to gather requirements, then generate a complete agent configuration:
Phase 1: Role & Purpose (2-3 questions)
Goal: Understand the agent's identity and primary purpose.
Questions:
- "What would you like this agent to do? Describe the main role in a sentence or two."
- "What problem are you trying to solve with this agent?"
- "How would you name this agent role?" (e.g., 'Code Reviewer', 'Test Engineer', 'API Guardian')
Learning: The agent's identity, purpose, and the gap it fills.
Follow-up if vague: "Can you give me an example of a task this agent would handle?"
Phase 2: Responsibilities (3-4 questions)
Goal: Define MUST do vs SHOULD do vs MUST NOT do.
Questions:
- "What are the 3-5 core duties this agent MUST perform every time?"
- "Are there secondary responsibilities it SHOULD handle when relevant?"
- "What is explicitly outside this agent's scope? What should it NOT do?"
- "Are there situations where the agent should stop and ask before proceeding?"
Learning: Clear boundaries and priorities.
Follow-up if too broad: "Let's focus on the most important duty - can you break that down into specific steps?"
Phase 3: Autonomy & Constraints (2-3 questions)
Goal: Determine autonomy level and risk tolerance.
Questions:
- "How much autonomy should this agent have?"
- High: Makes decisions and implements independently
- Medium: Proposes plan, gets approval, then implements
- Low: Only analyzes and advises, never modifies
- "What mistakes would be unacceptable for this agent to make?"
- "Are there specific files, directories, or systems the agent should never touch?"
Learning: Risk tolerance and guardrails.
Follow-up if unsure: "Would you be comfortable with this agent committing code while you're away?"
Phase 4: Technical Context (3-5 questions)
Goal: Understand the codebase and technical environment.
Questions:
- "What's your primary programming language(s)?" (e.g., TypeScript, Python, Go)
- "What framework or stack are you using?" (e.g., Next.js, Django, FastAPI)
- "What testing tools do you use?" (e.g., Jest, pytest, go test)
- "Do you have specific code style preferences or standards?"
- "Are there key architectural patterns this agent should follow?"
Learning: Technical environment and conventions.
Follow-ups:
- "Where can I find examples of 'good' code in your project?"
- "Do you have documentation files the agent should reference?"
- "What commands does the agent need to run regularly?" (test, lint, build)
Phase 5: Workflow & Process (3-4 questions)
Goal: Define the step-by-step operational procedure.
Questions:
- "When you use this agent, what's the typical workflow? Walk me through the steps."
- "What should the agent do FIRST every time?"
- "How do you want the agent to communicate findings or results?"
- "What must be true before the agent considers its work 'complete'?" (quality gates)
Learning: Operational procedure and definition of done.
Offer common patterns:
- Research → Plan → Implement: Good for features
- Analyze → Report: Good for audits/reviews
- Test → Code → Test: Good for TDD
- Scan → Flag → Suggest: Good for quality checks
Phase 6: Success Criteria (2-3 questions)
Goal: Define measurable success.
Questions:
- "How will you know if this agent is working well? What does success look like?"
- "Can you describe an example of a great outcome from this agent?"
- "What would a poor outcome look like? What should I watch out for?"
Learning: How to measure effectiveness and failure modes to avoid.
Follow-up: "What metrics matter most? Time saved? Issues caught? Code quality?"
Phase 7: Custom Commands (1-2 questions, optional)
Goal: Identify repeated workflows needing shortcuts.
Questions:
- "Are there specific workflows you'll run repeatedly that deserve a shortcut command?"
- "What would you name these commands, and what would they do?"
Examples to offer:
/project:review- Full review with checklist/project:quick-scan- Fast critical-issues-only scan/project:generate-tests- Create test suite for a file
Note: Many agents work fine without custom commands.
After Interview: Generate Agent
Once all phases are complete, generate these artifacts:
1. Complete CLAUDE.md File
Structure using XML-style semantic sections. See references/claude-md-template.md for the complete template.
Key sections:
<role>- Agent identity and expertise<responsibilities>- Core and secondary duties<constraints>- Must NOT do, autonomy level, when to ask<workflow>- Step-by-step process with quality gates<patterns>- Good/bad code examples<communication_style>- How to interact with user<success_criteria>- What good looks like
2. Custom Command Files (if any)
Format: .claude/commands/command-name.md
Example structure:
[Command description]
Usage: /project:command-name [ARGS]
Steps:
1. [Step 1]
2. [Step 2]
$ARGUMENTS will be replaced with user input3. Quick Start Guide
Brief usage instructions showing how to deploy and use the agent.
Key Principles for Agent Design
Token Efficiency
- Write "what" not "how" (Claude already knows how to code)
- Use examples from actual codebase, not generic examples
- Document only what Claude doesn't already know
Progressive Disclosure
- Put frequently-needed info at top of CLAUDE.md
- Reference detailed docs via file paths (don't inline everything)
- Use XML tags to create scannable sections
Clear Boundaries
- Make constraints explicit with ❌ symbols
- Define quality gates with checkboxes
- Specify when to ask for approval
Concrete Examples
- Show good vs bad code patterns
- Use actual code from the codebase when possible
- Prefer specific examples over abstract rules
Resources
For detailed examples and templates:
- references/agent-templates.md - Pre-built agent templates for common roles
- references/claude-md-template.md - Complete CLAUDE.md structure template
- references/best-practices.md - Tips, troubleshooting, and advanced patterns
Interview Best Practices
Do:
- ✅ Ask one question at a time
- ✅ Use the user's language and terminology
- ✅ Provide examples when concepts are abstract
- ✅ Confirm understanding before moving to next phase
- ✅ Take note of specific examples they mention
Don't:
- ❌ Jump ahead - follow phases in order
- ❌ Make assumptions - ask clarifying questions
- ❌ Rush through - this is a design process
- ❌ Generate anything until all phases complete
Adaptation
For experienced users: Move faster, skip basic explanations
For new users: Provide more context and examples
For technical leads: Focus on team consistency and standards
For individual devs: Focus on personal workflow optimization
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

