What does this skill do, and when should you use it?
Wiki Researcher is for developers who need to understand a codebase, complex system, or cross-file pattern in depth. It first resolves the source-repository context, then conducts five research iterations covering architecture, data flow, integration, patterns, and synthesis. Its outputs emphasize actual code paths, file citations, Mermaid diagrams, and structured tables. It is designed for deep investigation rather than general code generation or quick answers.
It first checks the Git remote and determines the current branch, then asks whether the repository is local-only or has a source URL. Across five lenses, it traces entry points, call chains, data flow, dependencies, integrations, patterns, risks, and recommendations. It produces Mermaid architecture or flow diagrams, structured tables, file-and-line citations, confidence ratings, open questions, and explicit research boundaries.
- A backend engineer needs to explain how a feature operates across multiple files and wants the full call chain traced.
- An architect needs evidence-based component boundaries, dependency relationships, and external integration details.
- A new project engineer needs a structured understanding of a complex codebase with citations to the implementation.
- A technical writer needs source-grounded material for a codebase wiki, architecture page, or onboarding guide.
How do you install this skill?
- Execution depends on Git repository context and a user response; no concrete fallback is specified for missing Git, unreachable remotes, or branch-resolution failures.
- The mandatory five-iteration process and per-iteration Mermaid diagram and table may impose unnecessary time and context cost for simple questions.
- Handling boundaries for secrets, sensitive code, and private repositories are unspecified, as are Chinese-language output requirements and mainland-China network considerations.
- Shell / CLI
- Network access
- Local filesystem
Git
Install the collection with npx skills add microsoft/skills. Select the needed skill in the wizard and install it into the chosen agent directory, such as .github/skills/ for GitHub Copilot. The README does not document a standalone command for installing only wiki-researcher.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/microsoft/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/.github/plugins/deep-wiki/skills/wiki-researcher" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
After installation, request an in-depth investigation, for example: Analyze how X works across multiple files, trace the actual code path, and include file citations, Mermaid diagrams, and open questions. The source-repository context must be resolved before research begins.
What are this skill's strengths and limitations?
- Explicitly requires reading real implementations and tracing complete call chains.
- Five research iterations cover architecture, data flow, dependencies, patterns, and synthesis.
- Requires file citations, Mermaid diagrams, tables, confidence ratings, and open questions.
- Available under the MIT license as part of Microsoft's Agent Skills collection.
- Research cannot begin until the source-repository context is resolved.
- It requires Git, filesystem access, and potentially network access.
- The supplied SKILL.md provides no test suite, concrete output schema, or implemented failure-recovery mechanism.
- Research quality depends on the code and citation information that can be accessed.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Wiki Codebase Researcher this page ✓ Microsoft · Official | 52 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Codebase Onboarding Guide Generator ✓ Microsoft · Official | 48 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Repository Wiki Q&A ✓ Microsoft · Official | 52 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Wiki Architect ✓ Microsoft · Official | 52 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Systematic Debugging | 48 · Use with care | ★ 299k | 15d ago | MIT |
How did FollowSkills review this skill?
The skill requires repository-context resolution, distinguishes remote and local citations, and insists on tracing real code while separating fact from inference, reducing misattribution and scope drift. It does not address sensitive-data handling, least-privilege permissions, external effects, rollback, or dependency security, so points are deducted.
The workflow, iterative lenses, and research boundaries are clearly specified, and unknowns must be reported explicitly. However, it depends on Git and resolvable repository context without defining diagnostics or recovery for missing Git, unreachable remotes, invalid branches, or failed cross-file tracing; static calibration also caps this dimension at 10.
Activation scenarios are clear and fit cross-file architecture, data-flow, and pattern research, with an explicit focus on the requested topic. Non-fit cases, input/output contracts, semantic exclusions, Chinese-language support, and mainland-China network fit are not specified, so points are deducted.
The SKILL.md has readable activation rules, core invariants, evidence standards, and a five-stage information architecture, with MIT licensing, author metadata, and version 1.0. It lacks installation and dependency notes, output examples, FAQs, known limitations, changelog, and a clear maintenance/update path, so points are deducted.
It provides a directly usable framework for deep codebase research, including code paths, evidence, implications, confidence, and open questions. The mandatory five iterations and diagram/table requirements may impose disproportionate cost, and no representative final output is verifiable from the supplied skill; static calibration therefore limits this score to 7 or below.
The skill specifies file paths, function names, call chains, citations, and fact/inference separation, creating a useful audit model. It contains no key-path tests or third-party execution evidence, and this review is static only, so the score remains within the 5-point static limit.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →