Results for “task-pipeline”

Orchestrate autonomous AI development pipelines through your Kanban board, managing multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

Load pre-trained models from Hugging Face Hub, run pipeline inference, generate text, and fine-tune models on NLP, vision, audio, and multimodal tasks using the Transformers library.

This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.

`analysis-agent`/`task-agent`/`review-agent`: use for release identity, compatibility, exposure, stop, rollback, or recovery; skip pipeline-only and no-release work.

`analysis-agent`/`task-agent`/`review-agent`: use when CI/CD checks, artifacts, deployment gates, IaC, or Helm behavior changes; skip when pipelines are unaffected.

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".

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.

`task-agent`/`review-agent`: use when a Kubernetes workload, route, identity, health, capacity, or rollout boundary changes; skip image-only, pipeline-only, and non-cluster work.

Orchestrate teams of agents — DAG execution, routing, state sharing, failure recovery. Use when coordinating parallel agents in task pipelines with dependencies or distributing work by capability.

Build multi-agent pipelines with CrewAI — define Agents with roles/goals/backstory, assemble them into a Crew, assign Tasks sequentially or in parallel, and wire LLM + tools per agent.

Full-stack task execution pipeline — discovers tools via semantic search, routes to the right agent, then executes via GODMODE. Chains manus-tool-discovery → manus-orchestrator → godmode-system in sequence.

Designs and implements CI/CD pipelines, infrastructure automation, and workflow optimization to reduce manual tasks.

Expert knowledge for Azure DevOps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing org/projects, Boards/work items, repos/pipelines, Analytics/OData/Power BI, or Azure DevOps Server, and other Azure DevOps related development tasks. Not for Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

Expert knowledge for Azure Pipelines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when setting up YAML CI/CD, service connections/Key Vault, self-hosted agents, IIS/Kubernetes deploys, or Git integrations, and other Azure Pipelines related development tasks. Not for Azure DevOps (use azure-devops), Azure Boards (use azure-boards), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

Design and implement CI/CD pipelines and automation — fast checks on every PR, safe releases, and repeatable workflows. Load when setting up GitHub Actions (or similar), adding pre-merge gates, automating releases, or the user asks "add CI", "set up workflows", "automate checks", "CD pipeline". Not for one-off local scripts (use task-specific tooling).

Expert knowledge for Azure Artifacts development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, views/promotion, retention, GitHub Actions CI/CD, or npm/.npmrc config, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines).

Use this skill for GitHub Actions, checks, release gates, deployment automation, reproducible pipelines. Trigger when the task involves devops work related to CI CD Automation, production implementation, audits, debugging, strategy, or validation.

Create a SAM.gov search monitor from a pursuit or expiring row — persists to Pipeline and writes sam_monitor.md in the pursuit folder. Primary admin rote task for capture managers watching recompetes.

Expert knowledge for Azure App Testing development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when running Azure Load Testing, Playwright Workspaces, JMeter/Locust tests, VNet/private endpoint tests, or CI/CD test pipelines, and other Azure App Testing related development tasks. Not for Azure Test Plans (use azure-test-plans), Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Playwright Workspaces (use azure-playwright-workspaces).

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.

Expert knowledge for Azure Test Plans development including limits & quotas, security, and integrations & coding patterns. Use when configuring test run custom fields, data retention, permissions, access levels, or tcm.exe automation, and other Azure Test Plans related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure App Testing (use azure-app-testing), Azure Boards (use azure-boards).

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.

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.

Expert knowledge for Azure Boards development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing work items, queries/charts, GitHub links, Scrum boards/sprints, or WIQL-based integrations, and other Azure Boards related development tasks. Not for Azure DevOps (use azure-devops), Azure Test Plans (use azure-test-plans), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos).

Optimize SVG files with SVGO — remove unnecessary metadata, minify paths, merge shapes, configure plugins, and integrate into build pipelines. Use when tasks involve reducing SVG file size, cleaning up exported SVGs from design tools, building icon systems, or automating SVG optimization in CI/CD.

Expert knowledge for Azure Repos development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing Azure Git/TFVC repos, branch policies/PRs, migrations from TFVC/SVN, CodeQL/GHAS scanning, or auth/permissions, and other Azure Repos related development tasks. Not for Azure DevOps (use azure-devops), Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Artifacts (use azure-artifacts).

Run the full autonomous engineering pipeline end-to-end (plan, work, code review, test, commit, push, open PR, watch CI, fix CI failures until green). Use only when the user explicitly requests hands-off execution of a software task and provides a feature description; do not auto-route casual conversation here.

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

Run GitHub's Spec-Driven Development (SDD) workflow via the `specify` CLI — install spec-kit, initialize a project for one of 30+ AI coding agents (Claude Code, Copilot, Gemini, Cursor, Codex, Qwen, opencode, Kiro, etc.), and drive the constitution → specify → plan → tasks → implement command pipeline. Use when the user wants to bootstrap a Spec-Driven Development project, install `specify-cli`, generate executable specs before code, or invoke the `/speckit.*` slash commands (`/speckit.constitution`, `/speckit.specify`, `/speckit.plan`, `/speckit.tasks`, `/speckit.implement`, `/speckit.clarify`, `/speckit.analyze`, `/speckit.checklist`). Triggers on: spec-kit, speckit, specify, specify init, spec-driven, spec driven development, SDD, /speckit, executable spec.

Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.

Expert knowledge for Azure Stack Edge development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when deploying IoT Edge modules, Kubernetes/GPU apps, DeepStream pipelines, Arc GitOps, or local ARM workloads, and other Azure Stack Edge related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure IoT Edge (use azure-iot-edge), Azure Local (use azure-local), Azure Virtual Machines (use azure-virtual-machines).

World-class autonomous sales and revenue skill system. Use ANY time the user asks to sell, pitch, prospect, close, negotiate, launch, monetize, build funnels, write outreach, craft proposals, develop pricing, design offers, write sales copy, create email sequences, plan campaigns, position products, handle objections, create playbooks, build pipeline, forecast revenue, develop GTM strategy, optimize conversions, write ad copy, create sales decks, score leads, nurture prospects, upsell, cross-sell, retain customers, write case studies, price products, structure enterprise deals, create battle cards, script discovery calls, build affiliate programs, plan product launches, create webinar funnels, design pricing pages, A/B test offers, analyze unit economics, calculate LTV/CAC, or ANY other sales, revenue, monetization, go-to-market, or commercial growth task. If it involves selling, revenue, deals, pipeline, or commercial strategy — USE THIS SKILL. Trigger aggressively.

Programmatic video creation in React with Remotion. Use this skill whenever the user writes Remotion, npx create-video, remotion studio/render/still, useCurrentFrame/interpolate/Sequence, Composition, Mediabunny, or any React-driven video/animation/caption/motion-graphics task — including scaffolding a project, writing markup, rendering/exporting (incl. transparent), adding captions/subtitles, making Studio-editable animations, building a Remotion SaaS (Player/Lambda/Vercel/Cloudflare), or getting media metadata. Do NOT use for ffmpeg-only pipelines, video.js/players, WebRTC live streaming, framer-motion web animation, or After Effects.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in...

Turn "block until condition X is met" into an agent wake-up via the bash_async tool. Use when you need to wait for a one-shot event without burning context — a webhook arriving, a CI pipeline finishing, a file appearing, a long shell command completing — AND you want this conversation to resume with full context when the event fires. Distinct from pollers (recurring) and the synchronous Bash tool (blocks the turn). The completion event lands as a fresh turn on the spawning channel; no polling needed.