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Found 1,832 Skills
Salesforce Industries DataPack deployment automation using Vlocity Build. TRIGGER when: user deploys or validates OmniStudio/Vlocity DataPacks with vlocity commands (packDeploy/packRetry/packExport/packGetDiffs), sets up DataPack CI/CD pipelines, or troubleshoots DataPack migration errors. DO NOT TRIGGER when: deploying Salesforce metadata with sf project deploy (use platform-metadata-deploy), authoring OmniStudio artifacts (use omnistudio-*-build), or writing Apex/LWC business logic (use platform-apex-generate/experience-lwc-generate).
Use after benchmark-methodology has produced scored competitor profile cards. Assembles findings into a decision-grade report: landscape map, competitor profiles, benchmarking matrix, white-space analysis, strategic recommendations, and team alignment trigger questions. Final step in the three-skill competitive pipeline.
This skill should be used when building data processing pipelines with CocoIndex, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex is Python-native (supports any Python types), has no DSL, and uses version 1.0.0 or later.
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
Full spec-driven pipeline — walks brief → tokens → shape (spec) → craft (build) → converge → ship in one guided run. Writes `.ui-craft/spec.md`. Run when starting a net-new surface from scratch. Invoke when the user asks for sddesign on their UI, or mentions 'sddesign' alongside design / UI / frontend work.
Migrate workloads from Heroku to AWS. Triggers on: migrate from Heroku, Heroku to AWS, move off Heroku, migrate Heroku app, migrate Heroku Postgres to RDS, migrate Heroku Redis to ElastiCache, migrate Heroku Kafka to MSK, migrate dynos to Elastic Beanstalk, migrate dynos to Fargate, Heroku migration, move from Heroku to AWS, migrate Heroku Private Space, Heroku to Elastic Beanstalk, Heroku to ECS, Heroku to Fargate, leave Heroku, migrate off Heroku platform, what-if workshop, reprice Heroku migration, compare migration scenarios, workshop mode. Runs a 6-phase process: discover Heroku resources live via the authenticated Heroku CLI (read-only, consent-gated) and/or from Terraform files, Procfile/app.json, and optional billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. After Estimate, an optional what-if workshop can reprice region/HA/compute/Graviton scenarios without re-discovery. Clarify must finish before Design, Estimate, or Generate. Uses a flat resource model (no clustering or dependency graphs) with deterministic mapping tables for core services (Dynos → Elastic Beanstalk by default, Postgres → RDS/Aurora, Redis → ElastiCache, Kafka → MSK) and a fast-path table for 13+ common add-ons. Cedar/Fir generation detection is detect-only in v1. Pipeline/Review Apps are detect-only. Do not use for: GCP or Azure migrations to AWS, AWS-to-Heroku reverse migration, general AWS architecture advice without migration intent, Heroku-to-Heroku refactoring, or multi-cloud deployments that do not involve migrating off Heroku.
Orchestrator for the full dembrandt UX pipeline. Routes a UI/UX task through six ordered stages — brand foundation → design tokens → layout → components → UX polish → accessibility gate — loading the right sub-skill at each stage. Use when the task spans multiple design concerns: "design review", "build UI", "audit interface", "from brand to UI". For single-concern tasks (e.g. "review my colour palette") go directly to the relevant sub-skill instead.
Use when writing, reviewing, debugging, or documenting LanceDB pipelines in Python or TypeScript, especially code that should work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables. Helps avoid non-portable full-table materialization, choose idiomatic query/search patterns, apply LanceDB performance defaults for ingestion, indexing, filtering, and diagnostics, and resolve connections to the remote server for Enterprise-only operations such as jobs.
OpenTelemetry declarative YAML configuration for SDK setup. Use when configuring OpenTelemetry SDK providers (tracer, meter, logger), setting up OTLP exporters, defining sampling strategies, or writing otel config files. Triggers on "otel config", "OpenTelemetry YAML", "declarative configuration", "otelconf", "OTEL_CONFIG_FILE", "file_format", "configure tracing/metrics/logs export", or when the user is setting up telemetry pipelines via config files rather than code.
Guide for testing Polar payment integrations using the sandbox environment. Use this skill when: (1) Setting up the Polar sandbox for development; (2) Testing checkout flows without real payments; (3) Using Stripe test cards with Polar; (4) Writing integration tests for payment flows; (5) Testing webhooks locally with ngrok; (6) Mocking Polar in unit tests; (7) Setting up CI/CD pipelines with Polar sandbox; (8) Debugging payment issues in sandbox.
Meta-router and multi-agent conductor for design work that needs skill selection or a multi-stage pipeline — visual frontend (web pages, landing pages, product UI, mobile screens), data visualization, HTML deliverables (reports/diagrams/plans), artifacts, motion polish, and module/API interface design. The STABLE unified entry point — member skills churn underneath, this router discovers them live, picks one direction authority, delegates image-generation stages to Codex workers, and closes every pipeline with an evidence-first anti-slop quality loop. Do NOT invoke for a one-line CSS/copy tweak, for backend-only work, or when the user explicitly names a single member skill for a single-skill-sized task — those go direct.
Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Use for binder design, minibinder design, de novo binders, RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines, epitope/hotspot-targeted design, in-silico binder validation, and ranking designs by interface confidence.