ln-511-test-researcher

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

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Install skill "ln-511-test-researcher" with this command: npx skills add levnikolaevich/claude-code-skills/levnikolaevich-claude-code-skills-ln-511-test-researcher

Test Researcher

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 Linear comment for downstream skills (ln-512, ln-513).

  • No test creation or status changes.

When to Use

This skill should be used when:

  • Invoked by ln-510-test-planner at start of test planning pipeline

  • 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

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

Input: Story ID from orchestrator (ln-510)

Phase 2: Extract Feature Domain

  • Fetch Story from Linear

  • 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 Linear comment on Story with research summary:

Test Research: {Feature}

Sources Consulted

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-512) and automated tests (ln-513).

Critical Rules

  • No test creation: Only research and documentation.

  • No status changes: Only Linear 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".

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

  • Linear comment posted with "## Test Research: {Feature}" header

  • At least 3 recommended test cases suggested

Output: Linear comment with research findings for ln-512 and ln-513 to use.

Reference Files

  • Research methodology: Web Search, MCP Ref, Context7 tools

  • Comment format: Structured markdown with sources

  • Downstream consumers: ln-512-manual-tester, ln-513-auto-test-planner

Version: 1.0.0 (Initial release - extracted from ln-503-manual-tester Phase 0) Last Updated: 2026-01-15

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