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...
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
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...
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.
Related Skills
code-surgeon-context-researcher
Use when analyzing a codebase to select relevant files, build dependency maps, and extract architectural patterns for informed implementation planning
api-researcher
Expert API research including discovery, evaluation, integration analysis, and documentation review
Academic Researcher
Academic paper search across 14+ scholarly platforms including arXiv, PubMed, Google Scholar, Web of Science, Semantic Scholar, Sci-Hub, and more. Use for literature review, research discovery, and citation management.
agent-ux-researcher
Expert UX researcher specializing in user insights, usability testing, and data-driven design decisions. Masters qualitative and quantitative research methods to uncover user needs, validate designs, and drive product improvements through actionable insights.
wikidata-search
Search for items and properties on Wikidata and retrieve entity details, claims, and external identifiers. Supports both keyword search (Wikidata Action API) and semantic/hybrid search (Wikidata Vector Database), plus direct entity retrieval (Special:EntityData) and structured querying (WDQS SPARQL).
stardew-wiki-advisor
Query Stardew Valley Wiki using natural language. Ask about crops, NPCs, strategies, and more.
gpt-researcher
Run GPT-Researcher multi-agent deep research framework locally using OpenAI GPT-5.2. Replaces ChatGPT Deep Research with local control. Researches 100+ sources in parallel, provides comprehensive citations. Use for Phase 3 industry/technical research or comprehensive synthesis. Takes 6-20 min depending on report type. Supports multiple LLM providers.
agent-market-researcher
Expert market researcher specializing in market analysis, consumer insights, and competitive intelligence. Masters market sizing, segmentation, and trend analysis with focus on identifying opportunities and informing strategic business decisions.
agent-data-researcher
Expert data researcher specializing in discovering, collecting, and analyzing diverse data sources. Masters data mining, statistical analysis, and pattern recognition with focus on extracting meaningful insights from complex datasets to support evidence-based decisions.
agency-researcher
Find and qualify real estate agencies in a given suburb
academic-benchmark-researcher
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
bgo
Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.