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Found 469 Skills
Application-developer GitOps work for OpenChoreo — onboarding Components (BYO image or source-build), authoring Workloads and `workload.yaml` descriptors, attaching PE-authored Traits, wiring component dependencies, generating ComponentReleases and ReleaseBindings via `occ` file-mode, promoting releases across Environments (single, project-wide, bulk), applying per-environment overrides, opening PRs upstream, and verifying Flux reconciliation. Use when the user says 'add a component to the GitOps repo', 'release my service via Git', 'open a PR for this Workload change', 'promote to staging via Git', 'bulk-promote my project', 'roll back a release', or operates a developer-side change from inside a scaffolded GitOps repo.
Feature-level UX audit for React/Next.js code. Catches what Lighthouse, axe, ESLint, and Storybook miss — state coverage gaps (missing loading/empty/error), form data loss on validation, broken focus management, optimistic UI without rollback, skeleton-induced layout shift, vague microcopy, and 25+ other modern frontend UX bugs. Diff-aware (audits changed files only) and produces a 3-tier ship-readiness verdict (release-blocker / fix-this-sprint / backlog) grouped by surface, with concrete fixes using modern React 19 APIs (useActionState, useFormStatus, useOptimistic, useTransition, Suspense). Use before merging a frontend PR, before shipping a feature, or when asked "is this checkout/onboarding/dashboard ready?", "review this PR for UX bugs", "audit this component", "what would break in production?", "is this ready to ship?"
Systematic documentation authoring workflow for AI coding agents. Analyzes repositories to determine what documentation is needed, classifies each document by Diataxis type (tutorial, how-to, reference, explanation), and generates accurate, maintainable documentation that stays synchronized with the codebase. Handles greenfield projects (no docs exist), brownfield updates (refresh, enhance, rewrite existing docs), and doc audits with workflow-specific guidance for each. Use when the user requests documentation for a project: README creation, API reference, architecture docs, developer guides, changelogs, or any technical writing tied to a codebase. Also use when existing docs need auditing, updating, rewriting, or restructuring. Triggers on phrases like "write a README", "document this project", "API reference", "architecture doc", "developer guide", "getting started guide", "tutorial", "how-to", "audit our docs", "what docs are missing", "refresh the docs", "Diataxis", "doc the public API", "write a CHANGELOG", "explain this codebase", "onboarding doc", or "ADR". Triggers when creating or editing `README.md`, `CONTRIBUTING.md`, `CHANGELOG.md`, `docs/`, `mkdocs.yml`, `docusaurus.config.*`, `sphinx`/`conf.py`, ADRs, or any markdown file paired with code. Triggers when public APIs, CLI flags, configuration options, or environment variables change and the user wants the docs kept in sync. Do NOT use for standalone prose, marketing copy, blog posts, design documents, RFCs unrelated to a codebase, or documents where the source of truth is not source code.
Turns AI-generated demo UIs into real usable product workflows. Use when building, reviewing, or finishing apps, dashboards, forms, CRUD flows, onboarding, checkout, settings, auth-like flows, or any interface that must work beyond a static mockup.
Autonomously set up an OpenClaw bot on a fresh Yandex Cloud VM in Kazakhstan (kz1-a, Karaganda). Asks the user for exactly two things — a Telegram bot token and one of three LLM access options (Anthropic API key, OpenRouter API key, or OpenAI Codex OAuth via ChatGPT Plus/Pro subscription) — then handles VM creation, hardening, OpenClaw install, CEO AI OS workspace seeding, Telegram pairing, chat_id auto-detection, and bot-reply verification on its own. The only other actions the user performs are pressing /start in Telegram once and (if Codex) confirming a device code on auth.openai.com. Use when the user says install OpenClaw to Yandex Cloud, deploy OpenClaw to YC Kazakhstan, set up my CEO bot in YC KZ, I am at OpenClaw workshop and need my own bot, create a Yandex Cloud VM for OpenClaw, or any close paraphrase. Targets a ~15-minute end-to-end run for non-DevOps users (founders, CEOs, marketing leads). Supports two modes of accessing Yandex Cloud — Plan A (the user's own YC Kazakhstan account via OAuth) and Plan B (a workshop-key bundle provided by the workshop organizer, for participants without their own YC account). The mode is auto-detected from the inputs. For local-machine OpenClaw install, use openclaw/install.sh in this repo instead. Companion skill openclaw-guide is required; prepare-yc-workshop is the matching organizer-side skill that produces the bundles consumed in Plan B; openclaw-user-onboarding is auto-invoked after Step 5 to collect the five basic facts about the user (identity, focus, style, tools, anti-patterns) and write them into USER.md so the bot is useful from message one.
Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, customer-onboarding, claims adjudication) in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time. Pairs Lean / Six Sigma / Theory-of-Constraints canon with deterministic stdlib-only Python tools to produce a process map, a ranked bottleneck list (with severity + root-cause hypothesis), and a cycle-time analysis (P50, P90, value-add ratio, Little's-Law throughput). Distinct from sales-pipeline, system-reliability (SLO), and strategic-OKR work — this is tactical process documentation for internal operations.
Connects NemoClaw to a local inference server. Use when setting up Ollama, vLLM, TensorRT-LLM, NIM, or any OpenAI-compatible local model server with NemoClaw. Trigger keywords - nemoclaw local inference, ollama nemoclaw, vllm nemoclaw, local model server, openai compatible endpoint, switch nemoclaw inference model, change inference runtime, nemoclaw additional model, nemoclaw sub-agent model, openclaw sub-agent, agents.list, sessions_spawn, vlm-demo, nemoclaw tool calling, ollama tool calls, vllm tool-call-parser, raw json in tui, nemoclaw inference options, nemoclaw onboarding providers, nemoclaw inference routing.
Explains how to run NemoClaw on a remote GPU instance, including the deprecated Brev compatibility path and the preferred installer plus onboard flow. Use when deploying NemoClaw to a remote VM, onboarding a Brev instance, or migrating away from the legacy `nemoclaw deploy` wrapper. Trigger keywords - deploy nemoclaw remote gpu, nemoclaw brev cloud deployment, nemoclaw plugins, openclaw plugins, install openclaw plugin, nemoclaw onboard from dockerfile, nemoclaw brev web ui, nemoclaw getting started, brev quickstart, nvidia nemotron agent, nemoclaw sandbox hardening, container security, docker capabilities, process limits.
Build or update a comprehensive financial plan covering retirement projections, education funding, estate planning, and cash flow analysis. Use for new client onboarding, annual plan reviews, or scenario modeling. Triggers on "financial plan", "retirement plan", "can I retire", "education funding", "estate plan", "cash flow analysis", or "plan update".
Creates selected backend Markdown documentation from code evidence, including architecture/design docs, bounded context maps, developer onboarding, API contracts, and gap analysis for code smells, project-rule violations, DDD issues, coupling, and improvement opportunities. Use when the user asks to document a backend, map backend architecture or domains, onboard backend developers, audit backend documentation gaps, or produce objective backend docs. Do NOT use for frontend docs, generic README writing, or implementing backend changes.
Use this skill when the user wants to review, audit, improve, or plan email sending best practices. This includes deliverability, inbox placement, sender reputation, consent, list hygiene, subject lines, preview text, preference centers, onboarding emails, lifecycle emails, product updates, or deciding between marketing and transactional email. It works for any email stack, but when Loops is involved, use Loops behavior and docs as the source of truth. Trigger on phrases like "email deliverability", "inbox placement", "sender reputation", "double opt-in", "unsubscribe", "subject line review", "preview text", "lifecycle emails", "onboarding emails", "product update email", "transactional vs marketing", or "email sending best practices". Do not prefer this skill for pure API implementation; use the Loops API skill for integration details.
Use when the user asks for a broad codebase review, substantial PR/branch review, architecture audit, tech-debt scan, cleanup assessment, structural sanity check, or design-alignment review. Default workflow: use sub-agents when available unless specifically forbidden; do not require the user to mention sub-agents, council mode, delegation, or parallel review. Focus on cruft, duplication, weak boundaries, missed reuse, lifecycle/concurrency risks, test/roadmap drift, and code aesthetics. Do not use for narrow bug fixes, ordinary small-diff reviews, frontend visual QA, repo-onboarding docs, or OpenAI Agents SDK production-readiness review. Output evidence-backed findings first, then pressure points, design alignment, open questions, and follow-through.