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Zhongwei Li
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---
name: github-issue-processing
description: Extract actionable information from GitHub issues including requirements, acceptance criteria, and technical constraints. Use when analyzing GitHub issues or preparing them for workflow execution.
---
# GitHub Issue Processing
This skill provides patterns for extracting and structuring information from GitHub issues.
## Information Extraction Pattern
When processing a GitHub issue, extract:
### 1. Core Requirements
```markdown
## Requirements
- Primary objective
- User stories or use cases
- Expected behavior
```
### 2. Technical Details
```markdown
## Technical Context
- Affected components/modules
- Current behavior vs. desired behavior
- Stack traces or error messages (if applicable)
- Environment details
```
### 3. Acceptance Criteria
```markdown
## Acceptance Criteria
- [ ] Criterion 1: Specific, testable condition
- [ ] Criterion 2: Specific, testable condition
- [ ] Criterion 3: Specific, testable condition
```
### 4. Constraints
```markdown
## Constraints
- Performance requirements
- Backward compatibility needs
- Security considerations
- Dependencies on other issues/PRs
```
## Labels Interpretation
Common GitHub labels and their implications:
- **bug**: Fix existing functionality, requires root cause analysis
- **enhancement**: Add new functionality, may need design review
- **documentation**: Update docs, ensure accuracy and completeness
- **breaking-change**: Impacts existing API, needs migration guide
- **help-wanted**: Good for community contribution, provide clear guidance
- **priority: high**: Address urgently, may need expedited review
## Issue Linking
Extract and track related issues:
```markdown
## Related Issues
- Blocks: #123, #456
- Blocked by: #789
- Related to: #321
- Duplicate of: (if applicable)
```
## Converting to PRP
Transform issue analysis into PRP format:
1. **Prompt**: Use issue title and description
2. **Response**: Include analysis and proposed approach
3. **Plan**: Break down into actionable steps with dependencies
## Integration Points
Works with:
- `context-engineering-github-issue-analyzer` agent
- `context-engineering-prp-generator` agent for issue → PRP conversion
- `/initial-github-issue` command

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---
name: prp-structure
description: Provides the standard structure and format for Prompt-Response-Plan (PRP) documents. Use when creating, formatting, or validating PRP documents for systematic problem-solving workflows.
---
# PRP Structure Skill
This skill defines the standard structure for Prompt-Response-Plan (PRP) documents used in context engineering workflows.
## PRP Document Structure
A well-formed PRP contains three main sections:
### 1. Prompt Section
The initial problem statement or request that initiates the workflow:
```markdown
## Prompt
[Clear, concise description of the problem or task]
### Context
- Background information
- Relevant constraints
- Success criteria
```
### 2. Response Section
Analysis and proposed approach:
```markdown
## Response
### Problem Analysis
- Key challenges identified
- Dependencies and prerequisites
- Risk factors
### Proposed Solution
- High-level approach
- Alternative approaches considered
- Rationale for chosen approach
```
### 3. Plan Section
Detailed execution plan with trackable steps:
```markdown
## Plan
### Implementation Steps
1. [ ] Step 1: [Description]
- Sub-tasks if needed
- Expected outcome
2. [ ] Step 2: [Description]
- Dependencies: Step 1
- Expected outcome
3. [ ] Step 3: [Description]
### Testing Strategy
- Unit tests
- Integration tests
- Acceptance criteria
### Rollback Plan
- Contingency steps if issues arise
```
## Best Practices
- **Keep prompts focused**: One PRP per distinct problem
- **Be specific in plans**: Each step should be actionable and measurable
- **Track progress**: Use checkboxes to mark completed steps
- **Document assumptions**: Make implicit knowledge explicit
- **Include validation**: Define how success will be measured
## File Naming Convention
```
prp-[description]-[timestamp].md
```
Example: `prp-refactor-auth-module-2025-11-21.md`
## Integration with Agents
This structure works seamlessly with:
- `context-engineering-prp-generator` agent (generates PRPs)
- `context-engineering-executor` agent (executes plans)
- `context-engineering-orchestrator` agent (coordinates workflows)