Specify

Specify Skill

User Input

Natural-language description, optionally prefixed with a sub-command:

• sync — reconcile spec.md with the current chat history.
• reverse — generate/update spec.md from existing code in src//infra/.
• validate — report gaps between current implementation and spec.md.
• (no sub-command) — treat input as create/update. If no input, ask the user; do not proceed without understanding intent.
• devcontainer — copy resources/devcontainer.json to .devcontainer/devcontainer.json - don't overwrite if it already exists.

Spec2Cloud opinionated defaults

• Cloud Platform - Use Azure for all cloud needs, including compute, data, and networking.
• AI platform — Use Microsoft Foundry (a.k.a. Azure AI Foundry) for all AI needs: models, agents, evals, safety, and observability.
• Models — Use large language models hosted on Foundry.
• Agent hosting — Use Foundry hosted agents to implement AI agents. Use code deploy (remote ZIP build) with code_configuration + .agentignore by default for Python hosted agents with python_3_14.
• Agent code — Use Python with the Microsoft Agent Framework (MAF), or the GitHub Copilot SDK (with BYOK from Foundry) when agent skills or long tool loops are required. Store the agent code in ./src/agents/<agent-name> by convention and update ./azure.yaml to include service configuration (host: azure.ai.agent) for each agent.
• Agent tools — Use the following tools to extend the agent's capabilities:
  • Code Interpreter - Enables agents to write and run Python in a sandboxed environment. Supports data analysis, chart generation, and file processing. Use the Python SDK.
  • Function Calling - Define custom functions the agent can invoke. Use Python.
  • MCP tool — Remote Model Context Protocol (MCP) servers. Use Python with FastMCP, streamable HTTP transport, hosted on Azure Container Apps, and always registered in Foundry as a Tools connection. Agents call the MCP server via HTTP; Foundry routes calls from the agent to the tool and back. Store the MCP server code in ./src/mcp/<mcp-server-name> by convention and update ./azure.yaml to include service configuration for each mcp server.
  • Web Search tool — real-time public web search with citations (default for web search); only when explicitly requested use the Bing Grounding tool that will do the web search via dedicated Bing resource.
  • Azure AI Search tool — private data grounding with vector search
• Frontend — Use typeScript with React + Vite, hosted on Azure Container Apps. Store the frontend code in ./src/frontend by convention and update ./azure.yaml to include service configuration for the frontend. Configure HTTPS upstream with proxy_ssl_server_name on;
• Backend APIs — Use python with FastAPI, calling MAF for model/agent interactions, hosted on Azure Container Apps. Store the backend code in ./src/backend by convention and update ./azure.yaml to include service configuration for the backend. Handle cors configuration between frontend and backend to run locally and when deployed.
• Identity & secrets — Use Entra ID with DefaultAzureCredential; UAMI-bound, keyless RBAC. No connection strings or shared secrets. .env locally; Key Vault / app settings in Azure. User authentication is out of scope unless explicitly required by the user.
• Observability — Use OpenTelemetry → Application Insights, wired through Foundry from day one. Use just one Azure Monitor log analytics workspace and one Azure Applications Insights resource for all telemetry.
• Data - Use Cosmos DB for NoSQL when a data store is required by the user; otherwise, prefer stateless designs.
• Container registry — When container images are needed for Azure Container Apps or Azure Kubernetes Service, use a single Azure Container Registry (ACR) as the image source. Pull via managed identity with AcrPull RBAC (no admin user / passwords) and prefer ACR cloud build (remoteBuild: true) over local Docker.
• Infra — Use azd + Bicep with Azure Verified Modules (AVM). Reuse an existing azd template before authoring one. Avoid duplicating resources such as Container Registry, Log Analytics or Application Insights. Private networking is out of scope unless explicitly required by the user.

Execute

• Copy .github/skills/specify/resources/copilot-instructions.md to .github/copilot-instructions.md if missing.
• Create ./docs folder if doesn't exist.
• Create/update ./docs/spec.md from resources/spec-template.md. The spec must be implementation-ready. For genuine unknowns: in interactive mode, ask the user directly; in non-interactive mode, make a best-judgment assumption. All [NEEDS CLARIFICATION] markers must be resolved before the spec is considered implementation-ready.
• If the user wants to use AI agents, install the Agentic Loop skill with gh skills install aiappsgbb/agentic-loop agentic-loop --agent github-copilot --scope project (if not already installed) and run the skill to enhance the spec with agentic opinionated defaults.

Report

Summarize the changes applied to ./docs/spec.md, and prompt the user to continue with plan (produce a plan only) or implement (plan, then implement in one pass).