
Use this skill when a local container won't start, a service is unreachable from the host, a local docker-compose stack is misbehaving, or as the Docker-layer diagnosis step of a local bugfix flow — including when the user describes the symptom without naming Docker — to run a Docker-specific local diagnostic that collects container status, logs, networking, and resource usage and diagnoses issues, applying the SRE or DevOps role for investigation. For multi-scope environment analysis (Docker + Kubernetes + CI runner + drift snapshot + optional auto-fix) use `/env-analyze` instead.
Use this skill when designing AI agent systems or RAG pipelines that need a principled approach to selecting, structuring, and delivering context to LLMs — the context engineering knowledge base covering the context stack model, RAG pipeline design, memory engineering, and multi-agent context coordination.
Use this skill when designing or implementing Python backend code in FastAPI projects, reviewing Python pull requests, or onboarding new contributors to FastAPI conventions — a Python backend patterns knowledge base covering Python 3.12+, FastAPI layered architecture, Pydantic v2 schemas, SQLAlchemy 2.0 async ORM, Alembic migrations, async/await, dependency injection, pytest with httpx.AsyncClient, OAuth2/JWT security, ruff and mypy strict, structured logging and Prometheus metrics.
Use this skill when a service, module, or system needs documentation, audit, or tech-debt assessment — onboarding a new team to an undocumented service, establishing a pre-redesign "what do we actually have?" baseline, or a tech-debt audit before roadmap planning, including when the user does not say "architecture" — to analyze the existing architecture and produce ARCHITECTURE.md, current-state C4 diagrams, a component inventory, gap analysis (current vs target), and a technical-debt register. Routes to system-architect (default — system/module/service analysis), cloud-architect (`--cloud` for cloud-resource inventory + cost + compliance review), or devops-architect (`--cicd` for CI/CD platform audit + DORA baseline + governance review).
Use this skill when a new feature with a PRD needs architectural definition before `/plan` can decompose it for implementation — including when the user describes a feature to be built but does not say "architecture" or "design" — to produce a feature-level design pack — ARD/ADR, C4 diagrams, API contracts, data models, NFR spec, and security review from the PRD. Routes to solution-architect (default — feature-level system design), cloud-architect (`--cloud` for cloud infra design, landing zones, networking, multi-cloud), or devops-architect (`--cicd` for pipeline design, deployment strategy, platform engineering).
Use this skill when assembling a subagent prompt and you need its context kept small and on-topic (db-engineer gets DB sections, ui-ux-designer gets UI/UX sections, etc.) — to load a per-role slice of project context from CLAUDE.md / AGENTS.md / ARCHITECTURE.md / project memory and return Markdown ready to embed in a subagent prompt.
Use this skill when deploying a completed feature to staging for smoke-testing and verification before a production release — the deploy-to-staging workflow that builds, deploys to a staging environment, runs smoke tests, and verifies.
Use this skill when authoring or reviewing a deploy workflow that needs the strategy and rollback knowledge — the deployment strategy reference (rolling, blue-green, canary, recreate) plus rollback decision criteria, pre-deploy checklist, and post-deploy verification, loaded by the `/deploy-production` and `/deploy-staging` workflows; not a workflow itself.
Use this skill when starting a task to surface prior decisions, gotchas, or conventions that apply — to query `.ai-skills-memory/learnings.md` (L4 project memory) and optionally `~/.claude/ai-skills/learnings.md` (L5 user-global) by topic or keyword, returning matching excerpts with surrounding context.
Use this skill when extending, repairing, or improving plugin assets, when ingesting a `/feedback` report as a fix-cycle backlog, or when you do not remember which lower-level command is right for the job — the umbrella workflow for ai-skills plugin-asset authoring and maintenance: creating, auditing, fixing, improving, refactoring, and migrating skills, agents, rules, hooks, prompts, schemas, and rubrics inside the plugin. Auto-classifies the request, loads the right knowledge skills (`@prompt-engineering`, `@context-engineering`, `@team-protocols`), and spawns the right subagents (`prompt-engineer`, `system-architect`, `python-engineer`, `software-engineer`, `qa-engineer`, `eval-judge`) via the `Agent` tool.
Use this skill when investigating a production incident, when an alert fires (latency spike, error rate, pod crashloop), when a customer-reported issue needs prod telemetry, or as the diagnosis step of an incident-response or production-bugfix flow — including when the user describes a prod symptom without asking to "analyze" — to analyze the production environment by collecting Kubernetes pod status, managed database health, logs, metrics, and networking and diagnosing issues, supporting GCP, Azure, and AWS via the `cloud-platforms` skill and applying the SRE or DevOps role.
Use this skill when the agent is running a security audit, scanning dependencies, prioritizing CVEs by exploit likelihood, signing release artifacts, generating SBOMs for compliance, or reviewing CI/CD artifact integrity — software supply chain security knowledge covering SBOM generation (Syft, CycloneDX, SPDX), SLSA provenance levels 1 through 4, Sigstore Cosign artifact signing, EPSS exploit-prediction scores, CISA KEV known-exploited catalog, dependency-confusion / typosquatting / lockfile-validation patterns, and EO 14028 and EU Cyber Resilience Act SBOM mandates.
Use this skill when running tests in a specific stack, wiring CI test steps, or designing test infrastructure — per-stack test runner commands and conventions for TypeScript/JavaScript, Java, Python, Go, and .NET covering unit, integration, E2E, and coverage commands plus Testcontainers conventions and quality-gate signals, a reference table for picking the right command after stack detection. Knowledge skill — loaded as context by `/test-local` and `/test-strategy`, never invoked directly.
Use this skill when the user needs to write a blog post, create or optimize page content, draft conversion copy, generate visuals, or run a quality audit on existing content — including when the request describes the deliverable without saying "content creation" — to run the content creation workflow plus tools knowledge — blog-post authoring (8-step pipeline), page content optimization, AI text/image generation tools, quality gates, GEO/AEO structure, and humanization. Accepts content type blog-post | page | landing | email | other.
Use this skill when reviewing a pull request, merge request, or code change before merge, conducting an architecture review, auditing code for security/performance/quality, or running a pre-merge quality gate — including when applied by a Reviewer or QA subagent — to produce a verdict (APPROVE / REQUEST_CHANGES / COMMENT) using Google's eng-practices framing (code health over perfection) and conventional comments vocabulary (nit / suggestion / issue / praise). Distinct from /security-scan (no dependency CVE scan) and /security-audit (no full OWASP audit).
Use this skill when migrating between technologies (REST → gRPC, monolith → microservices, on-prem → cloud, framework upgrade), executing a major redesign driven by tech debt or scale, or a strategic platform shift (database engine, message bus, identity provider, observability vendor) — including when the user describes a "from X to Y" move without saying "evolve" or "migration" — to plan and document architecture migration and evolution — an ADR for the migration decision, target-state ARCHITECTURE.md, a phased migration plan (strangler fig, expand-contract, branch by abstraction), fitness functions, and a rollback strategy. Not for new feature design — use `/architecture-design` instead.
Use this skill when shipping to production after staging is green, when a deploy needs a documented rollback plan, or when the APPROVE-gate workflow is required — the deploy-to-production workflow (final checks, approval gate, deploy, verify, rollback plan) that uses the `deployment-procedures` skill and requires explicit APPROVE before any production mutation.
Use this skill when implementing a feature with the canonical Anthropic `Agent` tool available — the multi-agent feature implementation pipeline (DEVELOP → REVIEW → QA with developer(s), reviewer, QA, and lead orchestrator) that spawns specialized subagents via the Agent tool (`subagent_type: "ai-skills:<role>"`); preferred over the single-agent /feature-dev fallback.
Use this skill when designing UI, defining or auditing a design system, reviewing UX deliverables, specifying components, evaluating accessibility, or producing redlines and tokens for engineering handoff — the UI/UX knowledge base covering user research, personas, jobs-to-be-done, user journeys, usability testing, information architecture, wireframing, prototyping, visual design, typography, color theory, grid systems, visual hierarchy, micro-interactions, animation, design systems, atomic design, design tokens, Figma workflows, WCAG 2.2 accessibility, responsive and mobile-first design, Material Design 3, Apple HIG, Nielsen heuristics, Gestalt principles, conversion-centered design, and design-to-code handoff.
Use this skill when the user wants "architecture" work but the scope is ambiguous and you want auto-classification — as the architecture entry-point router that classifies the request as feature design, existing-system analysis, or migration/evolution and then dispatches to `/architecture-design`, `/architecture-analyze`, or `/architecture-evolve`. For a known scope, invoke the specialized command directly instead.
Use this skill when running pre-release validation, detecting calibration regression, or tuning a skill — to run plugin evaluation tiers (Tier 1 linters and Tier 2 judge-calibration drift smoke) by wrapping the eval/runner.py harness. Tier 1 is free (no LLM); Tier 2 budgets tokens per `eval/config.json`. Tier 3 (full behavioral suites) is planned but not yet shipped — runner returns error code 3 if invoked.
Use this skill when investigating, diagnosing, and fixing a reported bug — including when the user reports an error message or broken behavior without saying "bugfix" — to run an end-to-end bugfix workflow with a mandatory DEVELOP → REVIEW → QA pipeline: the Lead orchestrates triage intake, environment analysis (`/env-analyze` for local, `/analyze-prod` for production), evidence collection, and bug report, then spawns developer/reviewer/QA via the Anthropic `Agent` tool to apply, review, and verify the fix.
Use this skill when reviewing how the plugin behaved across recent runs, after a release to confirm reliability, before filing a plugin bug report, or when planning the next plugin improvement cycle — to collect and analyze past Claude Code session logs for the ai-skills plugin and surface agent, subagent, skill, command, and hook errors, timeouts, unexpected exits, and other anomalies that point at plugin defects. Defaults to the last 7 days of sessions for the current project. Produces an extended Markdown report on disk plus a brief on-screen summary.
Use this skill when the agent is starting any Terraform or Helm change and needs to determine whether the apply flow is owned by a controller (in which case the change is a git PR, not a local imperative command) — for GitOps and orchestrator detection covering marker patterns and routing rules for Argo CD, Flux, Atlantis, HCP Terraform / Terraform Cloud, Spacelift, and env0. Loaded by `/infra-change` and related infrastructure workflows; not a workflow itself.
Use this skill when writing or editing public-facing text intended to be cited or quoted by LLMs (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) — blog posts, landing pages, help articles, FAQ blocks, programmatic SEO templates, pillar guides — to optimize that text for GEO/AEO (Generative Engine Optimization and Answer Engine Optimization).
Use this skill when editing, reviewing, or producing any public-facing text — blog posts, social media, UI copy, emails, landing pages, documentation — to remove signs of AI-generated writing by detecting and fixing AI patterns including inflated symbolism, promotional language, vague attributions, em dash overuse, rule of three, AI vocabulary, negative parallelisms, sycophantic tone, and filler phrases.
Use this skill when changing infrastructure config (Terraform, Helm, K8s) and a destructive or production-touching operation is involved — to run the infrastructure change workflow (Terraform plan/apply, Helm diff/upgrade, Kubernetes manifest changes) with mandatory approval gates. Applies DevOps role. Safe-by-default with plan review before any mutation.
Use this skill when explicit single-agent inline execution is requested or `/develop` is impractical for the situation — to run a single-agent fallback for feature implementation that detects tech stack and applies one engineering role inline (no Developer/Reviewer/QA spawning). The canonical Anthropic `Agent` tool is always available in modern Claude Code, so `/develop` (multi-agent pipeline) should be the default and this fallback should be selected only on a documented technical block.
Use this skill when capturing patterns, lessons learned, or architectural decisions worth surfacing in future sessions — to perform a curated write to L4 (project) or L5 (user-global, opt-in) learnings memory. Spawns memory-curator agent to dedupe + PII-filter + schema-validate before append.
Use this skill when the agent is defining or revising marketing positioning, messaging, channel mix, content pillars, a tactical/calendar plan, or success metrics — for marketing strategy frameworks covering hierarchy (mission → positioning → messaging → channels → pillars → tactical → calendar), channel-selection (audience/resource/content fit), content-pillar model, and measurement framework (vanity / engagement / pipeline / revenue). The reusable knowledge layer behind /marketing-init (one-time setup) and /marketing (recurring ops) — both reference these frameworks instead of inlining them.
Use this skill when bootstrapping marketing for a project that has no MARKETING.md yet — to run one-time marketing strategy initialization that gathers project context, runs an ICP/positioning interview, defines strategy (positioning, messaging, channels, pillars, tactical plan), and produces marketing/MARKETING.md and marketing/content-calendar.md in the target repo. Routes ongoing recurring operations to /marketing.
Use this skill when running any daily/weekly/monthly marketing task in a project that already has marketing/MARKETING.md — the recurring marketing operations dispatcher for social posts, blog posts, email campaigns, trend research, analytics, content repurposing, community engagement, and strategy review. For first-time strategy setup, use /marketing-init.
Use this skill when troubleshooting a deployment, debugging CI flakes, validating an environment before deployment, or when local diagnostics need to span more than just Docker — the multi-scope environment diagnostic covering Kubernetes, CI runner, network, resource usage, and a cross-scope drift snapshot for baselining across runs, with optional `--auto-fix` for container-level safe actions. For Docker-only local triage use `/analyze-local`. Not for code bugs (use /bugfix) or application-level diagnostics (use /spike).
Use this skill when running a security audit, performing a security gate on code review, threat-modeling an AI/LLM component, or verifying audit coverage against the canonical OWASP categories — the OWASP Web Top 10 (2021) and OWASP GenAI/LLM Top 10 (2025) reference catalog with per-category descriptions and mitigation patterns. Covers broken access control, cryptographic failures, injection, insecure design, security misconfiguration, vulnerable components, auth failures, integrity failures, logging/monitoring failures, SSRF; and LLM01 prompt injection, LLM02 sensitive info disclosure, LLM03 supply-chain, LLM04 model poisoning, LLM05 improper output handling, LLM06 excessive agency, LLM07 system prompt leakage, LLM08 vector/embedding weaknesses, LLM09 misinformation, LLM10 unbounded consumption.
Use this skill when the user has a PRD or feature brief and needs an executable plan before code is written — to plan feature implementation across services and roles by parsing the PRD/feature brief, reading project architecture (ARCHITECTURE.md, CLAUDE.md), decomposing into work packages per service/role with complexity estimates, and running a multi-reviewer feedback loop (product-manager + solution-architect + system-architect).
Use this skill when authoring or running a workflow that needs cloud-platform-specific commands, or when reviewing such workflows for platform coverage — as a knowledge module (not a workflow itself) providing platform-specific CLI commands, managed service patterns, networking, IAM, observability, and operational procedures across GCP, Azure, and AWS. Loaded by `/analyze-prod`, `/infra-change`, `/deploy-production`, and similar workflows.
Use this skill when researching a content topic, gathering supporting facts/data, auditing existing content, building a blog content brief, or generating headlines/value-props/feature-to-benefit/FAQ/social-proof copy for a blog post, landing page, or email — the reusable knowledge layer behind /content-creation and /marketing email + social-post operations, providing content research workflows (topic research, data/fact gathering, existing-content audit, blog content brief template) and AI text-generation prompt templates for headline generation, value propositions, feature-to-benefit translation, FAQ generation, and social proof.
Use this skill when the agent is authoring or running a Helm upgrade, previewing chart changes with helm diff, rolling back a release, inspecting release history, or managing values files for a Helm chart — for Helm procedures covering helm diff (via helm-diff plugin), atomic upgrade with --atomic and --wait, helm install / upgrade / rollback, release history, and values-file conventions. Loaded by `/infra-change` and other Kubernetes deployment workflows; not a workflow itself.
Use this skill when starting a new feature from concept — to convert a 1–3 sentence feature idea into a complete design pack (PRD, ARCHITECTURE, UX-FLOW, DATA-MODEL, RISKS, IMPLEMENTATION-PLAN, REVIEW-LOG) via a multi-agent three-wave pipeline orchestrated by feature-design-lead. Not for refactors (use /refactor), bugfixes (use /bugfix), or refining existing PRDs (use /develop directly).
Use this skill when scaffolding a new skill (workflow, knowledge, or companion utility) under `plugin/skills/` and you need spec-conformant frontmatter, an eval test-case stub, and memory-write points pre-wired — the internal procedure for `/plugin-author create`. Generates a new skill INSIDE the ai-skills plugin conforming to the agentskills.io specification (https://agentskills.io/specification) and ai-skills plugin conventions. No longer slash-invocable — call `/plugin-author create <name>` instead. Read by the `prompt-engineer` agent at task start when DEV-ing or reviewing a plugin skill.
Use this skill when the user asks to edit internal markdown documentation and source code must stay untouched — the internal documentation workflow to edit technical docs, ADRs, PRDs, design notes, release notes, and UI microcopy without touching source code, applying the Content Writer role. For public-facing blog posts, landing pages, and marketing content use `/content-creation` (it owns the GEO/SEO/humanizer pipeline). For full multi-document user-facing packs (README + API ref + runbook + tutorial) use `/docs-pack`.
Use this skill when running a quality gate over staged changes before a git commit to catch lint/format/type/test failures — a pre-commit quality gate that lints, formats, type-checks, tests, and validates before committing. Detects existing pre-commit framework / Husky / Lefthook / lint-staged and routes to it; otherwise runs an inline stack-detected gate. Not the [pre-commit framework](https://pre-commit.com) itself — see Step 0 for framework detection.
Use this skill when onboarding users to a feature, producing API documentation, or writing operational procedures — to generate a user-facing documentation pack (README, API reference, runbook, tutorial) for a module or feature; distinct from the `docs` knowledge skill — `docs-pack` produces git-versioned user docs while `docs` is general documentation guidance.
Use this skill when the user asks to analyze, investigate, assess, evaluate, compare, or research a codebase, architecture, system, product, market, or research topic in a structured way — including requests that need explicit scope, framework-driven reasoning, and separated facts/inference/recommendation but do not use the word "analyze" — to run a deep analysis workflow producing traceable scope, evidence, and conclusions. Not for quick one-off lookups or implementation work — this is an investigation-and-conclusions workflow.
Use this skill when installing the plugin, upgrading to a new version, or troubleshooting unexpected plugin behavior — a self-diagnostic for the ai-skills plugin that validates skill frontmatter, hook executability, run-log parseability, and judge calibration. Default mode is fast and cost-free; `--calibrate-judge` is opt-in.
Use this skill when breaking up large functions, migrating to new patterns, extracting to a library, or improving testability — to plan and execute a code refactor that changes structure without changing behavior via a multi-agent pipeline with a mandatory test-equivalence gate enforced by RALF. Not for adding features (use /develop) or fixing bugs (use /bugfix).
Use this skill when the agent is implementing frontend code in Next.js projects, reviewing React or Next.js pull requests, onboarding to App Router conventions, or designing a frontend architecture that needs canonical React/Next.js patterns — for canonical React and Next.js conventions covering Next.js App Router routing and rendering, React Server Components vs Client Components boundary, TypeScript strict-mode discipline, Tailwind CSS and shadcn/ui styling, React Query and Server Actions data fetching, Core Web Vitals performance optimization, WCAG 2.2 accessibility, Next.js metadata API and structured data for SEO, unit testing with Vitest and React Testing Library, and end-to-end testing with Playwright.
Use this skill when auditing a repo for vulnerabilities, before a release or production deploy, when a CVE alert needs investigation, when reviewing a third-party dependency upgrade, or as the security gate inside `/code-review`, `/security-audit`, or `/develop` — to run the security scan workflow (dependency audit, OWASP checklist, secrets scan, vulnerability report) applying the software-engineer role with security focus.
Use this skill when designing prompts, building AI features, or auditing LLM security — a prompt engineering knowledge base covering technique taxonomy, template patterns, the OWASP LLM Top 10 security checklist, eval frameworks, structured output contracts, and cost optimization.
Use this skill when verifying code by running tests, analyzing failures, or addressing coverage gaps — to run the test suite, analyze failures, auto-fix obvious issues, and re-run; serves as a sub-workflow for /develop, /bugfix, and /pre-commit and uses the `test-strategy` skill.
Use this skill when picking a diagnostic vocabulary for a latency, error-rate, saturation, or crashloop investigation, when authoring or reviewing an analysis workflow that needs the method reference, or when correlating SLI metrics and active alerts against a method's signal set — a knowledge skill of industry-canonical observability methodologies (Four Golden Signals, RED, USE, Distributed Tracing) with method-to-problem mapping that explains which signals each method surfaces and when each method applies. Loaded by `/analyze-prod`, `/analyze-local`, `/env-analyze`, `/infra-change`, and `/bugfix` workflows when production-context diagnosis needs a named methodology.
Use this skill when the user asks to validate a feature, write or improve tests, report a bug, or audit acceptance criteria coverage — /qa = QA-task workflows (verification, test creation, bug reports, exploratory). /test-strategy = test design principles + pyramid + coverage targets (knowledge). /test-local = test execution workflow.
Use this skill when cutting a release, tagging a version, preparing a changelog, or publishing release notes — to run the release workflow (version bump, changelog generation, signed git tag, release notes) which detects existing release tooling (release-please, semantic-release, Changesets, GoReleaser, cargo-release, JReleaser) and routes to it, otherwise runs an inline stack-detected release.
Use this skill when auditing a public-facing page or site for search visibility, before publishing a marketing or blog page, when traffic drops or indexing issues are suspected, when validating JSON-LD / Open Graph / canonical / sitemap setup, or as the SEO gate inside `/content-creation` and `/docs-pack` — to run the SEO review workflow (technical SEO audit, meta tags, structured data validation, Core Web Vitals, crawlability, indexability, AI search readiness) applying the SEO Engineer role.
Use this skill when evaluating new tech, prototyping approaches, researching unknowns, or building a proof-of-concept — for a time-boxed exploration with a go/no-go writeup, single-pass with optional reviewer and no RALF (a spike is exploration, not convergence). Not for development (use /develop) or decisions already made.
Use this skill when manually delegating from a workflow or building a custom orchestrator — a typed delegation helper that constructs a valid G7 spawn payload for a chosen role, validates against role-selection-table.md and the spawn-payload schema, and returns a fully-formed Agent(...) call ready for orchestrator use.
Use this skill when fixing a batch of issues from a code review or deep audit document — a multi-agent fix workflow that spawns developer(s), reviewer, QA via the Anthropic `Agent` tool with mandatory DEVELOP → REVIEW → QA pipeline. Auxiliary skill of /bugfix; not directly user-invoked.
Use this skill when running a full local QA cycle before commit or PR — a local dev testing workflow that verifies the test environment, provisions infrastructure (Docker, Testcontainers), runs a multi-level test suite (unit → integration → E2E), performs coverage analysis, and applies a quality gate. Applies the QA Engineer role.
Use this skill when the agent is designing schemas, reviewing migrations, tuning queries, modeling NoSQL access patterns, configuring replication, planning partitioning, auditing indexes, or onboarding to database conventions — the database patterns knowledge base for PostgreSQL, MySQL, SQL Server, MongoDB, Redis, Cassandra, DynamoDB engines, schema design, SQL optimization, indexing strategies (B-tree, hash, GIN, GiST, BRIN, partial, covering), partitioning (range, list, hash), replication topologies, NoSQL data modeling, time-series, graph, monitoring, capacity planning, backup and recovery.
Use this skill when authoring or running a Terraform plan/apply, refactoring modules, performing state surgery, importing existing cloud resources, or working in repos that use OpenTofu instead of HashiCorp Terraform — Terraform and OpenTofu procedures covering state operations (state list/show/mv/rm/pull/push), plan/apply lifecycle, OpenTofu fork compatibility, state-surgery safety warnings, and workspace handling. Loaded by `/infra-change` and other infrastructure workflows; not a workflow itself.
Use this skill when querying a production telemetry stack, when authoring or reviewing an analysis workflow that needs vendor query examples, or when identifying which stack is deployed from `CLAUDE.md`, helm charts, `prometheus-operator` CRDs, or an OTel collector config — a knowledge skill providing a production telemetry stack reference covering Prometheus + Grafana, Datadog, Honeycomb, New Relic, Sentry, and OpenTelemetry + Tempo / Jaeger, with per-stack ingestion model, query patterns, UI surface, and when each stack is the right choice. Loaded by `/analyze-prod`, `/analyze-local`, `/env-analyze`, `/infra-change`, and `/bugfix` workflows when production-context diagnosis needs vendor-specific query syntax.
Use this skill when authoring or auditing a multi-agent skill that spawns named subagents — shared protocols for multi-agent team coordination covering the execution model, named subagent spawning via the Agent tool, file conflict prevention, developer/reviewer/lead protocols, role selection table, and G7 spawn payload + return contract schemas. Referenced by multi-agent workflow skills (`/develop`, `/team-bugfix`, `/feature-design`). Not directly user-invoked.
Use this skill when iterating a task until a binary success condition holds (test passes, schema validates, output matches a regex) and a power user wants a standalone RALF (Read-Act-Learn-Feedback) iteration loop — to wrap the task in a feedback loop with a mechanically-verifiable oracle and a kill-on signal. Not for open-ended exploration — use /spike for that.
Use this skill when developing features or fixing bugs in isolated branches via git worktree — git worktree branch isolation for feature development and bugfixes, covering branch naming, worktree setup, branch verification, and safe cleanup.
Use this skill when designing interfaces, creating design systems, auditing accessibility, reviewing existing UI via browser screenshots, specifying components, or preparing design handoff for developers — covering UI/UX design systems, Figma workflows, accessibility checklists, browser-based visual audit with automated screenshots, component specification patterns, and design-to-code handoff procedures.
Use this skill when writing tests, designing test plans, setting up test infrastructure, or evaluating test quality — test strategy design, a test writing guide, and coverage targets, providing patterns for unit, integration, E2E, and API testing.
Use this skill when auditing, validating, or fixing an existing plugin skill's frontmatter, body length, progressive-disclosure structure, cross-references, or eval wiring — after editing skills under `plugin/skills/`, before merging a PR that touches them, or when a skill stops triggering as expected — the internal procedure for `/plugin-author audit`. Checks frontmatter against the agentskills.io specification plus ai-skills plugin conventions and optionally applies safe fixes. No longer slash-invocable — call `/plugin-author audit [<name> | --all] [--deep] [--strict] [--fix]` instead. Read by the `prompt-engineer` agent at task start when DEV-ing or reviewing a plugin skill (the safe-fix table and audit checks are the cached digest of upstream spec rules).
Use this skill when the agent is picking a release tool for a stack or running version bump + changelog generation inside the `/release` workflow — for the release tooling reference covering release-please, semantic-release, Changesets, GoReleaser, cargo-release, JReleaser, plus monorepo per-package versioning via Changesets, Lerna, and Nx, including detection markers, per-tool version-bump + changelog commands, and per-stack release conventions for Node, Go, Rust, and Java.
Use this skill when the agent is implementing Java backend services in Spring Boot projects, reviewing JPA entity models, configuring connection pools or caches, designing REST endpoints, writing repository/service/controller layers, or onboarding to Spring conventions — for Spring Boot 3 + JPA backend patterns covering Java 21+ features, layered architecture, Spring Data JPA, Hibernate fetch strategies, Flyway migrations, HikariCP pool tuning, Redis caching with Spring Cache, Spring Security 6 filter chains, REST API design (RFC 7807, pagination, versioning), Testcontainers + JUnit 5 + Mockito testing, Micrometer + Actuator observability, and virtual threads and performance tuning.
Use this skill when running a pre-release audit, compliance check, or threat modeling — to perform a full security scan of codebase and infrastructure (secrets, dependencies, auth, access control, data handling, cryptography, infra) with coverage against OWASP Top 10 (Web 2021) AND OWASP GenAI/LLM Top 10 (2025) per G3. Not for code-review security feedback (use /code-review) or pen testing (use professional service).
Use this skill when bootstrapping memory in a freshly cloned repo or when upgrading from a pre-memory plugin version — to initialize the .ai-skills-memory/ skeleton in the current project (directory structure, .gitignore rules, learnings.md template, .committed/ allowlist). Idempotent — safe to re-run on a project that already has memory wired.
Use this skill when bootstrapping scheduled knowledge-base sync for a repo that has no knowledge/.knowledge-sync.yml yet — to run one-time setup that detects the knowledge_root from CLAUDE.md/AGENTS.md, maps doc areas to source globs, records opt-in external sources (Linear/Notion/WebFetch, all disabled by default), captures a baseline last_scanned_sha, sets the per-area update policy, generates or seeds knowledge/CONVENTIONS.md, provisions the L4 memory dir, and offers to register the daily routine. Routes ongoing recurring sync operations to /knowledge-sync.
Use this skill when running the recurring (daily) knowledge-base rescan for a repo that already has knowledge/.knowledge-sync.yml — the main-thread dispatcher that reads the config, computes the git delta since last_scanned_sha, maps changed paths to affected doc areas, early-exits cheaply when nothing changed, then fans out one Agent(content-writer) per affected area, applies the propose/direct update policy, advances the baseline only on success, and writes an L4 run log — all with the G1 untrusted-content choke-point, secret-scan, deny-list, and budget controls woven in. For first-time setup use /knowledge-sync-init.
Use this skill when ready to submit code for review, opening a draft PR, or pushing a branch for review — to create a pull request by collecting the diff, generating a PR description with context, applying a checklist, and pushing, detecting .github/PULL_REQUEST_TEMPLATE.md + CODEOWNERS + stacked-PR tools (Graphite / Sapling / git-spice).
Use this skill when planning database schema changes with rollback, framework version upgrades, or library replacements — a planned migration with rollback plan across schema, library, version, and framework, run as a multi-agent pipeline with RALF on validation. Not for refactors without external contract change (use /refactor) or small bug-fix releases (use /develop).
Use this skill when the user asks to draft, write, or improve tweets, threads, LinkedIn posts, LinkedIn newsletter issues, Facebook posts, Threads posts, Bluesky posts, promote a blog post, share on social, or repurpose content for social channels — to create social media posts across X/Twitter, LinkedIn (posts + newsletters), Facebook, Threads, and Bluesky, reading brand context from marketing/MARKETING.md at runtime.
Use this skill when bootstrapping a target repository to be ai-skills-aware — on the first run of any ai-skills workflow in a fresh repo, when adopting the ai-skills plugin in an existing repo, or after upgrading to a plugin version that adds new memory paths or templates, including when the user does not say "init" but asks to "set up" or "onboard" the repo — to detect codebase type, create CLAUDE.md + AGENTS.md scaffolding, initialize the .ai-skills-memory/ directory tree from L1 templates, and configure .gitignore. Idempotent — safe to re-run. Accepts `--codebase-type <type>` and `--overwrite`. Not for re-initializing only memory — use `/memory-init` instead.
Create, modify, validate, and analyze Windsurf AI assets such as AGENTS.md files, Windsurf skills, templates, and supporting scripts. Use when building or maintaining the repository's Windsurf-specific AI component package.
Initialize AI context for an existing project — deep scan the codebase, detect tech stack, identify subprojects and major modules, generate root CLAUDE.md, ARCHITECTURE.md, TESTING.md, and directory-scoped CLAUDE.md files for subprojects and significant directories. Works with monorepos, polyglot projects, and large codebases via iterative scanning.
Quality assurance workflow for validating features, writing or improving tests, reporting bugs, and checking coverage or acceptance criteria.
Marketing operations knowledge base — MARKETING.md setup template, social media and email channel playbooks, trend research methodology, content calendar patterns, and recurring task definitions. Use when initializing marketing strategy, creating social media posts, drafting email campaigns, researching trends, or reviewing marketing analytics. Provides templates, platform-specific best practices, and quality checklists.
UI/UX design systems, Figma workflows, accessibility checklists, browser-based visual audit with automated screenshots, component specification patterns, and design-to-code handoff procedures. Use when designing interfaces, creating design systems, auditing accessibility, reviewing existing UI via browser screenshots, specifying components, or preparing design handoff for developers.
Develop a feature from documentation (PRD, ARD, design doc, implementation plan) with automatic role detection based on project tech stack
Documentation workflow — edit markdown docs, technical writing, blog content, release notes without touching source code. Applies Content Writer role. Includes SEO review branch for public-facing content.
Security scan workflow — dependency audit, OWASP checklist, secrets scan, vulnerability report. Applies software-engineer role with security focus. Use standalone or as part of code review.
Local dev testing workflow — verify test environment, provision infrastructure (Docker, Testcontainers), run multi-level test suite (unit → integration → E2E), coverage analysis, quality gate. Applies QA Engineer role. Use for full local QA cycle before commit or PR.
Context engineering knowledge base — context stack architecture (8-layer model), memory engineering (taxonomy, CRUD, conflict resolution, compression), agent harness patterns (init/continuation, state blobs, token budgeting, caching, failure modes), RAG pipeline design (chunking, packing, degradation signals), multi-agent orchestration (boundaries, payload schemas, fan-out patterns), production AI checklists (8 checklists), reference implementation templates (grounding envelope, untrusted wrapper, tool error envelope), privacy and compliance for AI systems. Use when designing AI agent systems, reviewing context quality, building RAG pipelines, implementing memory, planning multi-agent architectures, or preparing AI systems for production.
Develop a feature from documentation (PRD, ARD, design doc, implementation plan) with automatic role detection based on project tech stack
Marketing operations knowledge base — MARKETING.md setup template, social media and email channel playbooks, trend research methodology, content calendar patterns, and recurring task definitions. Use when initializing marketing strategy, creating social media posts, drafting email campaigns, researching trends, or reviewing marketing analytics. Provides templates, platform-specific best practices, and quality checklists.
Feature design workflow — transform raw feature inputs (ideas, research, stakeholder requests, competitive analysis) into a structured PRD or feature brief. Applies product-manager role. Produces PRD, user stories, acceptance criteria, success metrics, and updates FEATURES.md. First step in the planning chain, precedes /architecture and /feature-plan.
SEO review workflow — technical SEO audit, meta tags, structured data validation, Core Web Vitals, crawlability, indexability, AI search readiness. Applies SEO Engineer role. Use standalone or as part of feature/docs workflows.
Local dev testing workflow — verify test environment, provision infrastructure (Docker, Testcontainers), run multi-level test suite (unit → integration → E2E), coverage analysis, quality gate. Applies QA Engineer role. Use for full local QA cycle before commit or PR.
Test strategy design, test writing guide, and coverage targets. Use when writing tests, designing test plans, setting up test infrastructure, or evaluating test quality. Provides patterns for unit, integration, E2E, and API testing.
Git worktree branch isolation for feature development and bugfixes — branch naming, worktree setup, branch verification, and safe cleanup. Use when developing features or fixing bugs in isolated branches via git worktree.
Use when bootstrapping a target repository to be ai-assets-aware on first run in a new repo or when adopting the ai-assets plugin. Creates CLAUDE.md scaffolding, initializes .ai-assets-memory/ directory tree from L1 templates, configures .gitignore. Idempotent — safe to re-run.
Validate Claude Code AI assets for file size limits, frontmatter correctness, cross-reference integrity, runtime readiness, and naming conventions. Use after creating or modifying rules, skills, hooks, settings, or templates.
Deep analysis workflow for codebases, architectures, systems, products, markets, or research topics. Use when the user asks to analyze, investigate, assess, evaluate, compare, or research something in a structured way.
Run tests workflow — execute test suite, analyze failures, auto-fix obvious issues, re-run. Sub-workflow for /feature-dev, /bugfix, /pre-commit. Uses the `test-strategy` skill.
Architecture workflow — produce architectural documentation (ARD, design docs, API contracts, C4 diagrams, ARCHITECTURE.md updates) from a feature PRD, analysis request, or architectural initiative. Routes to solution-architect and system-architect roles based on scope. Input from product managers or direct analysis requests.
Structured code review with security, performance, and architecture checklists. Use when reviewing pull requests, code changes, or conducting architecture reviews. Provides actionable checklists for consistent review quality.
End-to-end bugfix workflow — analyze environment (local Docker or cloud production), collect evidence, prepare bug report, plan fix, apply appropriate engineer role, implement and verify the fix.
Analyze production environment — collect Kubernetes pod status, managed database health, logs, metrics, networking, and diagnose issues. Supports GCP, Azure, and AWS via the `cloud-platforms` skill. Applies SRE or DevOps roles. Use standalone or as part of production incident investigation.
Analyze production environment — collect Kubernetes pod status, managed database health, logs, metrics, networking, and diagnose issues. Supports GCP, Azure, and AWS via the `cloud-platforms` skill. Applies SRE or DevOps roles. Use standalone or as part of production incident investigation.
Create, modify, validate, and analyze Codex-native AI assets such as AGENTS.md files, shared skills, roles, rules, operations, templates, and checklists.
Create, modify, validate, and analyze Claude Code AI assets (rules, workflows, skills, hooks, CLAUDE.md) with prompt engineering discipline and dependency chain awareness
Analyze local Docker environment — collect container status, logs, networking, resource usage, and diagnose issues. Applies SRE or DevOps roles for investigation. Use standalone or as part of local environment bugfixing.
Architecture workflow — produce architectural documentation (ARD, design docs, API contracts, C4 diagrams, ARCHITECTURE.md updates) from a feature PRD, analysis request, or architectural initiative. Routes to solution-architect and system-architect roles based on scope. Input from product managers or direct analysis requests.
Blog post workflow — research topic, create content brief, write article, SEO optimize, quality review with feedback loop. Orchestrates product-manager, content-writer, and seo-engineer roles for public blog content creation and updates.
Validate Codex AI assets for size limits, frontmatter correctness, cross-reference integrity, runtime readiness, and parity safety after structural changes.
Develop a feature using a coordinated multi-agent team — developer(s), reviewer, QA, lead orchestrator with mandatory DEVELOP → REVIEW → QA pipeline. Multi-agent version of feature-dev.
Production deployment procedures with rollback plans, health checks, and verification steps. Use when deploying to production, planning rollback strategies, or verifying production health after deployment.
Production deployment procedures with rollback plans, health checks, and verification steps. Use when deploying to production, planning rollback strategies, or verifying production health after deployment.
End-to-end bugfix workflow — analyze environment (local Docker or cloud production), collect evidence, prepare bug report, plan fix, apply appropriate engineer role, implement and verify the fix.
Cloud platform reference modules — GCP, Azure, AWS. Provides platform-specific CLI commands, managed service patterns, networking, IAM, observability, and operational procedures. Activated when working with cloud infrastructure, production environments, managed databases, container orchestration, or cloud-native services. Detect the target platform from the project's `AGENTS.md` tech stack declaration.
Cloud platform reference modules — GCP, Azure, AWS. Provides platform-specific CLI commands, managed service patterns, networking, IAM, observability, and operational procedures. Activated when working with cloud infrastructure, production environments, managed databases, container orchestration, or cloud-native services. Detect the target platform from the project's CLAUDE.md tech stack declaration.
Structured code review with security, performance, and architecture checklists. Use when reviewing pull requests, code changes, or conducting architecture reviews. Provides actionable checklists for consistent review quality.
Content creation tools, AI content generators, image generation workflows, copywriting patterns, page content optimization, blog authoring patterns, content research workflows, and SEO content sync procedures. Use when creating page content, blog posts, generating visuals with AI tools, writing conversion copy, optimizing landing pages, integrating external content services, or synchronizing content across metadata and structured data. Provides tool guides, prompt templates, content brief templates, and quality checklists.
Create a pull request — collect diff, generate PR description with context, apply checklist, and push. Use when ready to submit code for review.
Create a pull request — collect diff, generate PR description with context, apply checklist, and push. Use when ready to submit code for review.
--- name: deploy-production description: Deploy to production workflow — final checks, approval gate, deploy, verify, rollback plan. Uses the `deployment-procedures` skill. Requires explicit APPROVE before any production mutation. disable-model-invocation: true argument-hint: [service-name] [version] --- # Deploy to Production Production deployment with mandatory approval gates, verification, and rollback plan. Every step that mutates production requires explicit user APPROVE. **⚠️ SAFETY: No
Deploy to staging workflow — build, deploy to staging environment, run smoke tests, verify. Use after feature completion before production deployment.
Deploy to staging workflow — build, deploy to staging environment, run smoke tests, verify. Use after feature completion before production deployment.
Documentation workflow — edit markdown docs, technical writing, blog content, release notes without touching source code. Applies Content Writer role. Includes SEO review branch for public-facing content.
Plan feature implementation across services and roles — parse requirements, understand project architecture (`ARCHITECTURE.md`, `AGENTS.md`), decompose into work packages per service/role, estimate complexity, produce an actionable implementation plan. Part of the umbrella feature workflow.
Remove signs of AI-generated writing from text. Use when editing, reviewing, or producing any public-facing text — blog posts, social media, UI copy, emails, landing pages, documentation. Detects and fixes AI patterns including inflated symbolism, promotional language, vague attributions, em dash overuse, rule of three, AI vocabulary, negative parallelisms, sycophantic tone, and filler phrases.
Remove signs of AI-generated writing from text. Use when editing, reviewing, or producing any public-facing text — blog posts, social media, UI copy, emails, landing pages, documentation. Detects and fixes AI patterns including inflated symbolism, promotional language, vague attributions, em dash overuse, rule of three, AI vocabulary, negative parallelisms, sycophantic tone, and filler phrases.
Infrastructure change workflow — Terraform plan/apply, Helm diff/upgrade, Kubernetes manifest changes with mandatory approval gates. Applies DevOps role. Safe-by-default with plan review before any mutation.
Infrastructure change workflow — Terraform plan/apply, Helm diff/upgrade, Kubernetes manifest changes with mandatory approval gates. Applies DevOps role. Safe-by-default with plan review before any mutation.
Marketing workflow — initialize marketing strategy with MARKETING.md, define channels and tactics, execute recurring marketing operations (social media, blog posts, email campaigns, trend research, analytics). Orchestrates marketing-strategist, product-manager, content-designer, content-writer, and seo-engineer roles. Owns marketing/ directory.
--- name: ml-pipeline description: ML pipeline orchestrator — single entry point for ML-related tasks. Coordinates ML Engineer (analysis, modeling, recommendations), SRE Engineer (production data extraction), and Product Manager (task formulation). Domain context from CLAUDE.md. MVP flow: define data requirements → extract from prod → analyze → model → recommend → feature plan. context: fork argument-hint: [analysis goal or dataset description] --- # ML Pipeline Single entry point for ML-relat
Pre-commit quality gate — lint, format, test, validate before committing. Runs automated checks and fixes issues. Use before git commit to ensure code quality.
Feature design workflow — transform raw feature inputs (ideas, research, stakeholder requests, competitive analysis) into a structured PRD or feature brief. Applies product-manager role. Produces PRD, user stories, acceptance criteria, success metrics, and updates FEATURES.md. First step in the planning chain, precedes /architecture and /feature-plan.
Prompt engineering knowledge base — technique taxonomy with decision tree, prompt template patterns and formatting conventions, OWASP LLM Top 10 security checklist, eval frameworks and testing guide, context engineering, structured output contracts, multi-agent orchestration patterns, cost optimization. Use when designing prompts, reviewing prompt quality, building AI features, creating AI assets, or auditing LLM security.
Prompt engineering knowledge base — technique taxonomy with decision tree, prompt template patterns and formatting conventions, OWASP LLM Top 10 security checklist, eval frameworks and testing guide, context engineering, structured output contracts, multi-agent orchestration patterns, cost optimization. Use when designing prompts, reviewing prompt quality, building AI features, creating AI assets, or auditing LLM security.
Quality assurance workflow for validating features, writing or improving tests, reporting bugs, and checking coverage or acceptance criteria.
Run tests workflow — execute test suite, analyze failures, auto-fix obvious issues, re-run. Follow-up skill for `feature-dev`, `bugfix`, and `pre-commit`. Uses the `test-strategy` skill.
Security scan workflow — dependency audit, OWASP checklist, secrets scan, vulnerability report. Applies software-engineer role with security focus. Use standalone or as part of code review.
Create social media posts across X/Twitter, LinkedIn, and Facebook for any product or project. Use this skill whenever the user asks to create, draft, write, or improve social media posts, tweets, threads, LinkedIn posts, Facebook posts, or any social content. Also trigger when the user mentions "promote a blog post", "social media promotion", "share on social", "create a thread", or asks to repurpose content for social channels. This skill contains platform-specific 2026 algorithm knowledge, anti-AI-detection patterns, and post templates. Reads brand context from marketing/MARKETING.md at runtime.
Create social media posts across X/Twitter, LinkedIn, and Facebook for any product or project. Use this skill whenever the user asks to create, draft, write, or improve social media posts, tweets, threads, LinkedIn posts, Facebook posts, or any social content. Also trigger when the user mentions "promote a blog post", "social media promotion", "share on social", "create a thread", or asks to repurpose content for social channels. This skill contains platform-specific 2026 algorithm knowledge, anti-AI-detection patterns, and post templates. Reads brand context from marketing/MARKETING.md at runtime.
Test strategy design, test writing guide, and coverage targets. Use when writing tests, designing test plans, setting up test infrastructure, or evaluating test quality. Provides patterns for unit, integration, E2E, and API testing.
UI/UX design systems, Figma workflows, accessibility checklists, browser-based visual audit with automated screenshots, component specification patterns, and design-to-code handoff procedures. Use when designing interfaces, creating design systems, auditing accessibility, reviewing existing UI via browser screenshots, specifying components, or preparing design handoff for developers.
Analyze local Docker environment — collect container status, logs, networking, resource usage, and diagnose issues. Applies SRE or DevOps roles for investigation. Use standalone or as part of local environment bugfixing.
Deep analysis workflow for codebases, architectures, systems, products, markets, or research topics. Use when the user asks to analyze, investigate, assess, evaluate, compare, or research something in a structured way.
Context engineering knowledge base — context stack architecture (8-layer model), memory engineering (taxonomy, CRUD, conflict resolution, compression), agent harness patterns (init/continuation, state blobs, token budgeting, caching, failure modes), RAG pipeline design (chunking, packing, degradation signals), multi-agent orchestration (boundaries, payload schemas, fan-out patterns), production AI checklists (8 checklists), reference implementation templates (grounding envelope, untrusted wrapper, tool error envelope), privacy and compliance for AI systems. Use when designing AI agent systems, reviewing context quality, building RAG pipelines, implementing memory, planning multi-agent architectures, or preparing AI systems for production.
--- name: ml-pipeline description: ML pipeline orchestrator — single entry point for ML-related tasks. Coordinates ML Engineer (analysis, modeling, recommendations), SRE Engineer (production data extraction), and Product Manager (task formulation). Domain context from `AGENTS.md`. MVP flow: define data requirements → extract from prod → analyze → model → recommend → feature plan. context: fork argument-hint: [analysis goal or dataset description] codex-roles: - product-manager - ml-engineer
Initialize AI context for an existing project — deep scan the codebase, detect tech stack, identify subprojects and major modules, generate root `AGENTS.md`, `ARCHITECTURE.md`, `TESTING.md`, and directory-scoped `AGENTS.md` files for subprojects and significant directories. Works with monorepos, polyglot projects, and large codebases via iterative scanning.
Release workflow — version bump, changelog generation, git tag, release notes. Use when preparing a new version for deployment.
SEO review workflow — technical SEO audit, meta tags, structured data validation, Core Web Vitals, crawlability, indexability, AI search readiness. Applies SEO Engineer role. Use standalone or as part of feature/docs workflows.
Git worktree branch isolation for feature development and bugfixes — branch naming, worktree setup, branch verification, and safe cleanup. Use when developing features or fixing bugs in isolated branches via git worktree.
Content creation tools, AI content generators, image generation workflows, copywriting patterns, page content optimization, blog authoring patterns, content research workflows, and SEO content sync procedures. Use when creating page content, blog posts, generating visuals with AI tools, writing conversion copy, optimizing landing pages, integrating external content services, or synchronizing content across metadata and structured data. Provides tool guides, prompt templates, content brief templates, and quality checklists.
--- name: deploy-production description: Deploy to production workflow — final checks, approval gate, deploy, verify, rollback plan. Uses the `deployment-procedures` skill. Requires explicit APPROVE before any production mutation. disable-model-invocation: true argument-hint: [service-name] [version] --- # Deploy to Production Production deployment with mandatory approval gates, verification, and rollback plan. Every step that mutates production requires explicit user APPROVE. **⚠️ SAFETY: No
Plan feature implementation across services and roles — parse requirements, understand project architecture (ARCHITECTURE.md, CLAUDE.md), decompose into work packages per service/role, estimate complexity, produce an actionable implementation plan. Part of the umbrella feature workflow.
Marketing workflow — initialize marketing strategy with MARKETING.md, define channels and tactics, execute recurring marketing operations (social media, blog posts, email campaigns, trend research, analytics). Orchestrates marketing-strategist, product-manager, content-designer, content-writer, and seo-engineer roles. Owns marketing/ directory.
Pre-commit quality gate — lint, format, test, validate before committing. Runs automated checks and fixes issues. Use before git commit to ensure code quality.
Release workflow — version bump, changelog generation, git tag, release notes. Use when preparing a new version for deployment.
Fix issues from a code review or deep audit document using a coordinated multi-agent team — developer(s), reviewer, QA, lead orchestrator with mandatory DEVELOP → REVIEW → QA pipeline
Shared protocols for multi-agent team coordination — execution model, named subagent spawning, file conflict prevention, developer/reviewer/lead protocols, role selection table. Referenced by multi-agent workflow skills.
Blog post workflow — research topic, create content brief, write article, SEO optimize, quality review with feedback loop. Orchestrates product-manager, content-writer, and seo-engineer roles for public blog content creation and updates.