Results for “pipe”

Orchestrates a dimensional-analysis pipeline to annotate codebases with unit/dimension comments, discover dimensional vocabulary, and detect arithmetic bugs from unit mismatches or precision loss.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Runs a full research-to-ideation pipeline: competitive gap analysis, technology trend research, user feedback analysis, and feature ideation, producing structured reports.

Download a video from YouTube, Instagram, TikTok, Twitter/X, Facebook, Vimeo (and 1000+ yt-dlp sites) to local disk, and optionally transcribe it. Standalone utility for repurposing and analysis — not part of the static publishing pipeline.

Guided webinar creation pipeline from vague idea to production-ready deliverables. Use when creating a webinar from scratch. Triggers: 'create a webinar', 'build a webinar', 'webinar creation', 'new webinar', 'help me make a webinar'.

Agent profile for design data pipelines, transformations, imports, exports, warehouse models, validation, and freshness checks. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Agent profile for handle CI/CD, environments, build pipelines, deployment topology, secrets, and operational readiness. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments. Use when the user requests ci cd or provides relevant inputs for this workflow.

Expert skill for building LLM applications with the LlamaIndex framework — RAG pipelines, multi-agent orchestration, event-driven workflows, knowledge graph construction, production deployment, and evaluation. Use when working with LlamaIndex or comparing RAG and agent orchestration frameworks.

Expert TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

Use when a project's visual foundation — typography scale, color palette, spacing — needs establishing as canonical design tokens; the target has no design system yet (the 0→1 case), the A-Team design phase needs tokens for the lofi and the spec's design-system mapping, or a human runs the token flow standalone ("set up the design system", "pick fonts and colors", "define design tokens"). In the A-Team pipeline it is conducted by ateam-design in pipeline mode — no interview; inputs derive from context.md's

Coordinate the end-to-end pipeline from CAD or source assets to SimReady USD assets, including conversion, material and physics assignment, validation, conformance, and optional packaging.

Provides a fast in-memory DataFrame library for datasets that fit in RAM, with lazy evaluation, parallel execution, and an Apache Arrow backend for ETL pipelines and analytics.

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.