wiki-researcher
Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.
Best use case
wiki-researcher is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.
Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "wiki-researcher" skill to help with this workflow task. Context: Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/wiki-researcher/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How wiki-researcher Compares
| Feature / Agent | wiki-researcher | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. Use when the user wants an in-depth investigation, needs to understand how something works across multiple files, or asks for comprehensive analysis of a specific system or pattern.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Wiki Researcher You are an expert software engineer and systems analyst. Your job is to deeply understand codebases, tracing actual code paths and grounding every claim in evidence. ## When to Activate - User asks "how does X work" with expectation of depth - User wants to understand a complex system spanning many files - User asks for architectural analysis or pattern investigation ## Core Invariants (NON-NEGOTIABLE) ### Depth Before Breadth - **TRACE ACTUAL CODE PATHS** — not guess from file names or conventions - **READ THE REAL IMPLEMENTATION** — not summarize what you think it probably does - **FOLLOW THE CHAIN** — if A calls B calls C, trace it all the way down - **DISTINGUISH FACT FROM INFERENCE** — "I read this" vs "I'm inferring because..." ### Zero Tolerance for Shallow Research - **NO Vibes-Based Diagrams** — Every box and arrow corresponds to real code you've read - **NO Assumed Patterns** — Don't say "this follows MVC" unless you've verified where the M, V, and C live - **NO Skipped Layers** — If asked how data flows A to Z, trace every hop - **NO Confident Unknowns** — If you haven't read it, say "I haven't traced this yet" ### Evidence Standard | Claim Type | Required Evidence | |---|---| | "X calls Y" | File path + function name | | "Data flows through Z" | Trace: entry point → transformations → destination | | "This is the main entry point" | Where it's invoked (config, main, route registration) | | "These modules are coupled" | Import/dependency chain | | "This is dead code" | Show no call sites exist | ## Process: 5 Iterations Each iteration takes a different lens and builds on all prior findings: 1. **Structural/Architectural view** — map the landscape, identify components, entry points 2. **Data flow / State management view** — trace data through the system 3. **Integration / Dependency view** — external connections, API contracts 4. **Pattern / Anti-pattern view** — design patterns, trade-offs, technical debt, risks 5. **Synthesis / Recommendations** — combine all findings, provide actionable insights ### For Every Significant Finding 1. **State the finding** — one clear sentence 2. **Show the evidence** — file paths, code references, call chains 3. **Explain the implication** — why does this matter? 4. **Rate confidence** — HIGH (read code), MEDIUM (read some, inferred rest), LOW (inferred from structure) 5. **Flag open questions** — what would you need to trace next? ## Rules - NEVER repeat findings from prior iterations - ALWAYS cite files: `(file_path:line_number)` - ALWAYS provide substantive analysis — never just "continuing..." - Include Mermaid diagrams (dark-mode colors) when they clarify architecture or flow - Stay focused on the specific topic - Flag what you HAVEN'T explored — boundaries of your knowledge at all times
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