Results for “pipeline”

Build, run, and debug Nextflow data pipelines and nf-core workflows end to end, covering processes, channels, operators, configuration, testing, and deployment to HPC or cloud.

Scaffold and develop AI agents using OpenAI Agents SDK patterns, covering agent definition, tools, guardrails, handoffs, context, RAG pipelines, streaming, API routes, testing, and debugging.

Reads BA and TechLead artifacts to produce a sprint plan, task breakdown, and story point estimates, creating one tracked todo per sprint task for live pipeline tracking.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

Build lightweight AI agents with HuggingFace Smolagents — use CodeAgent (writes Python to act) or ToolCallingAgent (JSON tool calls), add built-in or custom Tools, orchestrate multi-agent pipelines with ManagedAgent, and run locally or via HF Inference API.

Automate continuous integration, deployment, and post-deployment verification of developed solutions. Use when setting up CI/CD pipelines, deploying to staging/production environments, running automated tests in CI, or verifying deployments. Ensures reproducible builds and reliable deployments.

Run replication, robustness, and specification-sensitivity workflows for Stata projects. Use when a researcher wants to reproduce a result, rerun a pipeline, compare specifications, audit a do-file sequence, or check whether a claim is stable.

Generates phrase-grounded bounding box annotations from image-caption pairs using a VLM, producing cleaned captions, referring expressions, and pixel-space bounding boxes.

Runs a multi-phase verification pipeline including build, type-check, lint, tests, security scan, and diff review to ensure code quality before creating a PR.

Migrates .NET test projects from VSTest to Microsoft.Testing.Platform (MTP), updating project files, CLI arguments, and CI/CD pipelines for MSTest, NUnit, and xUnit.net.

Guided multi-page redesign of an existing website through a four-phase pipeline — extract, direct, prototype, and migrate. Tracks progress incrementally per page so redesigns are resumable.

Integrate Xquik into apps, scripts, data pipelines, or AI agents for X API tasks like tweet search, user lookup, follower export, media actions, and webhook verification.

Rapidly classify malware samples against known family signatures using YARA rules, covering rule writing, scanning, and integration with analysis pipelines.

Integrate Trivy vulnerability scanning into CI/CD pipelines to detect container image CVEs, Dockerfile misconfigurations, and enforce severity-based quality gates.

Deploy MISP, configure threat feeds, use the PyMISP API for programmatic access, and build automated collection pipelines that aggregate IOCs from multiple community and commercial sources.

Verify container image integrity across CI/CD pipelines using the in-toto framework to generate and check cryptographically signed attestations.

Record and analyze post-trade outcomes for trading signals, tracking false positives, missed opportunities, and regime mismatches to generate feedback for signal aggregation and skill improvement.

End-to-end firmware and IoT penetration testing pipeline following OWASP FSTM methodology. Extracts, emulates, and exploits router, camera, and smart-home firmware using binwalk, EMBA, Firmadyne, and AFL++.

Generates a complete marketing pipeline for apps, dev tools, and SaaS products, including positioning, listing assets, launch plans, and growth strategies, adapting to product type.

Operates AI infrastructure as a production dependency: manages GPU utilization, MCP servers, LLM gateways, inference pipelines, token costs, semantic caching, and model observability.

Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".

Use when driving a feature prompt to a production-ready PR through discovery, definition, design, spec, issues, and dev phases, or when the user runs /feature or /feature resume — orchestrates the A-Team agentic pipeline over a target repo.

Use when a researcher asks where to start, which project skill fits the current stage of a study, or in what order literature scoping, source access, vault upkeep, drafting, citation verification, disclosure, and review response should run.

Expert data engineer specializing in building reliable data pipelines, lakehouse architectures, and scalable data infrastructure. Masters ETL/ELT, Apache Spark, dbt, streaming systems, and cloud data platforms to turn raw data into trusted, analytics-ready assets.

Route research prompts to evidence, retrieval, scraping, data, market, or scientific research skills. Use when prompts mention deep research, current facts, citations, Exa, iterative retrieval, search-first, scraping, data pipelines, PubMed, USPTO, gget, literature review, or scholar evaluation.

End-of-day response check and pipeline update. Run at 6 PM via cron. Checks all channels for replies via Chrome DevTools MCP + Zoho MCP, updates lead statuses, pauses cadence for responded leads, sends EOD report via Telegram.

End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.

When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.

Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.

Build and deploy apps/applets on the DNAnexus cloud genomics platform, manage data objects, run workflows, and use the dxpy Python SDK for genomics pipeline development and execution.

Deploy Aqua Security's Trivy scanner to detect vulnerabilities, misconfigurations, secrets, and license issues in container images across CI/CD pipelines and registries.

Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.

Generate images from text prompts, perform image-to-image translation, inpainting, and build custom diffusion pipelines using Stable Diffusion models via HuggingFace Diffusers.

Use when data operation or statistical analysis tasks do not need full program implementation, and the agent should operate Insyra through CLI/REPL, .isr scripts, or DSL workflows, including environment workflows, reproducible command pipelines, and command selection guidance.

Research a topic and grow your knowledge graph. Uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to processing pipeline. Triggers on "/learn", "/learn [topic]", "research this", "find out about".

Diagnose and fix failing CircleCI builds quickly and safely. Use when users ask to investigate failed CircleCI jobs, triage flaky pipelines, identify root causes from logs, and implement minimal fixes in configuration, test setup, or build-related code paths.