Use when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI. Triggers include "break this down", "split into workstreams", "plan V6", "what should we build first", "run these in parallel", "dependency graph", "merge order", "worktree split", or any request to turn planning docs into running shep features. Produces a workstream plan first, then drives `shep feat new` / `shep feat start` wave by wave. Part of the Shep autonomous SDLC platform — https://shep.bot

Use when starting a piece of feature or fix work in this repository — taking or opening its GitHub issue, cutting the branch (a worktree on the local bare repo, an in-place branch on the remote environment) and producing the implementation plan, including the wiki pages the work will make stale and its observability plan. Also the step for turning a tracker finding into a branch without publishing it.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Now with automatic session recovery after /clear.

Store and retrieve unstructured objects, file uploads, and cache-like state on Netlify using the @netlify/blobs key/value API from Functions, Edge Functions, and Build Plugins. Use when a task involves saving user file or image uploads, persisting form or contact-form submissions, storing generated output from Background Functions (sitemaps/processed media/bulk-email results), building read-only asset stores, adding client-side blob expiration, or wiring file-based blob uploads at deploy time. Not for per-user, transactional, or relational data (counters/balances/sessions) — reach for Netlify DB there instead.

Validate your code changes through the no-mistakes pipeline - automated code review, tests, lint, docs, push, PR, and CI - before they reach the configured push target. Use when the user asks to run no-mistakes, gate or ship or validate their changes, push safely, asks you to do a task and then validate it, or invokes /no-mistakes.

Applies and explains code conventions across TypeScript, React, C#, and Markdown. Enforces naming rules, file naming patterns, TSDoc and XML doc standards, inline comment intent (the *why*, not the *what*), code structure, error handling, async patterns, and dead code policy. Also enforces ADR and contributor doc decisions, and flags decisions that appear stale or misaligned with current tooling. USE FOR: convention questions, code review against project standards, applying naming rules, auditing intent comments, checking TSDoc completeness, enforcing recorded ADR decisions, and flagging stale architectural decisions. DO NOT USE FOR: security vulnerability scanning, performance profiling, runtime debugging, or generating net-new code without a review target.

Operation-aware node configuration guidance. Use when configuring nodes, understanding property dependencies, determining required fields, choosing between get_node detail levels, or learning common configuration patterns by node type. Always use this skill when setting up node parameters — it explains which fields are required for each operation, how displayOptions control field visibility, and when to use patchNodeField for surgical edits vs full node updates.

Check and fix Apache 2.0 license headers and dependency licenses using skywalking-eyes. Use before submitting a PR.

Render a proposed redesign of a page on the current website as a self-contained static HTML file, then iterate via the impeccable craft loop. Per-page, idempotent, stale-aware. Use when the user asks for a redesign prototype, a before/after comparison, a design preview, a page mockup, a visual diff of the redesign, or invokes /stardust:prototype.

Migrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service using BPA CSV/cache, CAM/MCP discovery, and a one-pattern-per-session workflow. Use to review/scan a project for AEMaaCS migration (generates a read-only migration-runbook.md covering all patterns via per-pattern detection strategies), for BPA/CAM findings, Cloud Service blockers, or fixes for scheduler, ResourceChangeListener, replication, EventListener, OSGi EventHandler, DAM AssetManager, HTL data-sly-test lint, Classic UI dialog migration (lui — ExtJS/Coral 2 → Coral 3), Custom Design Widgets (cdw), Guava cache → Caffeine swaps (guavaCache), and static→editable template modernization. OSGi configs → Cloud Manager — scan for secrets and $[secret:]/$[env:] placeholders. Converts AMS/on-prem Dispatcher configs to AEMaaCS (Branch E, beta). After discovery, migration hands off each (pattern, file) pair to code-assessment for pattern guides/shared references; template modernization, legacy UI, and Guava cache follow references/ modules.

[BETA] Expert assistance for the Adobe I/O CLI plugin `@adobe/aio-cli-plugin-aem-rde` — the `aio aem rde` / `aio aem:rde` command tree used to deploy, inspect, log-tail, snapshot, and troubleshoot AEM Rapid Development Environments (RDEs). Activate ONLY when the user explicitly references RDE concepts: 'AEM RDE', 'Rapid Development Environment', `aio aem rde`, `aio aem:rde`, `aem-rde`, RDE snapshots, RDE deploy/install, `rde install`, `rde inspect`, `rde status`, `rde history`, `rde reset`, the `@adobe/aio-cli-plugin-aem-rde` package, or Cloud Manager program/environment configuration that is specifically for an RDE environment. Do NOT activate on generic AEMaaCS phrases like 'deploy to AEM Cloud', 'push my bundle', 'tail the publish log', 'cloud sandbox', or unqualified 'dispatcher-config / frontend / env-config deployments' — those belong to Cloud Manager pipelines, not RDE. This skill is in beta. Verify all outputs before applying them to production projects.

How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy.

Run a comprehensive WCAG accessibility audit covering perceivable, operable, understandable, and robust principles. Use this skill whenever the user wants to audit accessibility, review WCAG compliance, fix accessibility issues, prepare for accessibility certification, address an accessibility lawsuit risk, or systematically improve a site's accessibility. Triggers on accessibility audit, WCAG audit, a11y audit, accessibility compliance, ADA compliance, screen reader test, keyboard navigation, accessibility report, fix accessibility, axe scan. Also triggers when accessibility issues have been reported and need systematic remediation.

Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."

Run a WCAG 2.1 AA accessibility audit on a design or page. Trigger with "audit accessibility", "check a11y", "is this accessible?", or when reviewing a design for color contrast, keyboard navigation, touch target size, or screen reader behavior before handoff.

Multi-agent orchestrator for Claude Code. Use when user mentions gastown, gas town, gt commands, bd commands, convoys, polecats, crew, rigs, slinging work, multi-agent coordination, beads, hooks, molecules, workflows, the witness, the mayor, the refinery, the deacon, dogs, escalation, or wants to run multiple AI agents on projects simultaneously. Handles installation, workspace setup, work tracking, agent lifecycle, crash recovery, and all gt/bd CLI operations.

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' 'WebMCP,' 'do listicles still work for AI,' 'ChatGPT stopped citing comparison pages,' or 'AI citation format shift.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

Backend development guide for the Phoenix AI observability platform (Strawberry GraphQL, SQLAlchemy async, FastAPI). Use this skill when writing or modifying Python server code in the phoenix repo — adding mutations, types, migrations, or tests. Trigger on any backend task touching src/phoenix/server/, src/phoenix/db/, or tests/unit/server/.