/arn-spark-scaffold
This skill should be used when the user says "scaffold", "arn scaffold", "set up the project", "create project", "initialize project", "bootstrap project", "create the skeleton", "install dependencies", "configure the project", or wants to create a working project skeleton from
$ npx -y skills add AppsVortex/arness --skill arn-spark-scaffold --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/arn-spark-scaffold
Context preview
The summary Claude sees to decide when to auto-load this skill.
This skill should be used when the user says "scaffold", "arn scaffold", "set up the project", "create project", "initialize project", "bootstrap project", "create the skeleton", "install dependencies", "configure the project", or wants to create a working project skeleton from
SKILL.md
arn-spark-scaffold.SKILL.mdname: arn-spark-scaffold
description: >-
This skill should be used when the user says "scaffold", "arn scaffold",
"set up the project", "create project", "initialize project",
"bootstrap project", "create the skeleton",
"install dependencies", "configure the project", or wants to create a working
project skeleton from architecture decisions with installed dependencies,
configured build tools, and a UI toolkit ready for development.
version: 1.0.0
Arness Scaffold
Set up a working project skeleton from architecture vision decisions through guided conversation, aided by the `arn-spark-scaffolder` agent for project creation and optionally the `arn-spark-tech-evaluator` agent for UI toolkit comparisons. This is a conversational skill that runs in normal conversation (NOT plan mode). The primary artifact is a **buildable project** with all dependencies installed and configured.
This skill covers the initial project setup: framework scaffolding, dependency installation, build configuration, linting, and UI toolkit setup. It does not implement features, create screens, or write application logic -- those are handled by subsequent skills (`/arn-spark-spike`, `/arn-spark-style-explore`, `/arn-spark-static-prototype`, `/arn-spark-clickable-prototype`).
Prerequisites
An architecture vision document must exist. Check in order:
1. Read the project's `CLAUDE.md` for a `## Arness` section. If found, check the configured Vision directory for `architecture-vision.md` 2. If no `## Arness` section exists or Arness Spark fields are missing, inform the user: "Arness Spark is not configured for this project yet. Run `/arn-brainstorming` to get started — it will set everything up automatically." Do not proceed without it. 3. If `## Arness` exists but no architecture vision found, check `.arness/vision/architecture-vision.md` at the project root
**If an architecture vision is found:** Read it and proceed to Step 1.
**If no architecture vision is found:** Inform the user:
"No architecture vision document found. I recommend running `/arn-spark-arch-vision` first to define your technology stack. The scaffold needs to know which frameworks, build tools, and libraries to set up."
Do not proceed without an architecture vision or explicit technology stack from the user.
Determine the project root: 1. The project root is the working directory unless the user specifies otherwise 2. If the project root already contains a `package.json`, `Cargo.toml`, or similar, warn the user: "This directory already has a project manifest. Do you want to extend the existing project or start fresh in a subdirectory?"
Workflow
Step 1: Load Architecture Vision, Product Pillars, and Extract Stack Decisions
Read the architecture vision document. Extract technology decisions for each layer:
- **Application framework:** e.g., Tauri, Electron, plain web
- **UI framework:** e.g., Svelte, React, Vue
- **Language:** e.g., TypeScript, JavaScript
- **Build tool:** e.g., Vite, webpack
- **Package manager:** e.g., npm, pnpm, yarn, bun
- **Test framework:** e.g., Vitest, Jest, Playwright
- **Linter/formatter:** e.g., ESLint + Prettier, Biome
Also load the product concept document (same Vision directory) and extract the **Product Pillars** section if it exists. Pillars guide UI toolkit decisions in Step 2 — for example, a "design fidelity" pillar means the component library must allow full visual customization, while a "simplicity" pillar favors pre-styled components with minimal configuration.
Present the extracted stack and relevant pillars to the user:
"Based on your architecture vision, here is the stack I will scaffold:
| Layer | Technology | |-------|-----------| | Framework | [value] | | UI | [value] | | Build | [value] | | ... | ... |
[If pillars found:] Your product pillars that will guide UI toolkit choices:
- **[Pillar]:** [what it implies for CSS/component library decisions]
- ...
Ask (using `AskUserQuestion`):
**"Does this stack look right?"**
Options: 1. **Yes, proceed** — Set up the project with this stack 2. **Adjust** — I want to change something before proceeding"
Step 2: UI Toolkit Decisions
The architecture vision defines the high-level UI framework (e.g., Svelte) but typically does not specify the CSS approach and component library. These decisions happen now because they affect the scaffold setup and all subsequent work (style exploration, prototyping, production code).
When product pillars are available, annotate each option with how it serves or challenges the pillars. This helps the user make an informed choice aligned with their product's non-negotiable qualities.
**Profile-aware recommendations:** Read user profile. Check `.claude/arness-profile.local.md` first (project override takes precedence), then `~/.arness/user-profile.yaml`. Also check `.arness/preferences.yaml` for project-level team preferences. If the user lists specific frameworks (e.g., "React"), suggest compatible component libraries the user likely knows. If `development_experience: learning`, favor pre-styled libraries (DaisyUI, Chakra) over headless ones (shadcn, Radix) -- pre-styled libraries have less configuration overhead and visible results faster. If `development_experience: non-technical`, favor the most mainstream option with the largest community and most tutorials. Apply the advisory pattern for all toolkit recommendations: present the technically optimal recommendation first, then a preference-aligned alternative with pros/cons if they differ.
Ask the user about each:
**CSS approach:**
Ask (using `AskUserQuestion`):
**"Which CSS framework should we use?"**
Offer common options for the chosen UI framework, noting pillar alignment where relevant. For example, for Svelte with a "design fidelity" pillar:
Options: 1. **Tailwind CSS** (most popular, utility-first) — Supports design fidelity: fine-grained control over every visual detail 2. **UnoCSS** (similar to Tailwind, faster build) —
Read more
name: arn-spark-scaffold description: >- This skill should be used when the user says "scaffold", "arn scaffold", "set up the project", "create project", "initialize project", "bootstrap project", "create the skeleton", "install dependencies", "configure the project", or wants to create a working project skeleton from architecture decisions with installed dependencies, configured build tools, and a UI toolkit ready for development. version: 1.0.0
Arness Scaffold
Set up a working project skeleton from architecture vision decisions through guided conversation, aided by the `arn-spark-scaffolder` agent for project creation and optionally the `arn-spark-tech-evaluator` agent for UI toolkit comparisons. This is a conversational skill that runs in normal conversation (NOT plan mode). The primary artifact is a **buildable project** with all dependencies installed and configured.
This skill covers the initial project setup: framework scaffolding, dependency installation, build configuration, linting, and UI toolkit setup. It does not implement features, create screens, or write application logic -- those are handled by subsequent skills (`/arn-spark-spike`, `/arn-spark-style-explore`, `/arn-spark-static-prototype`, `/arn-spark-clickable-prototype`).
Prerequisites
An architecture vision document must exist. Check in order:
1. Read the project's `CLAUDE.md` for a `## Arness` section. If found, check the configured Vision directory for `architecture-vision.md` 2. If no `## Arness` section exists or Arness Spark fields are missing, inform the user: "Arness Spark is not configured for this project yet. Run `/arn-brainstorming` to get started — it will set everything up automatically." Do not proceed without it. 3. If `## Arness` exists but no architecture vision found, check `.arness/vision/architecture-vision.md` at the project root
**If an architecture vision is found:** Read it and proceed to Step 1.
**If no architecture vision is found:** Inform the user:
"No architecture vision document found. I recommend running `/arn-spark-arch-vision` first to define your technology stack. The scaffold needs to know which frameworks, build tools, and libraries to set up."
Do not proceed without an architecture vision or explicit technology stack from the user.
Determine the project root: 1. The project root is the working directory unless the user specifies otherwise 2. If the project root already contains a `package.json`, `Cargo.toml`, or similar, warn the user: "This directory already has a project manifest. Do you want to extend the existing project or start fresh in a subdirectory?"
Workflow
Step 1: Load Architecture Vision, Product Pillars, and Extract Stack Decisions
Read the architecture vision document. Extract technology decisions for each layer:
- **Application framework:** e.g., Tauri, Electron, plain web
- **UI framework:** e.g., Svelte, React, Vue
- **Language:** e.g., TypeScript, JavaScript
- **Build tool:** e.g., Vite, webpack
- **Package manager:** e.g., npm, pnpm, yarn, bun
- **Test framework:** e.g., Vitest, Jest, Playwright
- **Linter/formatter:** e.g., ESLint + Prettier, Biome
Also load the product concept document (same Vision directory) and extract the **Product Pillars** section if it exists. Pillars guide UI toolkit decisions in Step 2 — for example, a "design fidelity" pillar means the component library must allow full visual customization, while a "simplicity" pillar favors pre-styled components with minimal configuration.
Present the extracted stack and relevant pillars to the user:
"Based on your architecture vision, here is the stack I will scaffold:
| Layer | Technology | |-------|-----------| | Framework | [value] | | UI | [value] | | Build | [value] | | ... | ... |
[If pillars found:] Your product pillars that will guide UI toolkit choices:
- **[Pillar]:** [what it implies for CSS/component library decisions]
- ...
Ask (using `AskUserQuestion`):
**"Does this stack look right?"**
Options: 1. **Yes, proceed** — Set up the project with this stack 2. **Adjust** — I want to change something before proceeding"
Step 2: UI Toolkit Decisions
The architecture vision defines the high-level UI framework (e.g., Svelte) but typically does not specify the CSS approach and component library. These decisions happen now because they affect the scaffold setup and all subsequent work (style exploration, prototyping, production code).
When product pillars are available, annotate each option with how it serves or challenges the pillars. This helps the user make an informed choice aligned with their product's non-negotiable qualities.
**Profile-aware recommendations:** Read user profile. Check `.claude/arness-profile.local.md` first (project override takes precedence), then `~/.arness/user-profile.yaml`. Also check `.arness/preferences.yaml` for project-level team preferences. If the user lists specific frameworks (e.g., "React"), suggest compatible component libraries the user likely knows. If `development_experience: learning`, favor pre-styled libraries (DaisyUI, Chakra) over headless ones (shadcn, Radix) -- pre-styled libraries have less configuration overhead and visible results faster. If `development_experience: non-technical`, favor the most mainstream option with the largest community and most tutorials. Apply the advisory pattern for all toolkit recommendations: present the technically optimal recommendation first, then a preference-aligned alternative with pros/cons if they differ.
Ask the user about each:
**CSS approach:**
Ask (using `AskUserQuestion`):
**"Which CSS framework should we use?"**
Offer common options for the chosen UI framework, noting pillar alignment where relevant. For example, for Svelte with a "design fidelity" pillar:
Options: 1. **Tailwind CSS** (most popular, utility-first) — Supports design fidelity: fine-grained control over every visual detail 2. **UnoCSS** (similar to Tailwind, faster build) —
Showing the first part of this file.
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.
Repo: AppsVortex/arness
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Open skill - /arn-code-assess
This skill should be used when the user says "arness code assess", "arn-code-assess", "assess codebase", "technical review", "codebase assessment", "find improvements", "what should I improve", "tech debt review", "tech debt audit", "pattern compliance check", "codebase health
Open skill - /arn-code-batch-cve-fix
This skill should be used when the user says "fix CVEs", "patch vulnerabilities", "apply security patches", "resolve security advisories", "batch CVE fix", "patch dependencies", "fix security findings", "remediate CVEs", "apply CVE fixes", "batch fix vulnerabilities", "resolve
Open skill - /arn-code-batch-cve-scan
This skill should be used when the user says "scan for CVEs", "CVE scan", "check for vulnerabilities", "find vulnerabilities", "check security advisories", "dependabot triage", "dependabot scan", "scan dependencies for security issues", "audit dependencies", "vulnerability
Open skill - /arn-code-batch-implement
This skill should be used when the user says "batch implement", "implement all", "batch execution", "implement all features", "parallel implement", "implement in parallel", "arness batch implement", "arn-code-batch-implement", "run batch implementation", "implement everything",
Open skill - /arn-code-batch-merge
This skill should be used when the user says "batch merge", "merge batch", "arness batch merge", "arn-code-batch-merge", "merge all PRs", "merge batch PRs", "merge the batch", "merge implemented features", "batch merge PRs", "merge open PRs", "merge all feature PRs", "combine
Open skill

