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Categorize extracted information into the appropriate knowledge base subdirectory (tasks, definitions, wiki, project-status, people, jira-drafts). Use when organizing extractions into the knowledge base.

Progressive learning methodology for structured onboarding using time-boxed learning paths (Day-1, Week-1, Month-1), validation checkpoints, and scaffolding principles. Use when onboarding new contributors, reducing ramp-up time from weeks to days, creating self-service learning paths, systematizing ad-hoc knowledge sharing, or building institutional knowledge preservation. Provides 3 learning path templates (Day-1: 4-8h setup→contribution, Week-1: 20-40h architecture→feature, Month-1: 40-160h expertise→mentoring), progressive disclosure pattern, validation checkpoint principle, module mastery best practice. Validated with 3-8x onboarding speedup (structured vs. unstructured), 95%+ transferability to any software project (Go, Rust, Python, TypeScript). Learning theory principles applied: progressive disclosure, scaffolding, validation checkpoints, time-boxing.

Automated skill for keeping AI knowledge bases current with latest model versions, framework updates, and best practices

knowledge-validator

Comprehensive global knowledge worker salary data with total market value calculations, sector breakdowns, geographic comparisons, and authoritative sources. USE WHEN discussing knowledge worker compensation, salary benchmarking, economic analysis of professional labor markets, or AI impact on wages.

Extension to kobo-translation skill specifically for translating video subtitles and transcripts in SRT format. Adds subtitle-specific guidelines for character limits, spoken language patterns, chunked translation context management, and maintaining readability on screen. Use this skill when translating SRT subtitle files for KoboToolbox tutorials, webinars, or educational videos.

Correct factually incorrect Kodex topic content

Bootstrap a Kodex knowledge base by analyzing codebase structure and creating topic stubs

Koin dependency injection framework for Kotlin. Use for Kotlin DI, Android development, Ktor backend, Compose Multiplatform, dependency injection patterns, and module definitions.

Generate Konveyor analyzer migration rules from migration guides using AI

Extract API request/response schema from Korean Public Data Portal (data.go.kr) documentation pages and generate structured JSON representation

Context-efficient codebase exploration using AST analysis. Use when exploring Kosmos architecture, understanding code structure, or preparing documentation for AI programmers. Triggers: xray, map structure, skeleton, interface, architecture, explore kosmos, warm start, token budget, context compression.

Sealed class configuration pattern for Kotlin applications with environment-specific settings

Use when domain-specific language design in Kotlin using type-safe builders, infix functions, operator overloading, lambdas with receivers, and patterns for creating expressive, readable DSLs for configuration and domain modeling.

Expert Kotlin developer specializing in Kotlin 2.0, Kotlin Multiplatform Mobile (KMP), Coroutines, and Ktor for building modern cross-platform applications and backend services.

Use when building Kotlin applications requiring coroutines, multiplatform development, or Android with Compose. Invoke for Flow API, KMP projects, Ktor servers, DSL design, sealed classes.

Kotlin testing with JUnit 5, MockK, Spring test slices, and Testcontainers. Provides mocking patterns, test slice selection, and TDD workflow for Spring Boot + Kotlin. Use when writing tests for Kotlin/Spring code, setting up test infrastructure, or debugging test failures.

Calculate and track key performance indicators (KPIs) from pilot data and defined metrics. Use when deriving success metrics, benchmarking performance against objectives, or preparing quantitative reports and dashboards.

KPI for measuring and improving code quality. Covers lint errors, type safety, test coverage, and verification pass rates. Use to ensure code meets quality standards.

Define KPIs, build performance dashboards, track metrics, and drive data-driven decisions

Create engaging changelogs for recent merges to main branch. Triggers on requests for daily/weekly changelogs, release notes, or summarizing recent changes.

Extract and query meeting data from Granola's local cache on macOS and Windows.

Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription), ZIP archives, YouTube URLs, or EPubs to Markdown format for LLM processing or text analysis.

Generate comprehensive Pull Request descriptions by analyzing git changes, commit history, Linear issues, and code structure for both GitLab and GitHub

Use a structured workflow with three interconnected documents (main specification, open issues, and log) to plan, track, and document complex implementations, ensuring clarity and continuity. Use when you detect the presence of LOG.md and OPEN_ISSUES_OVERVIEW.md files.

Creates a new go-kratos microservice skeleton with clean architecture (server/service/biz/data), minimal Wire setup, HTTP + gRPC metrics, and complete project structure. Use when scaffolding new services in the brizy-go-services monorepo.

Generates protobuf API definitions for go-kratos microservices with HTTP annotations, validation rules, and OpenAPI documentation. Use when defining service contracts and APIs.

Generates gRPC/HTTP service handlers for go-kratos microservices. Creates service structs, handler methods, and integrates with protobuf definitions. Use when implementing RPC handlers, adding API endpoints, creating dual transport (HTTP/gRPC) handlers, or connecting transport layer to business logic in kratos services.

Search/read 3rd-party Gradle (Kotlin/Java) dependency sources. Avoid directly accessing `.gradle`; instead, proactively use this skill to inspect source code of dependencies to learn API shapes or implementations.

Expert in managing Kubernetes clusters using kubectl-ai and kagent. Use this for generating Helm charts, troubleshooting pods, and automating cluster operations.

Identifies and cleans up outdated Kubernetes deployments based on namespace patterns and application version age. Discovers deployments, analyzes release dates from annotations, performs cleanup actions, and notifies stakeholders.

- Install/remove packages via `nix profile`

Package and deploy applications to Kubernetes with Dockerfiles, Helm charts, and local Minikube deployment. Use when containerizing applications, creating Kubernetes manifests, setting up Helm charts, deploying to Minikube, or preparing cloud-ready configurations. Focuses on local-first deployment with stateless services.

Kubernetes cluster management and troubleshooting. Query pods, deployments, services, logs, and events. Supports context switching, scaling, and rollout management. Use for Kubernetes debugging, monitoring, and operations.

kubernetes-manifests

Assist with Kubernetes interactions including debugging (kubectl logs, describe, exec, port-forward), resource management (deployments, services, configmaps, secrets), and cluster operations (scaling, rollouts, node management). Use when working with kubectl, pods, deployments, services, or troubleshooting Kubernetes issues.

Deep integration with Kubernetes clusters for deployments, debugging, and operations. Execute kubectl commands, analyze pod logs/events/resources, generate and validate manifests, and debug cluster issues.

Use when deploying or managing Kubernetes workloads requiring cluster configuration, security hardening, or troubleshooting. Invoke for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization.

Troubleshoot and manage Kubernetes clusters, including resource inspection, debugging, pod logs, events, and cluster operations. Use when the user needs to diagnose issues, inspect workloads, analyze pod failures, or perform Kubernetes cluster operations.

Kubernetes container orchestration. Use when deploying to Kubernetes, writing manifests, configuring Helm charts, or troubleshooting cluster issues.

Kumo development assistant for MySQL database management tool. Use when working on Kumo features, understanding architecture, writing tests, or navigating the codebase. Helps with React components, API endpoints, database features, and Electron app development.

Provides working code for KurrentDB (formerly EventStoreDB) - the event-native database.

Use when generating ConfigMaps and Secrets with Kustomize for Kubernetes configuration management.

Use when managing environment-specific Kubernetes configurations with Kustomize overlays and patches.

Kubernetes native configuration management with Kustomize. Use for environment-specific configs, resource patching, manifest organization, multi-environment deployments, and GitOps workflows. Triggers: kustomize, kustomization, overlay, base, patch, strategic merge, json patch, json6902, configmap generator, secret generator, namespace, namePrefix, nameSuffix, commonLabels, commonAnnotations, component, transformer, replacement, multi-environment, dev/staging/prod configs, k8s manifest management.

This skill should be used when generating detailed experimental procedures from LA-Bench format JSONL files. It orchestrates multiple subagents to parse input data, fetch reference materials, generate procedures, validate outputs, refine results, and produce final formatted outputs. Triggered by requests to process LA-Bench data or generate experimental protocols from data/public_test.jsonl or data/private_test_input.jsonl files.

Orchestrate the complete LA-Bench experimental procedure generation workflow from JSONL input to validated output. This skill should be used when processing LA-Bench format experimental data to generate and validate detailed experimental procedures. It coordinates parsing, reference fetching, procedure generation, and quality validation with 10-point scoring.

Provides lab notebook creation and management for individual bioinformatics experiments. Supports both Jupyter notebooks (.ipynb) for Python-based experiments and Markdown (.md) for non-Python experim