Issue Analysis & Planning

This skill guides structured problem analysis and implementation planning before coding.

Workflow Overview

1. Discovery        → Add summary to doc/plans/module_issues.md
2. Planning         → Create gi_draft_<module>_<desc>.md with detailed plan
3. Review           → Review plan with user, refine until ready
4. Publish          → User creates GitHub issue, rename file to gi_<id>_<desc>.md
5. Implementation   → Work on issue, PRs link automatically
6. Completion       → Close GitHub issue, update module_issues.md status

Important: This skill creates LOCAL draft files only. The user reviews and manually publishes to GitHub.

Step 1: Problem Analysis

1.1 What is the current state?

• What exists now?
• What is the observed behavior?
• Where does this sit in the broader system?

1.2 What is the desired state?

• What should happen instead?
• Who benefits and how?
• What does "done" look like?

1.3 What is the gap?

• Why doesn't the current state meet the need?
• What's blocking progress?
• Are there dependencies or prerequisites?

1.4 What is the scope?

• What's included in this issue?
• What's explicitly excluded (for later)?
• What are the boundaries?

Step 2: Problem Description

Write a clear, concise problem statement following this structure:

**Context:** [Where does this fit? What's the background?]

**Problem:** [What's wrong or missing? One or two sentences.]

**Impact:** [Why does this matter? Who is affected?]

**Desired outcome:** [What should be true when this is resolved?]

Step 3: Implementation Planning

3.1 Approach options

List 2-3 possible approaches. For each:

• Brief description
• Pros
• Cons
• Rough effort estimate

3.2 Recommended approach

Select one approach and explain why.

3.3 Break into steps

List concrete implementation steps. Each step should be:

• Small enough to complete in one focused session
• Testable or verifiable
• Independent where possible

Example:

1. [ ] Create data model for X
2. [ ] Build function to fetch Y
3. [ ] Add validation for Z
4. [ ] Write tests for steps 1-3
5. [ ] Integrate with existing component W

3.4 Identify risks & unknowns

• What might go wrong?
• What do you need to learn or verify first?
• Are there external dependencies?

Step 4: Create Local Issue File

File naming

Draft (before GitHub publication):

doc/plans/issues/gi_draft_<module>_<short_description>.md

Published (after GitHub issue created):

doc/plans/issues/gi_<github_id>_<short_description>.md

Module abbreviations

prepq

prepg

prepf

lr

ml

pp

fd

cd

p

iEHF

r

infra

Issue file template

The issue file must be standalone — a junior developer should be able to implement it without access to the full project context.

# <Title>

**Status**: Draft | Ready for Review | Published (GitHub #XX) | In Progress | Complete
**Module**: <module name>
**Priority**: Critical | High | Medium | Low
**Labels**: `<label1>`, `<label2>`

---

## Summary

[One sentence describing what this issue accomplishes]

## Context

[Background — why this matters, where it fits in the system. Include enough detail that someone unfamiliar with the project can understand.]

## Problem

[What's wrong or missing. Be specific about observed vs expected behavior.]

## Desired Outcome

[What "done" looks like — specific, verifiable criteria]

---

## Technical Analysis

### Current Implementation

[Describe relevant existing code. Include file paths and line numbers.]

**Key files:**
- `apps/<module>/path/to/file.py` - Description of what this file does
- `apps/<module>/path/to/other.py:123-145` - Specific function/section

### Root Cause

[For bugs: what is causing the issue. For features: what's missing.]

---

## Implementation Plan

### Approach

[Describe the chosen approach and why it was selected over alternatives.]

### Files to Create

| File | Purpose |
|------|---------|
| `path/to/new_file.py` | Description |

### Files to Modify

| File | Changes |
|------|---------|
| `path/to/existing.py` | Description of changes |

### Implementation Steps

- [ ] Step 1: [Specific, actionable task]
- [ ] Step 2: [Specific, actionable task]
- [ ] Step 3: [Specific, actionable task]

### Code Examples

[Include specific code snippets showing the expected implementation. This helps ensure consistency and reduces ambiguity.]

```python
# Example of new function signature
def new_function(param1: str, param2: int) -> dict:
    """
    Brief description.

    Args:
        param1: Description
        param2: Description

    Returns:
        Description of return value
    """
    pass

Testing

Test Cases

• Test case 1: [Description of what to test]
• Test case 2: [Description of what to test]

Testing Commands

# How to run tests for this change
cd apps/<module>
uv run pytest tests/ -v -k "test_name"

Manual Verification

[Steps to manually verify the fix/feature works]

Out of Scope

[What this issue explicitly does NOT address — important for preventing scope creep]

Dependencies

[Other issues, external factors, or prerequisites that must be completed first]

Acceptance Criteria

• Criterion 1: [Specific, measurable]
• Criterion 2: [Specific, measurable]
• All existing tests pass
• New tests cover the changes
• Code follows project conventions (see

  .serena/memories/code_style_conventions.md

  )

References

• Related issues: #XX, #YY
• Planning docs:

  doc/plans/<related_doc>.md

• External docs: Link

---

## Step 5: Update Issue Index

After creating the issue file, add an entry to `doc/plans/module_issues.md`:

```markdown
### Issue <MODULE>-NNN: [Brief title]
**Status**: Draft | Ready | In Progress | Complete
**Priority**: High | Medium | Low
**File**: `issues/gi_draft_<module>_<desc>.md`
**GitHub**: #XX (after publication)

[One-line description]

Checklist Before Marking Ready for Review

• Problem is clearly understood and documented
• Scope is defined and bounded
• Technical analysis includes specific file paths and line numbers
• Implementation steps are small and testable
• Code examples show expected patterns
• Test cases are defined
• Acceptance criteria are specific and measurable
• A junior developer could implement this without asking questions
• Entry added to

  doc/plans/module_issues.md

Tips

Keep issues small. If you have more than 5-7 implementation steps, consider splitting into multiple issues.

Write for a junior developer. Assume someone unfamiliar with the codebase will implement this. Include file paths, function names, and code examples.

Define "done" precisely. Vague outcomes lead to scope creep. "Works correctly" is not a criterion; "Returns valid JSON for all input cases in test suite" is.

Surface unknowns early. If something needs investigation, make that a separate spike issue rather than bundling it with implementation.

Include code examples. Showing the expected function signature, data structure, or pattern reduces ambiguity and speeds up implementation.

Reference specific locations. Use

file.py:123

Related Skills

• executing-issues: Use to implement the issue once the plan is ready
• brainstorming: Use before planning if requirements are unclear
• software-architecture: Reference for code conventions