agent-creator
- Repo stars 0
- Author updated Live
- Author repo claude-code
- Domain
- Design
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 85 / 100 · community maintained
- Author / version / license
- @agdev · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- 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。; 上游仓库已 190 天未更新,可能与最新 agent 行为不一致。
---
name: agent-creator
description: Expert guidance for creating effective Claude Code agents (subagents). Use when users want to cr…
category: design
runtime: no special runtime
---
# agent-creator output preview
## PART A: Task fit
- Use case: Expert guidance for creating effective Claude Code agents (subagents). Use when users want to create a new agent, update an existing agent, or learn agent design best practices. Covers agent architecture, prompt engineering, tool selection, model choice, and common pitfalls. Integrates with skill-creator when agent needs accompanying skills..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “About Agents / Agents vs Skills / Agent Architecture” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Expert guidance for creating effective Claude Code agents (subagents). Use when users want to create a new agent, update an existing agent, or learn agent design best practices. Covers agent architecture, prompt engineering, tool selection, model choice, and common pitfalls. Integrates with skill-creator when agent needs accompanying skills.”.
- **02** When the source has headings, the agent prioritizes “About Agents / Agents vs Skills / Agent Architecture” 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, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; 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 does not require a stable slash command. After installation, invoke the skill by name and describe the task.
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, run shell commands.
Start with a small task and check whether the result follows “About Agents / Agents vs Skills / Agent Architecture”. 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: agent-creator
description: Expert guidance for creating effective Claude Code agents (subagents). Use when users want to cr…
category: design
source: agdev/claude-code
---
# agent-creator
## When to use
- Expert guidance for creating effective Claude Code agents (subagents). Use when users want to create a new agent, upda…
- 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 “About Agents / Agents vs Skills / Agent Architecture” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; 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 "agent-creator" {
input -> user goal + target files + boundaries + acceptance criteria
context -> About Agents / Agents vs Skills / Agent Architecture
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Agent Creator
Expert guidance for creating effective Claude Code agents following best practices.
About Agents
Agents are specialized subprocesses that handle complex tasks autonomously with isolated context windows. They extend Claude's capabilities by providing focused expertise, specific tool access, and appropriate model selection for different task types.
Agents vs Skills
| Aspect | Agents | Skills |
|---|---|---|
| Context | Isolated context window | Shared with main conversation |
| Purpose | Autonomous task execution | Procedural knowledge & guidance |
| Model | Can specify different model | Uses current model |
| Tools | Can restrict/expand tool access | Uses available tools |
| Use when | Task needs isolation, different capabilities, or parallel execution | Task benefits from guidance staying in current context |
Choose agents when:
- Task benefits from isolated context (clean slate)
- Different model is optimal (haiku for speed, opus for reasoning)
- Tool access should be restricted for security
- Tasks can run in parallel
Choose skills when:
- Knowledge should stay in current conversation
- Guidance benefits from seeing full context
- No special tool/model requirements
Agent Architecture
File Structure
Agents are Markdown files with YAML frontmatter stored in:
- Project agents:
.claude/agents/(highest priority) - User agents:
~/.claude/agents/(all projects)
Configuration Format
---
name: agent-identifier # Required: lowercase, hyphens only
description: When and why... # Required: PRIMARY TRIGGER MECHANISM
tools: Tool1, Tool2, Tool3 # Optional: restrict/specify tools
model: sonnet # Optional: sonnet, opus, haiku
skills: skill1, skill2 # Optional: auto-load skills
color: blue # Optional: UI indicator
---
[Agent system prompt here - Markdown instructions]
Configuration Fields
| Field | Required | Details |
|---|---|---|
name |
Yes | Lowercase letters and hyphens only (e.g., code-reviewer) |
description |
Yes | PRIMARY TRIGGERING MECHANISM - determines when agent is invoked |
tools |
No | Comma-separated list; omit to inherit all tools |
model |
No | haiku (fast), sonnet (balanced), opus (reasoning) |
skills |
No | Skills to auto-load when agent invokes |
color |
No | UI color indicator |
Core Principles
1. Single-Purpose Focus
Create focused, single-responsibility agents:
- One clear job per agent
- Better composability and predictability
- Easier to debug and maintain
2. Description is Everything
The description field is the PRIMARY TRIGGERING MECHANISM. Include:
- WHAT the agent does
- WHEN to use it (trigger scenarios)
- Specific examples in angle brackets
Pattern:
description: [Role/capability]. MUST BE USED for [specific scenarios]. [Additional context]. Examples: "[trigger phrase 1]", "[trigger phrase 2]"
3. Minimal Viable Toolset
Grant only necessary tools:
- Read-only agents: Glob, Grep, Read, LS
- Analysis agents: Add WebFetch, WebSearch
- Planning agents: Add Write, Edit, TodoWrite
- Implementation agents: Add Bash, Task
Performance impact:
- 3-5 tools: ~2-5k tokens
- 7-8 tools: ~7-10k tokens
- 15+ tools: 15-25k tokens (avoid)
4. Appropriate Model Selection
- Haiku: Fast, lightweight tasks (exploration, simple analysis)
- Sonnet: Balanced tasks (orchestration, validation, general work)
- Opus: Complex reasoning (planning, architecture, research)
Agent Creation Process
Step 1: Define Purpose
Before creating, answer:
- What specific problem does this agent solve?
- When should Claude automatically invoke this agent?
- What makes this different from existing agents?
- Does this need isolation, or would a skill suffice?
Step 2: Design Description
Write a comprehensive description:
- Start with role/capability statement
- Add "MUST BE USED" or "use PROACTIVELY" for strong triggers
- Include specific scenarios and trigger phrases
- Add examples using XML tags for complex scenarios
Step 3: Select Tools
Determine minimum tools needed:
- List all operations the agent must perform
- Map operations to specific tools
- Remove any tools not strictly necessary
- Consider security implications
Step 4: Choose Model
Select based on task complexity:
- Simple/fast tasks →
haiku - Standard tasks →
sonnetor omit (default) - Complex reasoning →
opus
Step 5: Write System Prompt
Structure the prompt body:
# [Role Title]
[One-sentence mission statement]
## Core Principles
- [Principle 1]: [Brief explanation]
- [Principle 2]: [Brief explanation]
## Responsibilities / Methodology
1. [Step/responsibility 1]
2. [Step/responsibility 2]
## [Workflow/Process] (if multi-step)
### Step 1: [Name]
[Instructions]
## Quality Standards / Output Format
[Checklists, criteria, format requirements]
## Constraints (if critical)
- [What agent MUST NOT do]
- [Boundaries and limitations]
Step 6: Define Boundaries
Add explicit boundaries if agent has limited scope:
## YOUR ROLE ENDS HERE
**CRITICAL BOUNDARY**: You are strictly a [role]. Once you have [completed work]:
- Your work is COMPLETE
- DO NOT [prohibited action 1]
- DO NOT [prohibited action 2]
Prompt Engineering Best Practices
Use Imperative Form
- "Identify issues" not "You should identify"
- "Generate report" not "The report should be generated"
Strong Emphasis Markers
- MUST, NEVER, ALWAYS, CRITICAL for non-negotiables
- Bold or capitalize critical constraints
- Use strategically, not everywhere
Include Examples
- Good vs Bad examples (Use checkmarks/X marks)
- Concrete user scenarios
- Before/after for transformations
Be Specific, Not Vague
- "Test min/max values, zero, negative" not "test edge cases"
- "Check for SQL injection, XSS, path traversal" not "check security"
- Include decision criteria and thresholds
Common Pitfalls to Avoid
Over-Engineering
- Creating 15+ tool agents with 30k token prompts
- One mega-agent instead of composable focused agents
- Adding features "just in case"
Solution: Start lightweight (<3k tokens, 3-5 tools), expand based on need
Vague Descriptions
- "Helps with coding" - too generic
- Missing "when to use" information
- No concrete trigger examples
Solution: Specific triggers, scenarios, and examples
Tool Overload
- Giving all tools "just in case"
- Read-only agents with Write/Bash access
- Security risk and performance hit
Solution: Minimal viable toolset per agent purpose
Missing Boundaries
- Agent scope creeps during execution
- No clear "done" criteria
- Agent attempts tasks outside its role
Solution: Explicit "YOUR ROLE ENDS HERE" section for bounded agents
Ignoring Token Budget
- Verbose explanations Claude already knows
- Repeated information
- Unnecessary examples
Solution: Challenge each paragraph - "Does this justify its token cost?"
Integration with Skills
When an agent needs domain expertise, use skills:
---
name: specialized-agent
skills: domain-skill, helper-skill
---
When to create an accompanying skill:
- Agent needs substantial domain knowledge
- Knowledge is reusable across multiple agents
- Information would bloat agent prompt
Use the skill-creator skill to create accompanying skills:
Invoke skill: skill-creator
Quality Checklist
Before finalizing an agent:
- Name: Lowercase with hyphens, descriptive
- Description: Includes WHAT, WHEN, and examples
- Tools: Minimal viable set for the task
- Model: Appropriate for task complexity
- Prompt: Follows structure (role → principles → methodology → standards)
- Boundaries: Clear scope and "done" criteria (if bounded)
- Token efficiency: <5k words for most agents
- Examples: Concrete scenarios in description or prompt
- No overlap: Doesn't duplicate existing agent functionality
Example: Well-Designed Agent
---
name: code-reviewer
description: Expert code review specialist. MUST BE USED when reviewing pull requests, code changes, or architecture decisions. Focuses on security, performance, maintainability, and best practices. Examples: "Review this PR", "Check this implementation for issues", "Review the changes in feature branch"
tools: Glob, Grep, Read, LS, WebSearch
model: sonnet
color: yellow
---
# Code Review Specialist
You are an expert code reviewer focused on identifying issues that impact security, performance, and maintainability.
## Review Priorities
1. **Security**: Injection vulnerabilities, auth issues, data exposure
2. **Performance**: O(n²) operations, memory leaks, unnecessary computation
3. **Maintainability**: Code clarity, naming, separation of concerns
4. **Best Practices**: Framework conventions, error handling, testing
## Review Process
1. Understand the change context (what problem is being solved)
2. Review for security vulnerabilities first
3. Check performance implications
4. Assess code quality and maintainability
5. Verify test coverage
## Output Format
For each issue found:
- **Severity**: Critical/High/Medium/Low
- **Location**: file:line
- **Issue**: Clear description
- **Recommendation**: Specific fix suggestion
## YOUR ROLE ENDS HERE
You are strictly a reviewer. After completing review:
- Your work is COMPLETE
- DO NOT implement fixes
- DO NOT modify code
Resources
- See
references/prompt-patterns.mdfor advanced prompt engineering patterns - Use
skill-creatorskill when agent needs accompanying skills
Decide Fit First
Design Intent
How To Use It
Boundaries And Review