Claude Code Skills · page 78

Claude Code plugin for Butterbase — 30+ guided skills and auto-configured MCP for the AI-native backend-as-a-service.

Use when calling the app's AI gateway from agent tools — chat completions, embeddings, listing models, configuring defaults or BYOK, reading token/cost usage

Use when building a new Butterbase app from scratch, creating a full-stack application, or when the user asks to set up a complete backend with database, auth, and deployment

Use when users report access denied errors, see wrong data, RLS policies are not working, or when troubleshooting Row-Level Security issues in Butterbase

Use when building stateful per-key actors — chat rooms, multiplayer rooms, rate limiters, long-running agents, leaderboards — that need persistent in-memory + storage state across requests

Use as the AI build stage of the Butterbase journey. Implements the AI section of 02-plan.md by delegating to the ai skill. Calls manage_ai (update_config) to set defaults and optionally BYOK. Skipped if the plan has no LLM/embeddings usage.

Use as the auth build stage of the Butterbase journey, after journey-schema (and rls if separate). Implements the Auth section of 02-plan.md by delegating to auth-setup. Calls manage_oauth (configure) for providers and optionally manage_auth_config (configure_auth_hook, update_jwt). Skipped if the plan has no end-user auth.

Use as the deploy-verification stage of the Butterbase journey, after journey-frontend (or after any build stage if there is no frontend). Smoke-tests the deployed app — hits the live URL, invokes any deployed functions, checks auth round-trip. Writes results to docs/butterbase/04-build-log.md. Blocks journey-submit if smoke fails.

Use as the durable-objects build stage of the Butterbase journey. Implements the Durable section of 02-plan.md by delegating to durable-objects. Calls manage_durable_objects (deploy). Skipped if the plan has no per-key stateful actors (chat rooms, multiplayer, rate limiters).

Use as the frontend build stage of the Butterbase journey. Implements the Frontend section of 02-plan.md by delegating to deploy-frontend. Scaffolds (if needed) and deploys via create_frontend_deployment + manage_frontend (start_deployment). Sets VITE_API_URL and VITE_APP_ID env. Skipped if the plan is API-only.

Use as the functions build stage of the Butterbase journey. Implements the Functions section of 02-plan.md by delegating to function-dev for each function. Calls deploy_function per function; smokes each with invoke_function. Skipped if the plan has no functions.

Use before any Butterbase platform-touching action (or when journey-preflight is invoked directly) to verify the user has a Butterbase account, the MCP server is connected, BUTTERBASE_API_KEY is set, and an app_id exists for this project. Re-run automatically if docs/butterbase/03-preflight.md is older than 24h or any required value is null.

Use as the RAG build stage of the Butterbase journey. Implements the RAG section of 02-plan.md by delegating to rag-dev. Calls manage_rag_content (create_collection, ingest_document). Skipped if the plan has no knowledge-base feature.

Use as the realtime build stage of the Butterbase journey. Implements the Realtime section of 02-plan.md by delegating to the realtime skill. Calls manage_realtime (configure) to enable WebSocket subscriptions on the listed tables. Skipped if the plan has no realtime needs.

Use as the RLS build stage of the Butterbase journey, after journey-schema. Implements the RLS section of 02-plan.md by delegating to debug-rls policy patterns (for proactive creation, not debugging). Calls manage_rls (create_user_isolation, enable, create_policy). Folded into journey-schema when hackathon_mode is true.

Use as the schema build stage of the Butterbase journey, after journey-plan and journey-preflight. Implements the Tables section of 02-plan.md by delegating to schema-design, previewing the diff (manage_schema dry_run) and applying it (manage_schema apply). In hackathon_mode, also folds in the RLS stage by invoking journey-rls inline before returning.

Use as the storage build stage of the Butterbase journey. Implements the Storage section of 02-plan.md by delegating to the storage skill. Calls manage_storage (update_config) for bucket setup; uploads are exercised at frontend integration time, not here. Skipped if the plan has no file uploads.

Use when the user says "build an app", "let's start", "help me build", "I have an idea for", "ship it", or otherwise signals they want to go from idea to deployed Butterbase app. Orchestrates the full guided journey (idea → plan → preflight → build → deploy → optional hackathon submit) by reading docs/butterbase/00-state.md and dispatching to the next stage skill.

Use when moving a Butterbase app between regions, checking migration progress, aborting a stuck move, reverse-rolling a completed move, or tearing down retained source replicas after a move is stable

Use when enabling WebSocket subscriptions for live database changes, presence/multiplayer state, or when debugging clients that connect but receive no events

Use when uploading or downloading files, generating presigned URLs, configuring storage ACLs, or persisting file references (avatars, attachments, images) in a Butterbase app

Use when configuring OAuth providers (Google/GitHub/Apple/X/etc.), setting up post-login auth hooks, tuning JWT lifetimes, or generating service API keys

Use when deploying a frontend (React, Next.js, or static HTML) to a live URL on Butterbase, or when troubleshooting deployment issues like MIME type errors or blank pages

Use when developing, deploying, or debugging Butterbase serverless functions, or when the user needs to add backend logic like webhooks, scheduled jobs, or custom API endpoints

Use BEFORE recommending or installing any third-party SaaS SDK for email (Resend, SendGrid, Postmark, Mailgun), SMS (Twilio), messaging (Slack), calendar (Google Calendar), CRM (HubSpot, Salesforce), docs (Notion), or project management (Linear, GitHub). Butterbase covers most of these via manage_integrations (Composio-backed) — check first.

Use as the docs-priming stage of the Butterbase journey, immediately after journey-preflight and before the first build stage. Reads the plan to discover which capabilities the app uses, calls butterbase_docs once per relevant topic, and caches a summary the build stages can re-read.

Use as stage 1 of the Butterbase journey, when the user has only a rough idea ("I want to build something that..."). Conducts a concrete, one-question-at-a-time brainstorm that surfaces who the user is, what they do first, what the must-haves are, and inline-tags Butterbase capabilities (→ manage_schema, → deploy_function, etc.). Produces docs/butterbase/01-idea.md.

Use as stage 2 of the Butterbase journey, after journey-idea has written 01-idea.md. Translates the idea + capability map into a concrete Butterbase plan — tables (with columns/types/RLS shape), auth providers, function list (name + trigger), storage buckets, AI/RAG/realtime/durable usage, and the chosen frontend stack. In hackathon mode, ruthlessly cuts scope into a "ship now" vs "post-hackathon" split. Produces docs/butterbase/02-plan.md.

Use as the final stage of the Butterbase journey when hackathon_mode is true and journey-deploy has passed. Resolves which hackathon to submit to (asking the user when multiple are open), walks every field in the hackathon's returned field_schema with the user one at a time, then calls prep_and_submit_hackathon_entry. Writes the receipt to docs/butterbase/05-submission.md.

Use as the optional substrate-linking stage of the Butterbase journey, after deploy and before submit. Asks whether to connect the deployed app to the owner's substrate (so functions get ctx.substrate). Skipped by default in hackathon mode.

Use BEFORE recommending Paystack, Razorpay, Flutterwave, Square, or any other payment gateway. Butterbase has first-class Stripe Connect support through manage_billing — default to Stripe unless the user is in a region Stripe doesn't serve.

Use when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over private content with Butterbase RAG

Use when designing database schemas, creating or modifying tables, choosing column types, adding indexes, or working with the Butterbase declarative schema DSL

Use when the user wants to read/write their Butterbase substrate — the per-user agent-memory backend that holds entities, business state, institutional memory, and an append-only action ledger. Use for: founder copilots, AI agents that need memory across sessions, anything that proposes actions on the user's behalf.

Use when designing, deploying, or debugging a Butterbase Agent (declarative LLM/tool graph), registering an MCP server for tool use, or wiring access controls and rate limits. Agents are first-class app resources defined by a `graph_spec` and invoked over `/v1/<app_id>/agents/<name>/runs`.

Use as the agents build stage of the Butterbase journey. Implements the Agents section of 02-plan.md by delegating to the `agents` skill for each agent. Registers any required MCP servers, validates each graph_spec, creates the agent, and smokes it via invoke_agent. Skipped if the plan has no agents.

Use as the optional template-publishing stage of the Butterbase journey, after deploy (and substrate, if used). Walks the user through authoring a clone-ready README, bundling agent spec files, dry-running and pushing the repo snapshot, flipping visibility to public + listed, and self-clone-testing. Delegates the mechanics to the `templates` skill. Skipped by default in hackathon mode.

Use when publishing a Butterbase app as a public template (visibility + listed + repo snapshot), browsing the template gallery, or cloning a template with environment-variable preflight. Templates are public apps with a pushed repo snapshot that other users can fork into their own region.

Search for and install Agent Skills that give you specialized capabilities. Before starting work, ask might a skill exist that handles this better than my base knowledge? If the task involves specific technologies, frameworks, file formats, or expert domains. Search proactively, even if the user doesn't mention skills. Skills encode best practices, tools, and techniques you wouldn't otherwise have. Also use when users explicitly ask to find, install, or manage skills.

智能配图与 PPT 信息图生成器。支持三种模式：(1) 文章配图模式 - 分析文章内容，生成插图；(2) PPT/Slides 模式 - 生成批量信息图；(3) Cover 模式 - 生成封面图。所有模式默认生成图片，`--prompt-only` 只输出 prompt。支持 Bento Grid 功能展示图风格（--style bento）。触发词：配图、插图、PPT、slides、封面图、thumbnail、cover、bento grid、功能展示图、feature showcase。

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Document-driven OSpec workflow for AI-assisted development with change-ready initialization, execution, validation, and archive readiness.

Create or advance a lightweight OSpec change using the classic fast workflow.

Create or advance a full OSpec goal using the current document, task graph, worker, review, and evidence workflow.

This skill should be used when the user asks to "find a class", "search for symbol", "find usages", "find implementations", "search codebase", "find file", "class hierarchy", "find callers", "module dependencies", "unused dependencies", "project map", "project conventions", "project structure", "what frameworks", "what architecture", "find Perl subs", "Perl exports", "find Python class", "Go struct", "Go interface", "find React component", "find TypeScript interface", "find Rust struct", "find Ruby class", "find C# controller", "find Dart class", "find Flutter widget", "find mixin", "find Scala trait", "find case class", "find object", "find PHP class", "find Laravel model", "find PHP trait", or needs fast code search in Android/Kotlin/Java, iOS/Swift/ObjC, Dart/Flutter, TypeScript/JavaScript, Rust, Ruby, C#, Scala, PHP, Perl, Python, Go, C++, or Protocol Buffers projects. Also triggered by mentions of "ast-index" CLI tool.

本技能应在用户要求"数学建模"、"建模比赛"、"数模论文"、"数学建模竞赛"、"建模分析"、"建模求解"或提及数学建模相关任务时使用。适用于全国大学生数学建模竞赛(CUMCM)、美国大学生数学建模竞赛(MCM/ICM)等各类数学建模比赛。

数学建模的论文撰写阶段。当进入"论文撰写"阶段、需要撰写或修改论文时加载。负责根据建模分析和代码实现撰写完整论文，支持国赛（中文）和美赛（英文）。

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of \"Word doc\", \"word document\", \".docx\", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a \"report\", \"memo\", \"letter\", \"template\", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Search academic papers via OpenAlex + AnySearch with cross-validation for math modeling references. Invoke when user needs literature search, paper references, or when writing papers requires citations.

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Use when you need to install the embedded robot agents into either .cursor/agents or .claude/agents, selecting the destination interactively and copying the embedded agent definitions from project assets. This should trigger for requests such as Install embedded agents; Bootstrap .cursor/agents; Bootstrap .claude/agents; Copy robot agents. Part of cursor-rules-java project

Use when you need to generate an AGENTS.md file for a Java repository — covering project conventions, tech stack, file structure, commands, Git workflow, and contributor boundaries — through a modular, step-based interactive process that adapts to your specific project needs. This should trigger for requests such as Create AGENTS.md; Update AGENTS.md file; Add agent instructions. Part of cursor-rules-java project

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Generated skill from request: trinity auto-boot validator

Create your OpenAI Agents SDK skill in one prompt, then learn to improve it throughout the chapter

Create your OpenAI Agents SDK skill in one prompt, then learn to improve it throughout the chapter

Create your Google Agent Development Kit skill in one prompt, then learn to improve it throughout the chapter

Create your Claude Agent SDK skill in one prompt, then learn to improve it throughout the chapter

Build a specification-first Digital FTE that orchestrates accumulated intelligence from Lessons 1-7. Learn to compose execution skills into production-ready agents, validate against specifications, and position for monetization.

Create a skill that orchestrates the write-execute-analyze loop to autonomously process data. Learn to implement error recovery, iterate toward robust solutions, and test your skill across diverse input scenarios. This is where specification-driven development meets real problem-solving.

Agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth, waiting for content, running multiple browser sessions in parallel, and troubleshooting common failures. Use when the user asks to interact with a website, fill a form, click something, extract data, take a screenshot, log into a site, test a web app, or automate any browser task.

Create your agent-integration skill from OpenAI SDK and LiteLLM documentation before learning framework integration

Work autonomously without waiting for hand-holding — complete tasks end-to-end without asking for permission at every step.

Create your LiveKit Agents skill from official documentation, then learn to improve it throughout the chapter

Create your LiveKit Agents skill from official documentation, then learn to improve it throughout the chapter

Create your Pipecat skill from official documentation, then learn to improve it throughout the chapter

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

Main orchestrator for autonomous coding operations. Use when running autonomous sessions, coordinating components, managing the full lifecycle, or orchestrating implementations.

Manage checkpoints for rollback capability. Use when creating save points, rolling back changes, managing recovery points, or restoring previous states.

Manage git commits for autonomous coding. Use when committing feature implementations, creating descriptive commits, managing git workflow, or handling version control.

Validate acceptance criteria and feature completion. Use when checking if features pass, validating test results, verifying acceptance criteria, or determining feature completion status.

Create handoff packages for session transitions. Use when ending sessions, preparing for continuation, saving session state, or creating resumable context.

Manage knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.

Phase 4.0 — Acceptance Criteria lock checkpoint before implementation. Consolidates all ACs from PRD, TechSpec, and Tasks, presents to user for confirmation, and saves the locked AC list to accepted-criteria.md. Implementation only starts after explicit user confirmation.

Master controller for complete autonomous operation. Use when starting full autonomous projects, managing end-to-end workflow, controlling autonomous lifecycle, or running complete implementations.

Manage persistent memory for autonomous coding. Use when storing/retrieving knowledge, managing Graphiti integration, persisting learnings, or accessing episodic memory.

Analyze context and decide on continuation via Stop hook. Use when determining if work should continue, analyzing completion status, making continuation decisions, or implementing the Two-Claude pattern.

Route plain-language requests for Pi, Claude Code, Codex, OpenCode, Gemini CLI, or ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions ("telephone game" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation.

Add new Agent templates to the meta-agent-skills framework.

Use Codex (CLI + AppServer) as the full agent provider — planning, tool orchestration, native compaction, MCP tools, session resume — in place of the Claude Agent SDK. ChatGPT subscription or OPENAI_API_KEY. Per-group via agent_provider. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).

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a set of guidelines to build with Botpress's Agent Development Kit (ADK) - use these whenever you're tasked with building a feature using the ADK

Detects when user requests warrant critical analysis via /advise command

Agent Farm CLI quick reference. Use when running af commands to check correct syntax, subcommands, and flags. Prevents guessing at command names.

Agent skill for agent - invoke with $agent-agent

Agent skill for agentic-payments - invoke with $agent-agentic-payments

Agent skill for app-store - invoke with $agent-app-store

Agent skill for arch-system-design - invoke with $agent-arch-system-design

Agent skill for architecture - invoke with $agent-architecture

Use when designing an autonomous agent, planning agent architecture, building a scheduled automation, or creating a Claude Code agent workflow. Triggers: 'design an agent', 'build an automation', 'agent architecture', 'automate this workflow', 'create a scheduled agent', 'shell script agent'.

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Validates agent configurations for model selection, tool permissions, focus areas, and approach quality. Use when reviewing, auditing, improving agents, or learning agent best practices.

Agent skill for authentication - invoke with $agent-authentication

Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.

Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent

Agent skill for base-template-generator - invoke with $agent-base-template-generator

Integrate Java business systems with ReachAI SDK registration and capability sync. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure reachai.registry/reachai.project/reachai.capability, expose @ReachCapability methods, or verify SDK onboarding from a ReachAI manifest.