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wiki-researcher

wiki-researcher

github.com/sickn33Development48.7k101 installsUpdated today
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About wiki-researcher

A research skill for software engineers and systems analysts investigating how codebases work. It emphasizes tracing real execution and data paths, supporting claims with file references, and separating verified facts from inference.

Key features

  1. 01Trace implementation and execution paths
  2. 02Map architecture and entry points
  3. 03Follow data and state flow
  4. 04Analyze dependencies and integrations
  5. 05Support findings with file references
  6. 06Mark confidence and open questions

Use cases

  1. 01Explain a feature across multiple files
  2. 02Investigate system architecture
  3. 03Trace data from entry point to destination
  4. 04Assess dependencies and design trade-offs

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill wiki-researcher

After installing, type/wiki-researcherin Claude Code to confirm it loaded.

Details

From the project README

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 Use

  • 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 TypeRequired 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 may be unused code"Search static call sites and dynamic registration/configuration; state the search scope and unresolved runtime loading

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
When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Example

User request:

Explain how this subsystem works across files, including control flow, data flow, boundaries, and evidence.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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