Multi-tenant data isolation - critical security skill for SaaS applications. Use whenever each user or customer must see only their own data. Triggers on: row-level security, tenant isolation, "users see each other's data", "data leak", per-customer filtering, designer/owner access with data isolation. Covers three isolation patterns with tradeoffs, plus custom fields per tenant (explicit `tenant`, `tenant_fields_rights`) and sticky overrides for scheduled exports and alerts. Use eagerly - wrong isolation is a security breach, not a bug. Pair with core for token generation and data-integration for connection-level overrides.

This skill should be used when the user says "arn-code-standard", "standard change", "medium change", "standard feature", "standard fix", or wants a mid-ceremony implementation for a change that needs lightweight architectural context (spec-lite) and task-tracked execution but not the full feature-spec/plan pipeline. Bridges the gap between arn-code-swift and the full thorough pipeline. Includes spec-lite generation, structured plan, in-session execution, review-lite, and a unified change record.

This skill should be used when the user says "execute plan", "run the plan", "execute project", "execute all tasks", "run all tasks", "run plan in parallel", "execute with review gates", or wants to execute a full structured project plan after tasks have been created via arn-code-taskify. This skill runs ALL tasks from the task list — for executing a single specific task, use arn-code-execute-task instead. For Agent Teams mode, use arn-code-execute-plan-teams. Do NOT use this skill when executing a single task — use `/arn-code-execute-task` instead.

Prioritize a backlog of feature ideas based on impact, effort, risk, and strategic alignment with top 5 recommendations. Use when prioritizing a feature backlog, making scope decisions, or ranking product ideas.

Prioritize assumptions using an Impact × Risk matrix and suggest experiments for each. Use when triaging a list of assumptions, deciding what to test first, or applying the assumption prioritization canvas.

Count Plugin SDK consumers across live ClawHub releases and bundled OpenClaw plugins before deprecation, removal, or behavior changes; supports public release-date filters.

Check a Paddle account's onboarding status in sandbox or live — audit settings, credentials, notification destinations, catalog, domains, and verification, report what's done and missing against the go-live checklist, then fix the gaps the API allows with the user's approval.

Use Alethia, a patent-pending agent-native software runtime for executing and verifying application workflows, to interact with a real browser, run E2E checks, enforce safety policies, audit accessibility or security, and export signed evidence. Requires the @vitronai/alethia MCP server to be configured. Trigger patterns include "test this page," "verify the login flow," "audit accessibility," "compliance audit," "run WCAG/NIST," "check that deletes are blocked," and similar.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Angular forms: Signal Forms (experimental) and Reactive Forms. Trigger: When working with forms, validation, or form state in Angular.

Generate, review, and refine high-retention technical YouTube hooks, outlines, and scripts. Use when the user wants video scripting tailored to a topic, audience, runtime, and evidence-bound claims.

Create or update an Obsidian-friendly local markdown knowledge base. Use when the user wants LLM-maintained notes, wikilinks/backlinks, frontmatter, citations, local attachments, vault cleanup, or reusable research synthesis.

Generate closure proof with exact commands, outcomes, artifact paths, untested lanes, and residual risk. Use when work and review need a receipt before closeout, release, tracker movement, or handoff.

Analyze stale or conflicting lifecycle state by separating local, PR, CI, review, tracker, artifact, and session truth. Use when status is confused, work resumed after drift, evidence lanes disagree, or a closeout claim may be stale.

Review Codex skill-management intent to select its downstream owner. Use when the owner is ambiguous or the user explicitly asks for routing, not when a downstream lane is already clear.

Answers questions about Brian Douglas's talk on training AI on your own code. Use when a user asks about Brian Douglas's pipeline for capturing agent sessions, extracting skills from traces, fine-tuning small local models, tapes/steros tooling, SFT vs DPO decisions, or wants to apply his agent telemetry and training data approach to their own work with Claude Code, QLoRA, or parallel agents.

Summarizes Simon Maple's AI Native DevCon welcome and explains the conference framing: context window, latent space, tool pool, hallway track, attendee goals, and practical learning themes. Use when the user asks about the event opening, conference themes, AI-native development framing, or how to orient work around the talks.

Explains Paul Stack's architecture-first AI workflow and helps create safe design artifacts: intent documents, architecture constraints, planner/reviewer loops, UAT criteria, and agent-output review gates. Use when the user asks about humans owning architecture while agents implement, why vibes do not scale, or applying the talk to team workflow design.

Use when the user asks about Patrick Debois's talk "Coding Agents Don't Scale Themselves. Neither Do Your Teams. The Rise of Agent Enablement." — including questions about agent enablement teams, the three pillars (Enablement, Platform, Governance), the Context Development Lifecycle applied to org charts, AI product engineers, agent KPIs like turns-per-task, harnesses and shared context libraries, fixing the system vs. fixing the code, the barrel mental model, continuous learning as the next CI/CD, or how VPs / team leads / platform teams should scale AI coding agents across an org.

Answers questions about, summarizes, and applies May Walter's AI Native DevCon talk "From Blind Spots to Merged PRs" on runtime intelligence for coding agents. Use when the user asks about production telemetry for agents, prod-to-code mapping, performance fixes from runtime data, why automated PRs need provenance, Hud's runtime code sensor, weekly performance reports, AI-generated fixes with production context, or applying Walter's evidence-first agent workflow to engineering teams.