MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

Wiki Researcher

Wiki Researcher is an learning AI skill with a core value of Conducts multi-turn iterative deep research on specific topics within a codebase with zero tolerance for shallow analysis. It helps developers solve real-world problems in the learning domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

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...

Last verified on: 2026-07-08

Quick Facts

Category learning
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-08
Risk Level Low
mkdir -p ./skills/wiki-researcher && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/wiki-researcher/SKILL.md -o ./skills/wiki-researcher/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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


When to Use

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

🎯 Best For

  • Claude users
  • Students
  • Lifelong learners
  • Educators

💡 Use Cases

  • Using Wiki Researcher in daily workflow
  • Automating repetitive learning tasks

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Wiki Researcher to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

How do I install Wiki Researcher?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/wiki-researcher/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Not reading the full skill

Skills contain important context and edge cases beyond the quick start.

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