Christophevg C3 Develop Agent

Develop Agent Skill

Guides the complete workflow for developing new Claude Code agents, from initial description to tested, documented agent ready for use.

Overview

Research Foundation

This skill incorporates best practices from comprehensive research on AI agent development (see research/2026-04-08-ai-agent-development-best-practices/):

• Task-specific design over role-based agents
• Five-section system prompt framework
• Least-privilege tool access for security
• Verification criteria as highest-leverage practice

When to Use

• User says "create an agent that..."
• User runs /develop-agent "description"
• User describes wanting a specialized agent for a task
• User wants to formalize a workflow into an agent

Workflow

Step 1: Initial Interview

Ask these agent-specific questions to clarify scope:

Scope & Purpose:

• What is the primary function of this agent?
• What problem does it solve?
• Who will use it (developer, analyst, end user)?

Input & Output:

• What inputs will the agent receive?
• What outputs should it produce?
• What format should outputs be in?

Tools & Capabilities:

• What tools does it need? (Read, Write, Edit, Grep, Glob, Bash, WebSearch, WebFetch, Skill)
• Should it spawn other agents?
• Any tools it should NOT have?

Constraints:

• What should it NOT do?
• Any scope boundaries?
• What decisions should it make vs. ask the user?

Narrow Scope Validation:

Apply the Three Tests for Narrow Scope:

1. Trigger Test: Does this agent have exactly one trigger condition?

   • If writing "when X or when Y," you have two agents in one
   • Each agent should have ONE specific situation

2. Action Test: Does this agent produce exactly one type of output?

   • If actions can fire independently, they should be separate agents
   • One output type per agent

3. Failure Test: If one part fails, does the rest still make sense?

   • Independent failure modes indicate separate agents
   • Contained failure scope

Complexity Detection:

If answers reveal complex functionality (multiple workflows, intricate logic, many edge cases):

• Spawn functional-analyst for deep requirements analysis
• Wait for functional-analyst to complete
• Continue with its analysis document

If straightforward (clear, focused task):

• Continue with the interview and create analysis directly

Step 2: Create Analysis Document

ALWAYS create an analysis document in the idea folder:

ideas/{agent-name}/analysis/functional.md

Analysis document structure:

# {Agent Name} - Functional Analysis

**Date:** YYYY-MM-DD
**Analyst:** [skill or functional-analyst]

## Purpose

[One paragraph describing what the agent does and why]

## Scope

### In Scope
- [Task 1]
- [Task 2]

### Out of Scope
- [Excluded task 1]
- [Excluded task 2]

## Inputs

| Input | Type | Description |
|-------|------|-------------|
| [input1] | [type] | [description] |

## Outputs

| Output | Type | Location |
|--------|------|----------|
| [output1] | [type] | [path] |

## Tools Required

| Tool | Usage | Risk Level |
|------|-------|------------|
| [tool1] | [what it's used for] | [read/modify/delete] |

## Constraints

- [Constraint 1]
- [Constraint 2]

## Workflow

1. [Step 1]
2. [Step 2]
3. [Step 3]

## Example Scenarios

### Scenario 1: [Name]
**Input:** ...
**Expected Output:** ...

## Decisions Made

| Decision | Rationale |
|----------|-----------|
| [decision] | [why] |

## Related Agents

- [agent1] - [relationship]
- [agent2] - [relationship]

## Scope Validation

- Trigger Test: [single trigger condition]
- Action Test: [single output type]
- Failure Test: [contained failure scope]

Step 3: Design Agent Structure

Color Selection

Step 4: Create Agent Definition

Create the agent file:

ideas/{agent-name}/artifacts/agent/{agent-name}.md

Five-Section System Prompt Framework

See references/system-prompt.md for the complete framework. Every agent must include:

1. Identity and Role
2. Capabilities and Constraints
3. Tool Instructions
4. Output Format
5. Guardrails and Error Handling

Agent Frontmatter Template

---
name: {agent-name}
description: [One-line purpose]. Use when [trigger conditions]. Examples: "[Example request 1]", "[Example request 2]", "[Example request 3]".
model: inherit
color: [blue|cyan|green|yellow|magenta|red|orange|purple|indigo]
tools: [list of tools]
---

# {Agent Name}

[Five-section system prompt following framework above]

IMPORTANT: YAML Frontmatter Format

The frontmatter must use single-line descriptions with inline examples. Multi-line content breaks YAML parsing:

✅ Correct format:

---
name: my-agent
description: One-line description. Use when X. Examples: "Example 1", "Example 2", "Example 3".
tools: Read, Grep
color: blue
---

❌ Broken format (DO NOT USE):

---
description: One-line description. Examples:

<example>
Context: ...
user: "..."
</example>

tools: Read
---

The <example> blocks are NOT parsed as part of the description field - they're treated as unknown YAML keys and ignored.

Description Best Practices

The description field is critical—Claude uses it to decide when to delegate.

1. Single-line format: Description must be ONE line. Use inline examples.

   description: Reviews code for quality. Use after implementation. Examples: "Review src/auth/", "Check PR #42".
   

2. Include specific conditions: "Use proactively after code changes"
3. Add trigger examples: "When reviewing Python files"
4. Be precise: Avoid vague descriptions like "helpful assistant"
5. Mention limitations: "Read-only, does not modify files"

Step 5: Symlink and Validate

Security Validation

Apply Least-Privilege Tool Access:

Tool Access by Use Case:

High-Risk Actions Requiring Approval:

• Anything touching payroll, HR, legal, or medical content
• Exports
• Deletions
• Permission changes
• Refunds
• Billing changes
• Public posting
• External emails

Symlink Creation

1. Symlink to local agents folder:

ln -sf ideas/{agent-name}/artifacts/agent/{agent-name}.md .claude/agents/{agent-name}.md

2. Verify the agent is detected by checking system reminders

3. Create documentation:

• README.md with usage examples
• Add to project's agent index if applicable

Testing Framework

Test Suite Components:

Testing Checklist:

• Agent triggers correctly from description examples
• Produces expected output format
• Handles edge cases gracefully
• Respects tool restrictions
• Does not exceed scope constraints
• Error handling works as specified

Personalization

Agent definitions should be generic and reusable, not hardcoded for specific users. Personalization comes from:

What NOT to hardcode in agents:

• Personal names (use "the user" or reference PERSONAL.md)
• Personal pronouns for the agent (let the user name their agent)
• User-specific prefixes or formats (make them configurable)
• Project-specific paths (use parameters)

Agent identity vs. user identity:

• Agent content describes capabilities and behavior
• PERSONAL.md provides user context and preferences
• The combination creates a personalized experience

Example:

# Good: Generic agent
"This agent helps process unstructured input..."

# Bad: Hardcoded personalization
"Hello Christophe, I'm Eira, your personal assistant..."

Proven Agent Patterns

See references/patterns.md for the complete pattern catalog including:

• Role + Constraints
• Chain of Verification
• Structured Output Enforcement
• Tool Selection Heuristics
• Error Recovery Instructions
• Context Window Management
• Guard Rails Pattern
• Progressive Disclosure
• Memory Integration
• Self-Evaluation Loop

Also includes common agent mistakes to avoid.

Hierarchy Design

See references/hierarchy.md for multi-agent system design including:

• Three-tier structure (Orchestrator, Team Lead, Specialist)
• Five hierarchy rules
• When to create new agents vs. avoid creating them

Incubator Workflow

When working in the incubator project (~/Workspace/agentic/incubator), see references/incubator-workflow.md for additional steps:

• KB Integration (checking and updating knowledge base)
• Registry Update (maintaining .claude/REGISTRY.md)

Example Workflow

User: "Create an agent to analyze API responses"

Skill Response:

1. Interview:

   • "What kind of APIs? (REST, GraphQL, gRPC)"
   • "What analysis? (performance, structure, errors, security)"
   • "What output format? (report, JSON, markdown)"
   • "Should it read local files or make HTTP requests?"

2. Scope Validation (Three Tests):

   • Trigger Test: "When user asks to analyze API responses" ✓
   • Action Test: Produces analysis report ✓
   • Failure Test: If analysis fails, report issue and stop ✓

3. Create Analysis:

   • Document scope, inputs, outputs, tools
   • Define workflow steps
   • Add example scenarios

4. Design:

   • Color: blue (analysis)
   • Tools: Read, Glob, Grep, Write (for report)
   • Apply five-section framework

5. Create Agent:

   • Write agent definition with all five sections
   • Add examples in description
   • Define output format
   • Add error handling

6. Symlink and Document:

   • Create symlink
   • Add README with usage
   • Update registry

Related Skills

• develop-skill - Complementary workflow for skill creation
• functional-analyst - Deep requirements analysis for complex agents
• researcher - Research before agent creation
• code-reviewer - Review agent implementations

Source: christophevg/c3 — distributed by TomeVault.