133 lines
3.9 KiB
Markdown
133 lines
3.9 KiB
Markdown
# Implementer Agent Skill Section Example
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Real-world example from `implementer.md` agent showing effective skill integration.
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## Original (Before Streamlining)
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```markdown
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## Relevant Skills
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You have access to these specialized skills during implementation:
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- **agent-output-formats**: Standardized output formats for agent responses
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- **python-standards**: Python code style, type hints, docstring conventions
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- Use for writing clean, idiomatic Python code
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- Reference for naming conventions and code organization
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- **observability**: Logging patterns, monitoring, and debugging strategies
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- Apply when adding logging or monitoring to code
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- **error-handling-patterns**: Standardized error handling and validation
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- Use for consistent error messages and exception handling
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When implementing features, consult these skills to ensure your code follows project standards and best practices.
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```
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**Token Count**: ~150 tokens
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## Streamlined (After Streamlining)
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```markdown
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## Relevant Skills
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You have access to these specialized skills during implementation:
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- **python-standards**: Follow for code style, type hints, and docstrings
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- **observability**: Use for logging and monitoring patterns
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- **error-handling-patterns**: Apply for consistent error handling
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Consult the skill-integration-templates skill for formatting guidance.
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```
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**Token Count**: ~70 tokens
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**Token Savings**: 80 tokens (53% reduction)
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## Key Improvements
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1. **Removed verbose sub-bullets** - Eliminated "Use for...", "Reference for..." details
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2. **One line per skill** - Concise purpose statements
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3. **Action verbs** - "Follow", "Use", "Apply" match implementation context
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4. **Meta-skill reference** - Points to skill-integration-templates
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5. **Removed agent-output-formats** - Not needed in Relevant Skills section (referenced elsewhere)
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## Why This Works
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### Progressive Disclosure
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- Full python-standards skill (~2,000 tokens) loads on-demand
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- Full observability skill (~1,500 tokens) loads on-demand
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- Full error-handling-patterns skill (~1,200 tokens) loads on-demand
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- Context overhead: 70 tokens vs. 150 tokens
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### Token Efficiency
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- 150 tokens → 70 tokens (80 token savings)
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- No functionality lost
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- Same skills available
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### Maintained Quality
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- Implementer knows which skills to reference
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- Action verbs guide usage
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- Progressive disclosure handles details
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## Usage in implementer.md
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**Location**: `plugins/autonomous-dev/agents/implementer.md`
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**Full Context**:
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```markdown
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---
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name: implementer
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description: Code implementation following architecture plans
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model: opus
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tools: [Read, Write, Edit, Grep, Glob, Bash]
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---
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You are the **implementer** agent.
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## Your Mission
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Write production-quality code following the architecture plan. Make tests pass if they exist.
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[agent-specific mission and workflow]
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## Relevant Skills
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You have access to these specialized skills during implementation:
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- **python-standards**: Follow for code style, type hints, and docstrings
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- **observability**: Use for logging and monitoring patterns
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- **error-handling-patterns**: Apply for consistent error handling
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Consult the skill-integration-templates skill for formatting guidance.
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[rest of agent prompt]
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```
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## Comparison: Verbose vs. Concise
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### Verbose (Bad)
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```markdown
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- **python-standards**: Python code style, type hints, docstring conventions
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- Use for writing clean, idiomatic Python code
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- Reference for naming conventions and code organization
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- Apply for documentation standards
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```
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**Why Bad**:
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- 4 lines for one skill (80+ tokens)
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- Duplicates content from python-standards skill
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- Defeats progressive disclosure purpose
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### Concise (Good)
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```markdown
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- **python-standards**: Follow for code style, type hints, and docstrings
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```
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**Why Good**:
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- 1 line for one skill (~15 tokens)
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- Progressive disclosure loads details on-demand
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- Token efficient
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## Related Examples
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- `planner-skill-section.md` - Planner agent example
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- `minimal-skill-reference.md` - Minimal reference pattern
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