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Found 1,659 Skills
Use this skill when writing job descriptions, building sourcing strategies, designing screening processes, or creating interview frameworks. Triggers on job descriptions, candidate sourcing, screening criteria, interview loops, recruiting pipelines, offer management, and any task requiring talent acquisition process design.
Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.
Use when editing, reviewing, or auditing DRF viewsets and serializers in PostHog. Triggers on files in posthog/api/, products/*/backend/api/, products/*/backend/presentation/, or any file importing rest_framework serializers or viewsets. Covers OpenAPI spec quality, field typing, schema annotations, and DRF best practices that flow through the type pipeline to generated TypeScript types and MCP tools.
Interactive skill for eliciting, formalizing, and persisting DynamoDB access patterns. Use when the user wants to start designing a DynamoDB table, define entities, or document how their application will read and write data. This is Step 1 of a 3-step pipeline: access patterns -> table design -> query interfaces. The output is a structured .md file that feeds into the dynamodb-table-design skill.
2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection
Multi-agent pipeline orchestrator that plans and dispatches parallel development tasks to worktree agents. Reads project context, configures task directories with PRDs and jsonl context files, and launches isolated coding agents. Use when multiple independent features need parallel development, orchestrating worktree agents, or managing multi-agent coding pipelines.
Structured specification with explicit scope boundaries: user stories, acceptance criteria, out-of-scope definition, risks, and estimation. Positions before feature-design in the feature lifecycle pipeline. Use when: "write spec", "user stories", "define requirements", "scope this", "what should this do", "acceptance criteria", "define scope"
Create a new voice profile from writing samples. 7-phase pipeline: Collect, Extract, Pattern, Rule, Generate, Validate, Iterate. Wabi-sabi (natural imperfections as features) is the core principle. Use when creating a new voice, starting voice calibration, or building a voice profile from scratch. Use for "create voice", "new voice", "build voice", "voice from samples", "calibrate voice". Do NOT use for generating content in an existing voice (use voice-orchestrator), editing content (use anti-ai-editor), or comparing voices (use voice-calibrator compare mode).
Use when cognee is a Python AI memory engine that transforms documents into knowledge graphs with vector and graph storage for semantic search and reasoning. Use this skill when writing code that calls cognee's Python API (add, cognify, search, memify, config, datasets, prune, session) or integrating cognee-mcp. Covers the full public API, SearchType modes, DataPoint custom models, pipeline tasks, and configuration for LLM/embedding/vector/graph providers. Do NOT use for general knowledge graph theory or unrelated Python libraries.
Design token management with W3C Design Token Community Group specification, three-tier token hierarchy (global/alias/component), OKLCH color spaces, Style Dictionary transformation, and dark mode theming. Use when creating design token files, implementing theme systems, managing token versioning, or building design-to-code pipelines.
Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.
Expert knowledge for Azure Resource Manager development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when authoring Bicep/ARM templates, using template specs, deployment stacks, CI/CD pipelines, or ARM REST/CLI, and other Azure Resource Manager related development tasks. Not for Azure Policy (use azure-policy), Azure Resource Graph (use azure-resource-graph), Azure Portal (use azure-portal), Azure Blueprints (use azure-blueprints).