TradingAgents/.claude/skills/skill-integration-templates/examples/implementer-skill-section.md

Implementer Agent Skill Section Example

Real-world example from implementer.md agent showing effective skill integration.

Original (Before Streamlining)

## Relevant Skills

You have access to these specialized skills during implementation:

- **agent-output-formats**: Standardized output formats for agent responses
- **python-standards**: Python code style, type hints, docstring conventions
  - Use for writing clean, idiomatic Python code
  - Reference for naming conventions and code organization
- **observability**: Logging patterns, monitoring, and debugging strategies
  - Apply when adding logging or monitoring to code
- **error-handling-patterns**: Standardized error handling and validation
  - Use for consistent error messages and exception handling

When implementing features, consult these skills to ensure your code follows project standards and best practices.

Token Count: ~150 tokens

Streamlined (After Streamlining)

## Relevant Skills

You have access to these specialized skills during implementation:

- **python-standards**: Follow for code style, type hints, and docstrings
- **observability**: Use for logging and monitoring patterns
- **error-handling-patterns**: Apply for consistent error handling

Consult the skill-integration-templates skill for formatting guidance.

Token Count: ~70 tokens

Token Savings: 80 tokens (53% reduction)

Key Improvements

1. Removed verbose sub-bullets - Eliminated "Use for...", "Reference for..." details
2. One line per skill - Concise purpose statements
3. Action verbs - "Follow", "Use", "Apply" match implementation context
4. Meta-skill reference - Points to skill-integration-templates
5. Removed agent-output-formats - Not needed in Relevant Skills section (referenced elsewhere)

Why This Works

Progressive Disclosure

• Full python-standards skill (~2,000 tokens) loads on-demand
• Full observability skill (~1,500 tokens) loads on-demand
• Full error-handling-patterns skill (~1,200 tokens) loads on-demand
• Context overhead: 70 tokens vs. 150 tokens

Token Efficiency

• 150 tokens → 70 tokens (80 token savings)
• No functionality lost
• Same skills available

Maintained Quality

• Implementer knows which skills to reference
• Action verbs guide usage
• Progressive disclosure handles details

Usage in implementer.md

Location: plugins/autonomous-dev/agents/implementer.md

Full Context:

---
name: implementer
description: Code implementation following architecture plans
model: opus
tools: [Read, Write, Edit, Grep, Glob, Bash]
---

You are the **implementer** agent.

## Your Mission

Write production-quality code following the architecture plan. Make tests pass if they exist.

[agent-specific mission and workflow]

## Relevant Skills

You have access to these specialized skills during implementation:

- **python-standards**: Follow for code style, type hints, and docstrings
- **observability**: Use for logging and monitoring patterns
- **error-handling-patterns**: Apply for consistent error handling

Consult the skill-integration-templates skill for formatting guidance.

[rest of agent prompt]

Comparison: Verbose vs. Concise

Verbose (Bad)

- **python-standards**: Python code style, type hints, docstring conventions
  - Use for writing clean, idiomatic Python code
  - Reference for naming conventions and code organization
  - Apply for documentation standards

Why Bad:

• 4 lines for one skill (80+ tokens)
• Duplicates content from python-standards skill
• Defeats progressive disclosure purpose

Concise (Good)

- **python-standards**: Follow for code style, type hints, and docstrings

Why Good:

• 1 line for one skill (~15 tokens)
• Progressive disclosure loads details on-demand
• Token efficient

Related Examples

• planner-skill-section.md - Planner agent example
• minimal-skill-reference.md - Minimal reference pattern