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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa

日本語の概要は準備中です。原文の説明を表示しています。

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

日本語の概要は準備中です。原文の説明を表示しています。

Bash scripting workflow for creating production-ready shell scripts with defensive patterns, error handling, and testing.

日本語の概要は準備中です。原文の説明を表示しています。

Write copy following Sentry brand guidelines. Use when writing UI text, error messages, empty states, onboarding flows, 404 pages, documentation, marketing copy, or any user-facing content. Covers both Plain Speech (default) and Sentry Voice tones.

日本語の概要は準備中です。原文の説明を表示しています。

Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.

日本語の概要は準備中です。原文の説明を表示しています。

Proper Lambda function structure with error handling

日本語の概要は準備中です。原文の説明を表示しています。

Modern Angular UI patterns for loading states, error handling, and data display. Use when building UI components, handling async data, or managing component states.

日本語の概要は準備中です。原文の説明を表示しています。

Builds production-ready REST API endpoints with validation, error handling, authentication, and documentation. Follows best practices for security and scalability.

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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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Uses Microsoft RESTler to perform stateful REST API fuzzing by automatically generating and executing test sequences that exercise API endpoints, discover producer-consumer dependencies between requests, and Bul: security and reliability bugs. The tester compiles an OpenAPI specification into a RESTler fuzzing grammar, configures authentication, runs test/fuzz-lean/fuzz modes, and analyzes results for 500 errors, authentication bypasses, resource leaks, and payload injection vulnerabilities. ...

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API integration patterns for Adynato mobile apps. Covers data fetching with TanStack Query, authentication flows, offline support, error handling, and optimistic updates in React Native/Expo apps. Use when integrating APIs into mobile applications.

日本語の概要は準備中です。原文の説明を表示しています。

Web API development conventions for Adynato projects. Covers API routes, middleware, authentication, error handling, validation, and response formats for Next.js and Node.js backends. Use when building or modifying API endpoints, server actions, or backend logic.

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Smoke tests contra URL Vercel deployada (preview ou production). Verifica saude minima do deploy — homepage, assets, auth, console errors, performance. Usa Playwright CLI ou suite de smoke tests. Use apos cada deploy para garantir que nada quebrou.

日本語の概要は準備中です。原文の説明を表示しています。

Deploys infrastructure on Oracle Cloud Infrastructure (OCI) with ARM64 instances (Always Free tier eligible). Handles compartments, VCNs, subnets, security lists, and compute instances. Use when: setting up Oracle Cloud infrastructure, deploying ARM64 instances, troubleshooting OUT_OF_HOST_CAPACITY errors, optimizing for Always Free tier. Keywords: oracle cloud, OCI, ARM64, VM.Standard.A1.Flex, Always Free tier, OUT_OF_HOST_CAPACITY, oci compartment, oci vcn

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Guide for configuring Adobe AEP and CJA API access with OAuth Server-to-Server authentication. Use when setting up API credentials or troubleshooting OAuth errors (401/403).

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Automated WCAG 2.1 AA accessibility testing with axe-core and Lighthouse CI. Auto-detects frontend framework (React, Next.js, Vue, Angular, Svelte, Astro, Flutter, React Native), discovers all routes and interactive components, installs Playwright + axe-core for page-level scanning and jest-axe/vitest-axe for component-level testing. Generates tests for color contrast (4.5:1), alt text, form labels, ARIA attributes, heading order, landmark regions, focus visibility, keyboard navigation (tab order, focus traps, modal focus management, skip-to-content), screen reader compatibility (aria-live regions, error announcements, toast notifications), and Flutter Semantics validation (48dp touch targets, semanticLabel). Reports violations by severity (critical, serious, moderate, minor) with WCAG criterion references. Use when adding a11y testing, auditing accessibility compliance, fixing contrast issues, or validating keyboard and screen reader support.

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Use when you need to design, review, or improve REST APIs with Micronaut — including @Controller routes, HTTP status codes, DTOs, Bean Validation, exception handlers, pagination, idempotency, ETag/If-Match, caching headers, versioning, contract-first OpenAPI (OpenAPI Generator), optional runtime OpenAPI via micronaut-openapi, and security annotations. This should trigger for requests such as Review or improve Micronaut @Controller REST APIs; Add validation, error handling, or align controllers with the OpenAPI contract on Micronaut HTTP layer. Part of cursor-rules-java project

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Use when you need to design, review, or improve validation in Quarkus applications — including Bean Validation on JAX-RS resources, @Valid on parameters and CDI beans, constraint groups, @ConfigMapping validation, custom constraints, nested DTO validation, and ExceptionMapper-based error mapping. This should trigger for requests such as Add validation support in Quarkus; Review Quarkus validation rules; Improve request validation in Quarkus REST APIs; Add custom validation constraints in Quarkus; Validate Quarkus @ConfigMapping properties. Part of cursor-rules-java project

日本語の概要は準備中です。原文の説明を表示しています。

Configures 1Password CLI with direnv for fast, secure credential loading. Activates for: 1Password + direnv setup, slow secrets (>2 sec), .env.op files, op:// references, AWS credentials via env vars, --reveal flag issues, repeated biometric prompts, creating 1Password items programmatically, op item get errors. Not for: 1Password GUI usage, SSH keys (use 1Password SSH agent).

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Deploy a new LLM model via LiteLLM (GitOps) and/or Azure (Terraform + node backend), add it to the ai toolkit, create the required pull requests, and verify end-to-end. Use when rolling out a model to any environment, adding a model to the toolkit, troubleshooting "model not available" errors, or finding pricing/token limits/model ids for any provider.

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Use whenever a question is about what the specification says or does — "what happens if", "can X do Y", "which errors can this return", "what does the screen show for", "what fields does this command take", "is there an operation for". Answers by running the Lean executable spec (scenarios, commands,

日本語の概要は準備中です。原文の説明を表示しています。

Use after a backend build succeeds or whenever authentication, authorization, actor/identity handling, external service calls (IdP, JWKS, DB, clock), logging, observability, configuration or error contracts are in play — delegates to the backend-design-reviewer agent for a design-quality review of the current branch.

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Busabase App-in-Skill dashboard visualizing a generated MOCK fleet of LLM agents running behind a shared AI gateway for a generic organization. Use when the user invokes $kelly-agent-observability or /kelly-agent-observability, wants to review agent health, call volume, latency, error rates, cost, or trace-level chain breaks across an agent fleet. This is a demo/reference dashboard over a generated mock fleet snapshot seeded into Busabase by a trusted script — never a live monitoring integration, never a real gateway or real agents.

日本語の概要は準備中です。原文の説明を表示しています。