agent-output-formats

Agent Output Formats Skill

Standardized output formats for all agent types to ensure consistent communication and parsing across the autonomous development workflow.

When This Skill Activates

Generating agent outputs
Parsing agent responses
Formatting research findings
Creating planning documents
Reporting implementation results
Writing code reviews
Keywords: "output", "format", "research", "planning", "implementation", "review"

Research Agent Output Format

Research agents (e.g., researcher, issue-creator, brownfield-analyzer) should structure outputs with these sections:

Template

Patterns Found

[List of discovered patterns with examples]

Pattern Name: Description
- Example: Code snippet or reference
- Use case: When to apply this pattern

Best Practices

[Industry best practices and recommendations]

Practice Name: Description
- Benefit: Why this matters
- Implementation: How to apply

Security Considerations

[Security implications and requirements]

Security Concern: Description
- Risk: Potential vulnerabilities
- Mitigation: How to address

Recommendations

[Actionable recommendations for implementation]

Recommendation: Detailed guidance
- Priority: High/Medium/Low
- Effort: Time estimate
- Impact: Expected benefit

Example Output

See examples/research-output-example.md for a complete example.

Planning Agent Output Format

Planning agents (e.g., planner, migration-planner, setup-wizard) should structure outputs with these sections:

Template

Feature Summary

[Brief description of what will be built]

Goal: What this achieves Scope: What's included/excluded Success Criteria: How to measure success

Architecture

[High-level design and component relationships]

Components: List of major components Data Flow: How data moves through system Integration Points: External dependencies

Components

[Detailed component specifications]

Component 1: [Name]

Purpose: What it does
Responsibilities: Core functions
Dependencies: What it needs
Files: Where it lives

Implementation Plan

[Step-by-step implementation guide]

Phase 1: [Description]

Step one
Step two

Phase 2: [Description]

Step one
Step two

Risks and Mitigations

[Potential issues and how to address them]

Risk: Description
- Impact: Severity and consequences
- Mitigation: How to prevent or handle

Example Output

See examples/planning-output-example.md for a complete example.

Implementation Agent Output Format

Implementation agents (e.g., implementer, retrofit-executor) should structure outputs with these sections:

Template

Changes Made

[Summary of what was implemented]

Feature: What was built Approach: How it was implemented Design Decisions: Key choices made

Files Modified

[List of changed files with descriptions]

Created Files

path/to/file.py: Description of new file
path/to/test.py: Test coverage

Modified Files

path/to/existing.py: Changes made
- Added: New functionality
- Modified: Updated behavior
- Removed: Deprecated code

Tests Updated

[Test coverage changes]

New Tests:

Test file: What it covers
Coverage: Percentage or lines

Updated Tests:

Test file: What changed
Reason: Why it was needed

Next Steps

[Follow-up actions and recommendations]

Action: What needs to happen next
- Owner: Who should do it
- Priority: Urgency level
- Blockers: Any dependencies

Example Output

See examples/implementation-output-example.md for a complete example.

Review Agent Output Format

Review agents (e.g., reviewer, security-auditor, quality-validator) should structure outputs with these sections:

Template

Findings

[Overview of review results]

Reviewed: What was examined Scope: What was checked Summary: High-level results

Code Quality

[Code quality assessment]

Strengths

Aspect: What's done well
- Evidence: Specific examples

Areas for Improvement

Issue: What needs work
- Severity: Critical/Major/Minor
- Recommendation: How to fix
- Location: Where the issue is

Security

[Security analysis]

Security Strengths

Protection: What's secure
- Implementation: How it's done

Security Concerns

Vulnerability: Potential issue
- CWE Reference: Standard classification
- Risk Level: High/Medium/Low
- Remediation: How to fix

Documentation

[Documentation assessment]

Documentation Completeness

Aspect: What's documented
- Quality: How well it's done

Documentation Gaps

Missing: What needs docs
- Priority: How important
- Suggestion: What to add

Verdict

[Final recommendation]

Status: ✅ APPROVED / ⚠️ APPROVED WITH CHANGES / ❌ NEEDS REVISION

Rationale: Why this verdict Blockers: Must-fix issues (if any) Suggestions: Nice-to-have improvements

Example Output

See examples/review-output-example.md for a complete example.

Commit Message Format

Commit message generator agents should follow conventional commits:

Template

<body>

Types

feat : New feature
fix : Bug fix
docs : Documentation only
style : Formatting, no code change
refactor : Code restructuring
test : Adding tests
chore : Maintenance tasks

Example

feat(skills): add agent-output-formats skill for standardized outputs

Extracts duplicated output format specifications from 15 agent prompts into a reusable skill package following progressive disclosure architecture.

Token savings: ~3,000 tokens (200 tokens per agent × 15 agents)

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>

Pull Request Format

PR description generator agents should follow this structure:

Template

Summary

[Brief description of changes]

Key change 1
Key change 2
Key change 3

Test Plan

Unit tests pass
Integration tests pass
Manual testing completed
Documentation updated

Related Issues

Closes #XXX

🤖 Generated with Claude Code

Usage Guidelines

For Agent Authors

When creating or updating agent prompts:

Reference this skill in the "Relevant Skills" section
Remove duplicate format specifications from agent prompts
Trust progressive disclosure - full content loads when needed
Use consistent terminology from this skill

For Claude

When executing agents:

Load this skill when keywords match ("output", "format", etc.)
Follow format templates for structured outputs
Include all required sections for agent type
Maintain consistency across similar agents

Token Savings

By centralizing output formats in this skill:

Before: ~250 tokens per agent for format specification
After: ~50 tokens for skill reference
Savings: ~200 tokens per agent
Total: ~3,000 tokens across 15 agents (8-12% reduction)

Progressive Disclosure

This skill uses Claude Code 2.0+ progressive disclosure architecture:

Metadata (frontmatter): Always loaded (~150 tokens)
Full content: Loaded only when keywords match
Result: Efficient context usage, scales to 100+ skills

When you use terms like "output format", "research findings", "planning document", or "code review", Claude Code automatically loads the full skill content to provide detailed guidance.

Examples

Complete example outputs are available in the examples/ directory:

research-output-example.md : Sample research agent output
planning-output-example.md : Sample planning agent output
implementation-output-example.md : Sample implementation agent output
review-output-example.md : Sample review agent output

Refer to these examples when generating agent outputs to ensure consistency and completeness.