3.7K skills · sorted by relevance
Research new models and API capabilities offered by providers behind Pipecat services. Produce dated reports for each service and local committed branches for clear updates, with review commands and separate publishing steps.
Review and improve code changes on the current branch using Pipecat conventions. Refine names and structure, add type hints and documentation, check example patterns, and make conservative performance improvements while preserving compatibility.
Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.
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Procedimiento estructurado de 6 pasos para mejorar, renovar o reconstruir pipelines existentes, carpetas de proyectos individuales, estructuras de documentación o stacks de software. Direccionable como "pipeline optimizer" (para pipelines temáticos completos, p. ej. de software, investigación o desarrollo de videojuegos) o "project-folder optimizer" (para carpetas de proyectos individuales dentro de un pipeline, p. ej. una herramienta de software o proyecto de artículo académico). Se activa ante tareas como "mejorar pipeline X", "optimizar el stack", "reconstruir Y", "renovación", "refactorización de pipeline", "limpiar carpeta de proyecto", "mejorar estructura de carpetas", "unificar convenciones", "consolidación de documentación", "integrar en sistema existente" o cualquier intervención sustancial en estructuras establecidas. Proporciona análisis del estado actual, aclaración del propósito, boceto del estado ideal, plan de brechas, identificación empírica de puntos de dolor y reevaluación con subagentes limpios. Previene estándares paralelos, duplicación y rupturas en el pipeline.
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Use when working with Aws Data Pipeline — aWS Data Pipeline workflow management and execution analysis. Covers pipeline inventory, pipeline definitions, execution status, object status, task runner health, and pipeline scheduling. Use when inspecting data pipeline health, debugging failed executions, reviewing pipeline definitions, or auditing pipeline schedules.
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Explains how to replace the removed $pipeline command with $plan for planning and $team for coordinated parallel work. This retirement notice provides migration guidance rather than an executable workflow of its own.
Review sales opportunities by stage to see deal totals, aging, and risks. Identify stalled deals, overdue closing dates, and missing next steps, then compare available opportunities with sales targets and recommend where to focus.
Edit Screenpipe walkthrough videos built from screenshots and narration while preserving the original procedure. Adjust wording, titles, pacing, and visual focus, then render an updated video when requested.
Prepare a short brief for an upcoming meeting by confirming the event and participants, reviewing relevant Screenpipe history, and summarizing prior commitments, unresolved decisions, likely purpose, and two useful questions with supporting links.
Resume interrupted work by reviewing recent Screenpipe activity and checking the current state of relevant files, jobs, or services. Recover the last verified result, identify unfinished steps, and avoid repeating actions that may already have completed.
Search Screenpipe recordings, screen text, audio transcripts, and synced-device history through its local API. Use the evidence to review activity, find meeting details, and inspect saved workflows, including recorded steps that can inform automation.
Add or modify Tauri commands in the screenpipe desktop app and regenerate TypeScript bindings, the code connecting frontend calls to Rust handlers. Follow the required annotations and checks to keep both sides consistent.
Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.
CI/CD is the backbone of modern software delivery. Continuous Integration catches bugs early. Continuous Deployment gets features to users fast. But poorly designed pipelines can be slow, flaky, and a source of constant frustration. This skill covers GitHub Actions (the most popular), GitLab CI, and general pipeline design principles. The focus is on pipelines that are fast, reliable, and maintainable. 2025 reality: Your pipeline is infrastructure code. It deserves the same care as your application. A flaky test in CI is worse than no test - it teaches developers to ignore failures. Use when "github actions, gitlab ci, ci cd, pipeline, workflow, deploy automation, continuous integration, continuous deployment, build pipeline, yaml workflow, ci-cd, github-actions, gitlab-ci, devops, automation, deployment, pipelines" mentioned.
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Write a data pipeline design document. Use when the user says "pipeline design doc", "document this pipeline", "pipeline architecture doc", "data flow document", "how does this pipeline work", "design doc for ETL", "pipeline spec", or needs to capture the architecture, data flow, and operational details of a data pipeline - even if they don't explicitly say "design doc".
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Use when working with Aws Codepipeline — aWS CodePipeline CI/CD pipeline management and execution analysis. Covers pipeline inventory, stage and action status, execution history, pipeline triggers, artifact stores, and action type configurations. Use when inspecting pipeline health, debugging failed stages, reviewing execution history, or auditing pipeline configurations.
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Analyze a chosen period of Screenpipe work history to find repeated research tasks, match them with existing skills, and prepare new skill drafts or combinations of existing skills for review.
Prepare screenpipe component releases by updating versions and dependency lock files, running required build checks, and triggering GitHub Actions. Verify build results and release artifacts, then hand public app publication over to a person.
Draft a project status report against stated milestones using recent Screenpipe activity and verified outcomes. Separate work performed from delivered and accepted results, summarize blockers and next actions, and cite dated sources with verification limits.
Create and run KubeSphere DevOps pipelines connected to Jenkins to automate builds and tests. Covers branch-specific workflows, required input parameters, private repository credentials, execution monitoring, logs, and downloading build output files.
Assess security weaknesses in CI/CD systems that automate software builds and delivery. Covering GitHub Actions, Jenkins, GitLab CI, and Azure DevOps, the skill examines unsafe inputs, excessive permissions, exposed credentials, and tampered build outputs, alongside detection and defensive controls.
Turn security findings into candidate code fixes with review results and summaries. Depending on the input, use independent source review or a pipeline with execution checks, then provide diffs for a person to review and apply.
NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.
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Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).
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