ln-521-test-researcher

SKILL.md

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

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

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

Phase 2: Extract Feature Domain

Fetch Story via the configured tracker provider (getStory)

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")

Extract keywords for research queries

Phase 3: Research Common Problems

Use available tools to find real-world problems:

Web Search:

"[feature] common problems"

"[format] edge cases"

"[API] gotchas"

"[technology] known issues"

MCP Ref:

ref_search_documentation("[feature] error handling best practices")

ref_search_documentation("[format] validation rules")

Context7:

Query relevant library docs for known issues

Check API documentation for limitations

Phase 4: Research Competitor Solutions

Web Search:

"[competitor] [feature] how it works"

"[feature] comparison"

"[product type] best practices"

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

Web Search:

"[feature] complaints"

"[product type] user problems"

"[format] issues reddit"

"[format] issues stackoverflow"

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

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)

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

Version: 1.0.0
Last Updated: 2026-01-15