wiki-researcher
Has your agent analyze complex systems and architectural patterns. It investigates code to explain how things work in depth.
What it does
This AI agent is a code detective. It investigates how a computer program works internally by tracing the exact code paths and explaining each step.
When to use it
- ✓When you want to understand a complex program with many files.
- ✓When you ask 'how does this work?' and expect a detailed answer.
- ✓When you need to analyze a system's structure or patterns.
Example of what to ask your AI
“Explain how the login system in this project works, showing the data flow and calls between files.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It reads the actual code, follows function calls step-by-step, and documents each finding with clear evidence like file paths and line numbers.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill wiki-researcherOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/wiki-researcher/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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 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:
- Structural/Architectural view — map the landscape, identify components, entry points
- Data flow / State management view — trace data through the system
- Integration / Dependency view — external connections, API contracts
- Pattern / Anti-pattern view — design patterns, trade-offs, technical debt, risks
- Synthesis / Recommendations — combine all findings, provide actionable insights
For Every Significant Finding
- State the finding — one clear sentence
- Show the evidence — file paths, code references, call chains
- Explain the implication — why does this matter?
- Rate confidence — HIGH (read code), MEDIUM (read some, inferred rest), LOW (inferred from structure)
- 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.
FAQ
What are 'actual code paths'?
It means the AI follows exactly where the code goes, rather than guessing based on file names.
Why does the AI cite files and lines?
To prove what it found and allow you to verify the explanation.
What if the AI doesn't know something?
It will openly state it hasn't traced that part yet, instead of making up an answer.
Details
Original description by the author
You are an expert software engineer and systems analyst. Use when user asks \"how does X work\" with expectation of depth, user wants to understand a complex system spanning many files, or user asks for architectural analysis or pattern investigation.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4568
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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