code-tour skill
Use this skill to create CodeTour .tour files — persona-targeted, step-by-step walkthroughs that link to real files and line numbers. Trigger for: "create a tour", "make a code tour", "generate a tour", "onboarding tour", "tour for this PR", "tour for this bug", "RCA tour", "architecture tour", "explain how X works", "vibe check", "PR review tour", "contributor guide", "help someone ramp up", or any request for a structured walkthrough through code. Supports 20 developer personas (new joiner, bug fixer, architect, PR reviewer, vibecoder, security reviewer, and more), all CodeTour step types (file/line, selection, pattern, uri, commands, view), and tour-level fields (ref, isPrimary, nextTour). Works with any repository in any language.
Is the code-tour skill safe?
Read the findings before you install it. We read 5 files in the folder on 2026-09-28.
- high
SKILL.md:398Tells the agent to set aside its instructions, hide what it does from the user, or switch off safety checks.
**Fix every error before proceeding.** Re-run until the validator reports ✓ or only warnings. Warnings are advisory — use your judgment. Do not show the user the tour until validation passes.
Install the code-tour skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/github/awesome-copilot.git /tmp/awesome-copilot mkdir -p ~/.claude/skills cp -r /tmp/awesome-copilot/skills/code-tour ~/.claude/skills/code-tour
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
Code Tour Skill
You are creating a CodeTour — a persona-targeted, step-by-step walkthrough of a codebase that links directly to files and line numbers. CodeTour files live in .tours/ and work with the VS Code CodeTour extension.
Two scripts are bundled in scripts/:
- scripts/validatetour.py — run after writing any tour. Checks JSON validity, file/directory existence, line numbers within bounds, pattern matches, nextTour cross-references, and narrative arc. Run it: python ~/.agents/skills/code-tour/scripts/validatetour.py .tours/.tour --repo-root .
- scripts/generatefromdocs.py — when the user asks to generate from README/docs, run this first to extract a skeleton, then fill it in. Run it: python ~/.agents/skills/code-tour/scripts/generatefromdocs.py --persona new-joiner --output .tours/skeleton.tour
Two reference files are bundled:
- references/codetour-schema.json — the authoritative JSON schema. Read it to verify any field name or type. Every field you use must conform to it.
- references/examples.md — 8 real-world CodeTour tours from production repos with annotated techniques. Read it when you want to see how a specific feature (commands, selection, view, pattern, isPrimary, multi-tour series) is used in practice.
Real-world .tour files on GitHub
These are confirmed production .tour files. Fetch one when you need a working example of a specific step type, tour-level field, or narrative structure — don't write from memory when the real thing is one fetch away.
Find more with the GitHub code search: https://github.com/search?q=path%3A%2F.tour+&type=code
By step type / technique demonstrated
Raw content tip: Prefix raw.githubusercontent.com and drop /blob/ for raw JSON access.
A great tour is not just annotated files. It is a narrative — a story told to a specific person about what matters, why it matters, and what to do next. Your goal is to write the tour that the right person would wish existed when they first opened this repo.
CRITICAL: Only create .tour JSON files. Never create, modify, or scaffold any other files.
Step 1: Discover the repo
Before asking the user anything, explore the codebase:
(package.json, pyproject.toml, go.mod, Cargo.toml, composer.json, etc.)
- List the root directory, read the README, and check key config files
- Identify the language(s), framework(s), and what the project does
- Map the folder structure 1–2 levels deep
- Find entry points: main files, index files, app bootstrapping
- Note which files actually exist — every path you write in the tour must be real
If the repo is sparse or empty, say so and work with what exists.
If the user says "generate from README" or "use the docs": run the skeleton generator first, then fill in every [TODO: ...] by reading the actual files:
python skills/code-tour/scripts/generate_from_docs.py \
--persona new-joiner \
--output .tours/skeleton.tourEntry points by language/framework
Don't read everything — start here, then follow imports.
Repo type variants — adjust focus accordingly
The same persona asks for different things depending on what kind of repo this is:
For monorepos: identify the 2–3 packages most relevant to the persona's goal. Don't try to tour everything — open the tour with a step that explains how to navigate the workspace, then stay focused.
Large repo strategy
For repos with 100+ files: don't try to read everything.
- Read entry points and the README first
- Build a mental model of the top 5–7 modules
- For the requested persona, identify the 2–3 modules that matter most and read those deeply
- For modules you're not covering, mention them in the intro step as "out of scope for this tour"
- Use directory steps for areas you mapped but didn't read — they orient without requiring full knowledge
A focused 10-step tour of the right files beats a scattered 25-step tour of everything.
Step 2: Read the intent — infer everything you can, ask only what you can't
One message from the user should be enough. Read their request and infer persona, depth, and focus before asking anything.
Intent map
Infer silently: persona, depth, focus area, whether to add uri/ref, isPrimary.
Ask only if you genuinely can't infer:
- "bug tour" but no bug described → ask for the bug description
- "feature tour" but no feature named → ask which feature
- "specific files" explicitly requested → honor them as required stops
Never ask about nextTour, commands, when, or stepMarker unless the user mentioned them.
PR tour recipe
For PR tours: set "ref" to the branch, open with a uri step for the PR, cover changed files first, then unchanged-but-critical files, close with a reviewer checklist.
User-provided customization — always honor these
Step 3: Read the actual files — no exceptions
Every file path and line number in the tour must be verified by reading the file. A tour pointing to the wrong file or a non-existent line is worse than no tour.
For every planned step:
- Read the file
- Find the exact line of the code you want to highlight
- Understand it well enough to explain it to the target persona
If a user-requested file doesn't exist, say so — don't silently substitute another.
Step 4: Write the tour
Save to .tours/-.tour. Read references/codetour-schema.json for the authoritative field list. Every field you use must appear in that schema.
Tour root
{
"$schema": "https://aka.ms/codetour-schema",
"title": "Descriptive Title — Persona / Goal",
"description": "One sentence: who this is for and what they'll understand after.",
"ref": "main",
"isPrimary": false,
"nextTour": "Title of follow-up tour",
"steps": []
}Omit any field that doesn't apply to this tour.
when — conditional display. A JavaScript expression evaluated at runtime. Only show this tour if the condition is true. Useful for persona-specific auto-launching, or hiding advanced tours until a simpler one is complete.
{ "when": "workspaceFolders[0].name === 'api'" }stepMarker — embed step anchors directly in source code comments. When set, CodeTour looks for // comments in files and uses them as step positions instead of (or alongside) line numbers. Useful for tours on actively changing code where line numbers shift constantly. Example: set "stepMarker": "CT" and put // CT in the source file. Don't suggest this unless the user asks — it requires editing source files, which is unusual.
Step types — full reference
All step types: content (intro/closing, max 2), directory, file+line (workhorse), selection (code block), pattern (regex match), uri (external link), view (focus VS Code panel), commands (run VS Code commands).
Path rule: "file" and "directory" must be relative to repo root. No absolute paths, no leading ./.
When to use each step type
Step count calibration
Match steps to depth and persona. These are targets, not hard limits.
Scale with repo size too. A 3-file CLI doesn't get 15 steps. A 200-file monolith shouldn't be squeezed into 5.
More skills from github/awesome-copilot
- Aacquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
- Aacreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
- Aacreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
- Aacreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
- Aad-campaign-analyzerUse this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
- Aadd-educational-commentsAdd educational comments to the file specified, or prompt asking for file to comment if one is not provided.
- Aadobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
- Aagent-architectureDesign AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
- Aagent-governancePatterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
- Aagent-owasp-complianceCheck any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
- Aagent-skill-stackFind, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills workflow.
- Aagent-supply-chainVerify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"