Wiki Changelog
Analyzes git commit history and generates structured changelogs categorized by change type. Use when the user asks about recent changes, wants a changelog, or needs to understand what changed in the repository.
MCP get_skill({ skillId: "wiki-changelog-2c2c28c3" })Use this skill with your agent
Create a free account and connect via MCP
# Wiki Changelog Generate structured changelogs from git history. ## Source Repository Resolution (MUST DO FIRST) Before generating any changelog, you MUST determine the source repository context: 1. **Check for git remote**: Run `git remote get-url origin` to detect if a remote exists 2. **Ask the user**: _"Is this a local-only repository, or do you have a source repository URL (e.g., GitHub, Azure DevOps)?"_ - Remote URL provided → store as `REPO_URL`, use **linked citations** for commit hashes and file references - Local-only → use plain commit hashes and file references 3. **Do NOT proceed** until source repo context is resolved ## When to Activate - User asks "what changed recently", "generate a changelog", "summarize commits" - User wants to understand recent development activity ## Procedure 1. Examine git log (commits, dates, authors, messages) 2. Group by time period: daily (last 7 days), weekly (older) 3. Classify each commit: Features (🆕), Fixes (🐛), Refactoring (🔄), Docs (📝), Config (🔧), Dependencies (📦), Breaking (⚠️) 4. Generate concise user-facing descriptions using project terminology ## Constraints - Focus on user-facing changes - Merge related commits into coherent descriptions - Use project terminology from README - Highlight breaking changes prominently with migration notes - When `REPO_URL` is available, link commit hashes: `[abc1234](REPO_URL/commit/abc1234)` and changed files: `[file_path](REPO_URL/blob/BRANCH/file_path)`
Related Skills
More skills in Software Engineering
Accessibility Standards
Comprehensive web accessibility standards based on WCAG 2.2 AA, with 38+ anti-patterns, legal enforcement context (EAA, ADA Title II), WAI-ARIA patterns, and framework-specific fixes for modern web frameworks and libraries.
Accord
Authoring unified specification packages across Business/Development/Design teams via staged elaboration (L0 Vision → L1 Requirements → L2 Team Detail → L3 Acceptance Criteria). No code. Use when authoring cross-team specs, building L0-L3 packages, or aligning Biz/Dev/Design on a single source of truth.
Acquire Codebase Knowledge
Use 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.
Acreadiness Assess
Run 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.
Acreadiness Generate Instructions
Generate 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.
Acreadiness Policy
Help 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.
Explore Other Categories
Skills from other categories with shared topics
Airunway Aks Setup
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
Azure AI Services
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
Azure Aigateway
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.