ln-521-test-researcher

Paths: File paths (references/, ../ln-*) are relative to this skill directory.

Inputs

Input	Required	Source	Description
storyId	Yes	args, git branch, kanban, user	Story to process

Resolution: Story Resolution Chain. Status filter: To Review

Test Researcher

Type: L3 Worker

Researches real-world problems and edge cases before test planning to ensure tests cover actual user pain points, not just AC.

Purpose & Scope

• Research common problems for the feature domain using Web Search, MCP Ref, Context7.
• Analyze how competitors solve the same problem.
• Find customer complaints and pain points from forums, StackOverflow, Reddit.
• Post structured findings as a tracker comment (addComment) for later test-planning steps.
• No test creation or status changes.

When to Use

This skill should be used when:

• Use at the start of a test-planning workflow when feature-domain evidence is needed
• Story has non-trivial functionality (external APIs, file formats, authentication)
• Need to discover edge cases beyond AC

Skip research when:

• Story is trivial (simple CRUD, no external dependencies)
• Research comment already exists on Story
• User explicitly requests to skip

Workflow

Phase 1: Discovery

MANDATORY READ: Load references/input_resolution_pattern.md

1. Resolve storyId: Run Story Resolution Chain per guide (status filter: [To Review]).

2. Auto-discover Team ID from docs/tasks/kanban_board.md

Phase 2: Extract Feature Domain

1. Fetch Story via the configured tracker provider (getStory)
2. Parse Story goal and AC to identify:
   • What technology/API/format is involved?
   • What is the user's goal? (e.g., "translate XLIFF files", "authenticate via OAuth")
3. Extract keywords for research queries

Phase 3: Research Common Problems

Use available tools to find real-world problems:

1. Web Search:

   • "[feature] common problems"
   • "[format] edge cases"
   • "[API] gotchas"
   • "[technology] known issues"

2. MCP Ref:

   • ref_search_documentation("[feature] error handling best practices")
   • ref_search_documentation("[format] validation rules")

3. Context7:

   • Query relevant library docs for known issues
   • Check API documentation for limitations

Phase 4: Research Competitor Solutions

1. Web Search:

   • "[competitor] [feature] how it works"
   • "[feature] comparison"
   • "[product type] best practices"

2. Analysis:

   • How do market leaders handle this functionality?
   • What UX patterns do they use?
   • What error handling approaches are common?

Phase 5: Research Customer Complaints

1. Web Search:

   • "[feature] complaints"
   • "[product type] user problems"
   • "[format] issues reddit"
   • "[format] issues stackoverflow"

2. Analysis:

   • What do users actually struggle with?
   • What are common frustrations?
   • What gaps exist between user expectations and typical implementations?

Phase 6: Compile and Post Findings

1. Compile findings into categories:

   • Input validation issues (malformed data, encoding, size limits)
   • Edge cases (empty input, special characters, Unicode)
   • Error handling (timeouts, rate limits, partial failures)
   • Security concerns (injection, authentication bypass)
   • Competitor advantages (features we should match or exceed)
   • Customer pain points (problems users actually complain about)

2. Post tracker comment (addComment) on Story with research summary:

## Test Research: {Feature}

### Sources Consulted
- [Source 1](url)
- [Source 2](url)

### Common Problems Found
1. **Problem 1:** Description + test case suggestion
2. **Problem 2:** Description + test case suggestion

### Competitor Analysis
- **Competitor A:** How they handle this + what we can learn
- **Competitor B:** Their approach + gaps we can exploit

### Customer Pain Points
- **Complaint 1:** What users struggle with + test to prevent
- **Complaint 2:** Common frustration + how to verify we solve it

### Recommended Test Coverage
- [ ] Test case for problem 1
- [ ] Test case for competitor parity
- [ ] Test case for customer pain point

---
_This research informs both manual tests (ln-522) and automated tests (ln-523)._

Critical Rules

• No test creation: Only research and documentation.
• No status changes: Only tracker comment.
• Source attribution: Always include URLs for sources consulted.
• Actionable findings: Each problem should suggest a test case.
• Skip trivial Stories: Don't research "Add button to page".

Runtime Summary Artifact

MANDATORY READ: Load references/test_planning_summary_contract.md, references/test_planning_worker_runtime_contract.md

Runtime profile:

• family: test-planning-worker
• worker: ln-521
• summary kind: test-planning-worker
• payload fields used by coordinators: worker, status, warnings, research_comment_path

Invocation rules:

• standalone: omit runId and summaryArtifactPath
• managed: pass both runId and exact summaryArtifactPath
• always write the validated summary before terminal outcome

Definition of Done

• Feature domain extracted from Story (technology/API/format identified)
• Common problems researched (Web Search + MCP Ref + Context7)
• Competitor solutions analyzed (at least 1-2 competitors)
• Customer complaints found (forums, StackOverflow, Reddit)
• Findings compiled into categories
• Tracker comment posted with "## Test Research: {Feature}" header
• At least 3 recommended test cases suggested

Output: Tracker comment with research findings for ln-522 and ln-523 to use.

Reference Files

• Research methodology: Web Search, MCP Ref, Context7 tools
• Comment format: Structured markdown with sources
• Downstream consumers: ln-522-manual-tester, ln-523-auto-test-planner
• MANDATORY READ: Load references/research_tool_fallback.md

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Version: 1.0.0 Last Updated: 2026-01-15