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Found 1,084 Skills
Playbook iterativo para llevar proyectos Node y TypeScript (NestJS + React en monorepo) a cumplir Quality Gates de SonarQube sin romper build ni pipelines. Usar cuando se necesite subir cobertura priorizando New Code, eliminar issues nuevos (Bugs, Vulnerabilities, Code Smells), revisar Security Hotspots y controlar duplicacion y deuda tecnica.
Complete ClickHouse operations guide for DevOps and SRE teams managing production deployments. Provides practical guidance on monitoring essential metrics (query latency, throughput, memory, disk), introspecting system tables, performance analysis, scaling strategies (vertical and horizontal), backup/disaster recovery, tuning at query/server/table levels, and troubleshooting common issues. Use when diagnosing ClickHouse problems, optimizing performance, planning capacity, setting up monitoring, implementing backups, or managing production clusters. Includes resource management strategies for disk space, connections, and background operations plus production checklists.
Production-ready CI/CD configurations for Playwright — GitHub Actions, GitLab CI, CircleCI, Azure DevOps, Jenkins, Docker, parallel sharding, reporting, code coverage, and global setup/teardown.
Intelligent skill router and creator. Analyzes ANY input to recommend existing skills, improve them, or create new ones. Uses deep iterative analysis with 11 thinking models, regression questioning, evolution lens, and multi-agent synthesis panel. Phase 0 triage ensures you never duplicate existing functionality.
Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.
Implement iOS authentication patterns including Sign in with Apple (ASAuthorizationAppleIDProvider, ASAuthorizationController, ASAuthorizationAppleIDCredential), credential state checking, identity token validation, ASWebAuthenticationSession for OAuth and third-party auth flows, ASAuthorizationPasswordProvider for AutoFill credential suggestions, and biometric authentication with LAContext. Use when implementing Sign in with Apple, handling Apple ID credentials, building OAuth login flows, integrating Password AutoFill, checking credential revocation state, or validating identity tokens server-side.
Token optimization best practices for MCP server and tool interactions. Minimizes token consumption while maintaining effectiveness. USE WHEN: user mentions "token usage", "optimize tokens", "reduce API calls", "MCP efficiency", asks about "how to use less tokens", "MCP best practices", "limit output size", "efficient queries" DO NOT USE FOR: Code optimization - use `performance` instead, Text compression - this is about API usage patterns, Cost optimization (infrastructure) - use cloud/DevOps skills
Author or modify Azure TypeSpec API specifications. USE FOR: Any task that creates, modifies, or troubleshoots .tsp files or TypeSpec API specifications — including but not limited to API versioning evolution(add new preview version, add new stable version), ARM resource type(tracked, proxy, extension, child resources) or data-plane resource definitions, resource operations (CRUD, PATCH, custom actions, paging, async/Long Running Operations), models, enums, unions, properties, decorators, constraints, parameters, and swagger-to-TypeSpec conversion. DO NOT USE FOR: SDK generation from TypeSpec, releasing SDK packages, single MCP tool calls that do not require multi-step workflows. TOOLS/COMMANDS: azsdk_typespec_generate_authoring_plan, azsdk_run_typespec_validation
Manage App Store Connect code signing resources using the `asc` CLI tool. Use this skill when: (1) Managing bundle identifiers — register, list, or delete (`asc bundle-ids`) (2) Managing signing certificates — create from CSR, list, or revoke (`asc certificates`) (3) Registering or listing test devices (`asc devices`) (4) Managing provisioning profiles — create, list, or delete (`asc profiles`) (5) Setting up the full code signing chain for CI/CD pipelines (6) User says "set up signing", "create a profile", "register my device", "revoke cert", "list certificates", "create bundle id", or any code-signing related task
Manage App Store Connect team members and user invitations using the `asc` CLI tool. Use this skill when: (1) Listing team members with their roles (`asc users list`) (2) Filtering members by role (`asc users list --role DEVELOPER`) (3) Updating or replacing a member's roles (asc users update --user-id ID --role ADMIN) (4) Revoking or removing access for a departing employee (asc users remove --user-id ID) (5) Listing pending invitations (asc user-invitations list) (6) Inviting a new team member by email (asc user-invitations invite) (7) Cancelling a pending invitation (asc user-invitations cancel) (8) User says "revoke access", "remove team member", "offboard user", "invite developer", "add someone to App Store Connect", "manage team roles", "who has admin access", "grant access", "onboard", or any team/user management task in App Store Connect
This skill should be used when the user asks about Wardley Mapping, evolution stages, strategic positioning, situational awareness, technology evolution, competitive landscape, creating maps, value chain decomposition, gameplay patterns, doctrine assessment, doctrine maturity, climatic patterns, climate assessment, build vs. buy decisions, inertia analysis, D&D alignment of strategies, peace/war/wonder cycles, play-position matrix, pioneers/settlers/planners, or quantitative evolution scoring including differentiation pressure, commodity leverage, weak signal detection, and readiness scores.
Self-evolving AI agent system with 26 tools, three-layer memory, MCP plugins, and 24/7 self-repair in pure Python.