chatgpt-apps

Scaffold ChatGPT Apps SDK implementations with a docs-first, example-first workflow, then generate code that follows current Apps SDK and MCP Apps bridge patterns. Use this skill to produce: • A primary app-archetype classification and repo-shape decision • A tool plan (names, schemas, annotations, outputs) • An upstream starting-point recommendation (official example, ext-apps example, or local fallback scaffold) • An MCP server scaffold (resource registration, tool handlers, metadata) • A widget scaffold (MCP Apps bridge first, window.openai compatibility/extensions second) • A reusable Node + @modelcontextprotocol/ext-apps starter scaffold for low-dependency fallbacks • A validation report against the minimum working repo contract • Local dev and connector setup steps • A short stakeholder summary of what the app does (when requested)

Define the tool surface area from user intents. • Use one job per tool. • Write tool descriptions that start with "Use this when..." behavior cues. • Make inputs explicit and machine-friendly (enums, required fields, bounds). • Decide whether each tool is data-only, render-only, or both. • Set annotations accurately (readOnlyHint, destructiveHint, openWorldHint; add idempotentHint when true). • If the app is connector-like, data-only, sync-oriented, or intended for company knowledge or deep research, default to the standard search and fetch tools instead of inventing custom read-only equivalents. • For educational/demo apps, prefer one concept per tool so the model can pick the right example cleanly. • Group demo tools by learning objective: data into the widget, widget actions back into the conversation or tools, host/layout environment signals, and lifecycle/streaming behavior. Read references/search-fetch-standard.md when search and fetch may be relevant.

Use $openai-docs first whenever building or changing a ChatGPT Apps SDK app. • Invoke $openai-docs (preferred) or call the OpenAI docs MCP server directly. • Fetch current Apps SDK docs before writing code, especially (baseline pages): • apps-sdk/build/mcp-server • apps-sdk/build/chatgpt-ui • apps-sdk/build/examples • apps-sdk/plan/tools • apps-sdk/reference • Fetch apps-sdk/quickstart when scaffolding a new app or generating a first-pass implementation, and check the official examples repo/page before inventing a scaffold from scratch. • Fetch deployment/submission docs when the task includes local ChatGPT testing, hosting, or public launch: • apps-sdk/deploy • apps-sdk/deploy/submission • apps-sdk/app-submission-guidelines • Cite the docs URLs you used when explaining design choices or generated scaffolds. • Prefer current docs guidance over older repo patterns when they differ, and call out compatibility aliases explicitly. • If doc search times out or returns poor matches, fetch the canonical Apps SDK pages directly by URL and continue; do not let search failure block scaffolding. If $openai-docs is unavailable, use: • mcp__openaiDeveloperDocs__search_openai_docs • mcp__openaiDeveloperDocs__fetch_openai_doc Read references/apps-sdk-docs-workflow.md for suggested doc queries and a compact checklist. Read references/app-archetypes.md to classify the request into a small number of supported app shapes before choosing examples or scaffolds. Read references/repo-contract-and-validation.md when generating or reviewing a repo so the output stays inside a stable “working app” contract. Read references/search-fetch-standard.md when the app is connector-like, data-only, sync-oriented, or meant to work well with company knowledge or deep research. Read references/upstream-example-workflow.md when starting a greenfield app or when deciding whether to adapt an upstream example or use the local fallback scaffold. Read references/window-openai-patterns.md when the task needs ChatGPT-specific widget behavior or when translating repo examples that use wrapper-specific app.* helpers.

Use prompts that explicitly pair this skill with $openai-docs so the resulting scaffold is grounded in current docs. Preferred prompt patterns: • Use $chatgpt-apps with $openai-docs to scaffold a ChatGPT app for <use case> with a <TS/Python> MCP server and <React/vanilla> widget. • Use $chatgpt-apps with $openai-docs to adapt the closest official Apps SDK example into a ChatGPT app for <use case>. • Use $chatgpt-apps and $openai-docs to refactor this Apps SDK demo into a production-ready structure with tool annotations, CSP, and URI versioning. • Use $chatgpt-apps with $openai-docs to plan tools first, then generate the MCP server and widget code. When responding, ask for or infer these inputs before coding: • Use case and primary user flows • Read-only vs mutating tools • Demo vs production target • Private/internal use vs public directory submission • Backend language and UI stack • Auth requirements • External API domains for CSP allowlists • Hosting target and local dev approach • Org ownership/verification readiness (for submission tasks)