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Found 11,806 Skills
Guides CI/CD for agent skills repositories and skill packages—pipeline design (build, test, validate, package), GitHub Actions for PR checks and release promotion, environment gates, secrets hygiene (no secrets in repo), skill-creator integration (quick_validate.py, package_skill.py), .skill artifact strategy, rollback, and operational runbooks for skill releases. Use when the user mentions CI/CD, CI/CD engineer, pipeline design, GitHub Actions, skill validation CI, package skills, release pipeline, deploy skills, PR checks, continuous integration, or skill release workflow—not application-only CI without skill packaging (devops), pre-flight plan go/no-go (build-validator), IDP or golden paths (platform-engineer), org-wide SLO and error-budget programs without pipeline ownership (site-reliability-engineer), or portfolio catalog governance without pipeline YAML (ai-skill-manager).
Generate a fully working React + Vite app that explains a codebase's workflows, data types, and architecture through interactive visuals — click-to-step animated walkthroughs with auto-play, sequence diagrams, animated packet tracers, message inspectors that toggle between named-field view and raw JSON, and collapsible code peeks with file:line citations. Splits the repo into 4–6 domain clusters and dispatches one content agent per cluster to write the pages in parallel. The skill bundles its own reference pages (under references/examples/) so it works in any repo. Use this skill whenever the user asks for interactive docs, animated explainers, an "agent team" for docs, one page per domain, wants to visualize a system's request flow or wire protocol, or any visual documentation site. Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 in .claude/settings.json.
Create, show, and guide with ScreenCI videos in an already-initialized project by editing `.video.ts` files and running the Screenci workflow.
Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
Placekey integration. Manage data, records, and automate workflows. Use when the user wants to interact with Placekey data.
Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, mapping data between nodes, or referencing webhook data in workflows. Use this skill whenever configuring node fields that reference data from previous nodes — expressions are how n8n passes data between nodes, and getting the syntax wrong is the most common source of workflow errors.
Design background Data Atlas style agents for Itô basket research, market discovery, parameter drafting, and human-in-the-loop editing. Use for architecture and workflow planning, not live order execution.
Pay with Bolt integration. Manage data, records, and automate workflows. Use when the user wants to interact with Pay with Bolt data.
Use when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
AI-assisted academic research workflows for literature review, paper writing, peer review, and research pipelines
Creates comprehensive GitHub Actions CI/CD workflows for linting, testing, building, and deploying. Includes caching strategies, matrix builds, artifact handling, and failure diagnostics. Use for "GitHub Actions", "CI pipeline", "workflow automation", or "continuous integration".
Use before any Luma / 拾光 / 拾光智能体 / 拾光工具 production workflow. Defines common luma-cli rules for auth, tool discovery, projects, artifacts, runtime resources, and safe agent behavior.