cicd-pipeline-qe-orchestrator
Orchestrate end-to-end quality engineering across CI/CD pipelines, from commit-stage unit testing and shift-left strategies to production-stage synthetic monitoring and compliance gates.
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163 skills found
Orchestrate end-to-end quality engineering across CI/CD pipelines, from commit-stage unit testing and shift-left strategies to production-stage synthetic monitoring and compliance gates.
An intelligent gateway that analyzes, scores, and routes user requests across 27 agents, 27 skills, and 14 MCPs to optimize Claude Code execution.
Transform raw data into compelling, decision-driving narratives using visualization strategies, story frameworks, and persuasive structures for analytics and executive reporting.
A microworld operating system for LLM-based agent living memory, transforming filesystems into navigable rooms and code into habitable worlds.
A comprehensive moderation toolkit for Civitai, providing automated user management, strike systems, image review, content regulation, and CSAM reporting via tRPC API.
Structured parallel brainstorming agent for ideation and conceptual expansion. Uses multi-agent perspectives to evolve vague ideas into practical, actionable visions. Ideation only, not for task planning.
Architects enterprise AI agents from structured specs, generating production-ready code, data flow diagrams, and platform-specific logic for ServiceNow, Salesforce, and Snowflake.
Architectural planning and scaling for spectre-build, covering GUI, server layers, multi-model support, and industrial pipeline orchestration.
A comprehensive library of 305+ modular instruction packages, Python CLI tools, and agent workflows designed to extend the capabilities of AI coding assistants like Claude Code, Cursor, Aider, and Gemini CLI.
Advanced context engineering system for orchestrating AI agents, memory management, and token optimization to improve long-term persistence and project intelligence.
Safely execute, test, and verify commands discovered in documentation with real output capture, performance tracking, and git-aware safety protocols.
Foundational mental model and operational rules for using TraceMem to ensure secure, auditable, and compliant AI agent execution.