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...

16 stars

Best use case

wiki-researcher is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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...

Teams using wiki-researcher should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/wiki-researcher/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/development/wiki-researcher/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/wiki-researcher/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How wiki-researcher Compares

Feature / Agentwiki-researcherStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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...

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.

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

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

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