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By AppsVortex
Automate end-to-end infrastructure workflows: zero-config project setup, IaC generation with Terraform/Pulumi/CDK, safe deployments to AWS/GCP/Azure with gates, post-deploy verification, migrations, CI/CD pipelines, monitoring, and cost/security audits in structured pipelines.
npx claudepluginhub appsvortex/arness --plugin arn-infraThis agent should be used when a skill needs to generate a phased infrastructure implementation plan from a change specification. It reasons about provisioning dependencies, blast radius classification, rollback checkpoint placement, environment promotion strategy, cost budgeting per phase, and parallel execution opportunities. It produces plans structured in infrastructure terms, not application development terms. <example> Context: Invoked by arn-infra-change-plan to generate an initial plan from a change spec user: "plan infra change migrate-database-managed" assistant: (invokes arn-infra-change-planner with the INFRA_CHANGE spec, provider config, and environment config) </example> <example> Context: Invoked by arn-infra-change-plan to revise a plan based on user feedback user: "combine phases 2 and 3, and add a rollback checkpoint before the DNS cutover" assistant: (resumes or re-invokes arn-infra-change-planner with user feedback and the current plan) </example> <example> Context: Invoked by arn-infra-change-plan to plan infrastructure teardown or cleanup user: "plan infra change decommission-legacy-api" assistant: (invokes arn-infra-change-planner with the INFRA_CHANGE spec for resource destruction, dependency-reverse ordering, and data backup checkpoints) </example>
This agent should be used when the arn-infra-review-change skill or arn-infra-execute-change skill needs a structured quality review of completed infrastructure changes. It evaluates security posture, cost compliance, blast radius adherence, rollback documentation, environment parity, state consistency, and resource tagging. <example> Context: Post-execution review of a completed multi-phase infrastructure change user: "review infra change" assistant: (invokes arn-infra-change-reviewer with all phase reports, spec, and plan) </example> <example> Context: Mid-execution phase review gate during arn-infra-execute-change user: (automatic invocation after phase deployment and verification) assistant: (invokes arn-infra-change-reviewer with single phase report for gate check) </example> <example> Context: User wants to re-review a previously completed infrastructure change user: "re-review infra change for the migration project" assistant: (invokes arn-infra-change-reviewer with existing reports for fresh review) </example>
This agent should be used when infrastructure changes need cost estimation before deployment, when the user wants to understand the cost impact of their current or planned infrastructure, or when spend thresholds need enforcement. It integrates with Infracost when available and provides usage-based pricing estimates with clear caveats. <example> Context: Invoked by arn-infra-deploy before Level 3+ deployment operations user: "deploy to staging" assistant: (invokes arn-infra-cost-analyst to estimate cost impact before proceeding) </example> <example> Context: User asks for a cost estimate of their current infrastructure plan user: "how much will this infrastructure cost per month?" assistant: (invokes arn-infra-cost-analyst with the IaC files and provider context) </example> <example> Context: Invoked during infrastructure assessment to estimate total deployment cost user: "assess my app's infrastructure needs" assistant: (invokes arn-infra-cost-analyst with the proposed resource list) </example>
This agent should be used when the arn-infra-report skill needs diagnostic investigation of an Arness Infra workflow issue. Analyzes infrastructure configuration, directory structure, and skill behavior against expected patterns documented in the infra knowledge base. Reports only Infra-specific issues — never reads or reports user project code or business logic. <example> Context: Invoked by arn-infra-report skill during investigation phase user: "infra report" assistant: (invokes arn-infra-doctor with user description + config context) </example> <example> Context: User reports deployment failure user: "deployment to staging keeps failing with permission denied" assistant: (invokes arn-infra-doctor to check cloud CLI auth, IAM, provider config, deploy artifacts) </example> <example> Context: User reports containerization issue user: "arn-infra-containerize generated a Dockerfile that ignores my multi-stage setup" assistant: (invokes arn-infra-doctor to check Project topology, Application path, Dockerfile patterns reference) </example> <example> Context: User reports execute-change phase failure user: "execute-change failed on phase 2 and didn't create a rollback checkpoint" assistant: (invokes arn-infra-doctor to check PROGRESS_TRACKER.json, phase reports, rollback artifacts, plan structure) </example>
This agent should be used when CI/CD pipeline configurations need to be generated or extended for infrastructure deployment. It produces GitHub Actions workflows, GitLab CI configs, and other pipeline definitions. It detects existing CI/CD setups and extends them rather than replacing them. <example> Context: Invoked by arn-infra-pipeline to generate CI/CD pipeline configs user: "set up deployment pipelines" assistant: (invokes arn-infra-pipeline-builder with provider context, environment config, and existing CI detection) </example> <example> Context: User asks to add infrastructure deployment to their existing CI/CD user: "add terraform plan and apply steps to my GitHub Actions workflow" assistant: (invokes arn-infra-pipeline-builder with existing workflow files and IaC context) </example> <example> Context: Invoked to generate environment-specific pipeline stages user: "create a staging deployment pipeline" assistant: (invokes arn-infra-pipeline-builder with environment config and deployment strategy) </example>
This skill should be used when the user says "assess infrastructure", "infra assess", "arn infra assess", "infrastructure assessment", "analyze infrastructure needs", "what infrastructure do I need", "infrastructure audit", "app infrastructure assessment", "full infra assessment", "infrastructure review", "assess my app", "un-defer infrastructure", "infra backlog", or wants a comprehensive analysis of their application's infrastructure needs, including processing deferred infrastructure backlogs, to produce a prioritized infrastructure backlog published as issues.
This skill should be used when the user says "plan infra change", "arn-infra-change-plan", "infra change plan", "plan this infrastructure change", "create infra plan", "arn infra plan", "plan the infra spec", "infrastructure implementation plan", "infra plan from spec", "generate infra plan", "plan infrastructure", or wants to generate a phased implementation plan from an infrastructure change specification, structured by provisioning order, blast radius, rollback checkpoints, and environment promotion gates.
This skill should be used when the user says "infra change spec", "infrastructure change", "specify infrastructure change", "change spec", "arn infra spec", "describe infra change", "what infrastructure needs to change", "infra spec", "spec this infra change", "write infra spec", "infrastructure change specification", "create infra change spec", "upgrade to pipeline", "convert to pipeline", or wants to iteratively develop an infrastructure change idea into a well-formed specification through guided conversation, or wants to upgrade existing interactive IaC artifacts into the structured change pipeline.
This skill should be used when the user says "cleanup", "infra cleanup", "arn infra cleanup", "clean up resources", "destroy expired resources", "check ttl", "check expired", "ttl cleanup", "remove old deployments", "destroy dev environment", "tear down", "teardown infra", "destroy resources", "cleanup ephemeral", "check for expired resources", "clean up infra", "resource cleanup", "destroy old resources", "prune resources", "delete expired deployments", "decommission", or wants to check for and destroy expired ephemeral infrastructure resources. This skill also supports periodic monitoring via `/loop 6h /arn-infra-cleanup` for session-duration TTL enforcement.
This skill should be used when the user says "containerize", "dockerize", "create dockerfile", "docker setup", "container setup", "arn infra containerize", "infra containerize", "generate docker", "docker compose", "compose setup", "containerize my app", "docker configuration", "create docker-compose", "multi-stage docker", "container config", "dockerize my application", "infra docker", "set up containers", or wants to generate Dockerfiles, docker-compose configurations, and .dockerignore files for their application with security auditing and multi-stage build best practices.
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Infrastructure management for AWS, Kubernetes, Docker, Helm, Fly.io
Deployment engineering with Terraform and infrastructure as code
Enhances web_search Skill by researching best practices and deploying infrastructure automatically
Cloud infrastructure agents — cloud, container, SRE specialists
DevOps, cloud, and deployment specialists - Kubernetes, Terraform, AWS, Azure, GCP, and SRE
Complete infrastructure lifecycle management from IaC to deployment. Master AWS, Terraform, CI/CD pipelines, GitOps workflows, and deployment automation for both home projects and enterprise systems.
Greenfield project skills with progressive zero-config init — auto-configures with sensible defaults on first skill invocation, no upfront ceremony required. Includes project initialization, product discovery with AI-assisted persona generation and competitive landscape research, brand naming exploration through a structured 4-step methodology (strategic foundation, creative generation, qualitative filtering, due diligence with domain and trademark screening), architecture vision, use case authoring, team-based use case debate, project scaffolding, visual sketch exploration for iterative visual direction proposals, development environment setup, risk validation, visual style exploration with visual grounding, static visual validation, team-based static visual validation with expert debate, interactive prototype validation, team-based interactive prototype validation with expert debate, prototype preservation and locking, feature extraction, visual testing strategy with layered validation, visual readiness checkpoint for deferred layer activation, issue tracker integration for feature upload, stress testing suite (synthetic user interviews, competitive gap analysis, pre-mortem investigation, PR/FAQ), concept review with user-approved updates, and a guided wizard that walks through the entire greenfield pipeline with decision gates. Parallelized expert reviews and phase-parallel stress interviews for faster validation cycles.
Development toolkit for Claude Code — plan, implement, ship, review, and assess features with AI-assisted workflows. Progressive zero-config init: auto-configures with sensible defaults on first skill invocation, no upfront ceremony required. Three-tier ceremony model: swift (lightweight), standard (mid-ceremony spec-plan-execute), and thorough (full pipeline) with severity-aware scope routing. Five entry points: arn-planning (scope router, spec, plan), arn-implementing (execute plans, swift, or standard changes), arn-shipping (commit, push, PR), arn-reviewing-pr (PR feedback), arn-assessing (codebase health). Includes arn-code-sketch for UI preview, arn-code-swift for quick implementations, and arn-code-standard for mid-ceremony changes. Includes arn-code-catch-up for retroactive documentation of out-of-pipeline commits. Pipeline preference persistence for streamlined repeat sessions. Batch pipeline: arn-code-batch-planning (multi-feature planning), arn-code-batch-implement (parallel worktree execution), arn-code-batch-merge (conflict-aware merge), arn-code-batch-simplify (cross-feature quality).

Arness — H not required.
Structured AI workflows for Claude Code. From first idea to production deploy.
Seven entry commands. That's all you need to remember. Behind them, 134 specialist skills and agents handle the details across three independent plugins — ideation, development, and infrastructure.
Most AI coding tools help you write code faster. Arness helps you build software better. It gives your Claude Code session a structured pipeline: specs before code, plans before execution, reviews before shipping. Every stage produces a human-readable artifact that feeds the next. Nothing is hidden, nothing is locked in.

Most projects fail before the first commit — wrong problem, wrong audience, wrong architecture. Spark takes a raw idea and puts it through product discovery, stress testing, brand naming, use case writing, architecture evaluation, and interactive prototyping. By the time you write real code, you have a validated concept, a prioritized feature backlog, and a scaffolded codebase ready for development.
Start here: /arn-brainstorming | Full guide

The development pipeline that treats AI-assisted coding like engineering, not guesswork. Every feature flows through spec, plan, structure, execute, review, and ship — with three ceremony tiers (swift, standard, thorough) that scale process to match scope. A one-file fix gets a lightweight pass. A cross-cutting refactor gets phases, task dependencies, and quality gates.
Start here: /arn-planning | Full guide

A guided approach to containerization, IaC, deployment, and monitoring. Arness Infra audits your toolchain, generates Dockerfiles and IaC, configures environments and secrets, builds CI/CD pipelines, and walks you through deployment and verification. For complex changes, a structured change management pipeline mirrors the rigor of the development pipeline. Infra is experimental — the newest and least mature of the three plugins. Always review generated infrastructure before applying it. Feedback and suggestions are welcome.
Start here: /arn-infra-wizard | Full guide
Arness asks you questions once and remembers. Your role, tech stack, preferred frameworks, and development conventions are captured on first run and reused across every session and every project. No repeated configuration.
Every skill produces a Markdown or JSON artifact — specs, plans, task lists, review reports, PR descriptions — stored in .arness/ at your project root. Each artifact feeds the next stage in the pipeline, creating a traceable chain from initial idea to merged PR. You can read, edit, or version-control any of it.
Three ceremony tiers — swift, standard, and thorough — match process to scope automatically. A bug fix touching two files does not get the same pipeline as a new authentication system. Arness detects complexity and routes accordingly.
| Plain text, open source | All artifacts are Markdown and JSON. MIT licensed. Read, edit, or delete anything. |
| Learns your preferences | Captures your stack, role, and conventions once. Every session uses them. |
| Artifact-chain traceability | Spec feeds plan feeds tasks feeds code feeds review feeds PR. Nothing is orphaned. |
| Graduated ceremony | Swift (1-8 files), Standard (medium scope), Thorough (complex) — process scales to match the work. |
| Clean project structure | Everything lives in .arness/. Your source tree stays yours. |
| No vendor lock-in | Works with GitHub, Bitbucket, Jira, Figma, and Canva. Remove Arness and your code is untouched. |
# Add the Arness marketplace (one-time)
/plugin marketplace add AppsVortex/arness
# Install the plugins you need
/plugin install arn-spark@arn-marketplace # New product from scratch
/plugin install arn-code@arn-marketplace # Development pipeline
/plugin install arn-infra@arn-marketplace # Infrastructure & deployment
Then run the entry point that matches what you want to do — Arness auto-configures on first use:
/arn-brainstorming New product — discover, validate, prototype, extract features
/arn-planning Plan a feature or fix from scratch
/arn-implementing Pick up where you left off
/arn-shipping Commit, push, open a PR
/arn-reviewing-pr Handle PR feedback
/arn-assessing Deep-dive codebase review
/arn-infra-wizard Infrastructure end-to-end