Automatically organizes files in a directory into a clean structure based on configurable rules — by file type, date, project, or priority — with support for duplicate detection, naming conventions, and archival strategies. Use when the user requests file organization or provides relevant inputs for this workflow.
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Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited. Use when the user requests threat modeling or provides relevant inputs for this workflow.
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Write compelling, persuasive marketing and sales copy using proven frameworks like AIDA, PAS, BAB, and 4Ps, with tone and voice customization, CTA optimization, and A/B variant generation. Use when the user requests copywriting or provides relevant inputs for this workflow.
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Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis. Use when the user requests analytics reporting or provides relevant inputs for this workflow.
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Automate repetitive tasks and workflows using scripting, file watchers, scheduled jobs, CI triggers, and API polling to eliminate manual toil. Use when the user requests task automation or provides relevant inputs for this workflow.
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Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.
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Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.
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Deploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms. Use when the user requests model deployment or provides relevant inputs for this workflow.
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Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.
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Populate databases with realistic, reproducible test data for development, testing, and staging environments. Use when the user requests database seeding or provides relevant inputs for this workflow.
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Design RESTful APIs with proper resource modeling, HTTP method semantics, status codes, pagination, versioning, and documentation. Use when the user requests api design or provides relevant inputs for this workflow.
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Implement OAuth 2.0 authentication flows including authorization code with PKCE, client credentials, and device code for secure API integration. Use when the user requests oauth 2 0 setup or provides relevant inputs for this workflow.
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Automatically generate clear, comprehensive documentation for codebases — including API references, inline docstrings, README files, and usage guides. Use when the user requests code documentation or provides relevant inputs for this workflow.
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Perform thorough code reviews on files or pull requests, checking for bugs, security vulnerabilities, performance issues, and style violations. Use when the user requests code review or provides relevant inputs for this workflow.
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Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.
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Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.
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Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.
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Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns. Use when the user requests refactoring or provides relevant inputs for this workflow.
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Transcribe meeting audio with speaker diarization, generate structured summaries with action items, decisions, and follow-ups, and support multiple audio formats and languages. Use when the user requests meeting transcription or provides relevant inputs for this workflow.
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Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting. Use when the user requests testing or provides relevant inputs for this workflow.
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Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.
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Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests. Use when creating a new MCP server, exposing an API or data source through MCP, reviewing an MCP server design, adding or revising MCP tools, or preparing an MCP server for production.
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Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.
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Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.
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