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Al Build

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javiarmesto
al-build

Build, package, and deploy AL extensions to Business Central environments. ALDC workflow (Copilot prompt al-build); invoke explicitly.

Overview

Publisherjaviarmesto
RepositoryALDC-AL-Development-Collection
Skill nameal-build
Stars
115
Forks
37
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by javiarmesto on GitHub. Read the source before you install it.

Installation

Install the Al Build AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/javiarmesto/ALDC-AL-Development-Collection/tree/main/.claude/skills/al-build
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/javiarmesto/ALDC-AL-Development-Collection.git /tmp/ALDC-AL-Development-Collection
mkdir -p .claude/skills
cp -r /tmp/ALDC-AL-Development-Collection/.claude/skills/al-build .claude/skills/al-build
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Al Build in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Al Build on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Al Build is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Claude Code adapter — generated by scripts/sync-plugin-support.js, do not edit. Canonical source: prompts/al-build.prompt.md (sha256 126f6ff7fda0215517ead6a1a0c9996d8181c5117abdef8f52c269724284989d). This workflow was written for @workspace use … in GitHub Copilot Chat. Apply these harness mappings and treat everything else as binding:

Copilot surface in the contractClaude Code equivalent
#runSubagent / runSubagent / agents:The Task tool with subagent_type: "aldc:<agent>"; one invocation carries everything inline (subagents are stateless)
#askQuestions / vscode/askQuestionsAskUserQuestion (main session only; a subagent returns its questions in its structured output)
handoffs: entries, and send: false on oneNo equivalent — the gate is yours to keep. In Copilot a handoff is a button the human clicks, and send: false additionally hands them the prompt to review before it is sent; the host supplies the approval. Claude Code delegates through the Task tool, with no click and no review step. So never auto-delegate: present your output, get explicit approval, and only then delegate or route to another role. The handoff's routing is carried by the plugin's role entry skills; its human gate is carried by this rule
@al-architect, @al-spec-agent, @al-conductor, @al-developer, @al-developer-reviewer, @al-triage, @al-presales, @al-agent-builder, @dredd/aldc:architect, /aldc:spec, /aldc:conduct, /aldc:develop, /aldc:review, /aldc:triage, /aldc:presales, /aldc:agent-builder, /aldc:audit (or claude --agent aldc:<agent>)
@workspace use <workflow>/aldc:<workflow> with dots replaced by dashes (al-spec.create -> /aldc:al-spec-create); already rewritten below
${input:Name} prompt variablesTake the value from $ARGUMENTS; ask through AskUserQuestion when missing. Already rewritten below as <Name from $ARGUMENTS>
execute / runInTerminal, AL compilation or symbol restoreDeveloper/Implementer: discovered official AL MCP mcp__al__al_compile / mcp__al__al_build / mcp__al__al_downloadsymbols, or an installed ALTool/AL-Go runner through Bash. Use the correct App/Test project; unavailable capabilities are never simulated. Publishing retains its separate human/CI gate and is not granted by this adapter
edit, read/readFile, search, #codebaseEdit/Write, Read, Grep/Glob
#changes, changes, search/changesRead-only git status / git diff through Bash only where granted; otherwise read the caller-supplied diff artifact or return the missing evidence request
read/problems, al_get_diagnostics, #testFailureDiscovered mcp__al__al_getdiagnostics for the explicit project, or actual compiler/test output. Diagnostics are not evidence of a fresh compile or executed tests. No result: record not-run / unavailable
al-symbols-mcp/*, upstash/context7/*, microsoft-learn/*, microsoft-docs/*Tools named mcp__al-symbols-mcp__*, mcp__context7__*, mcp__microsoft-docs__* (or the mcp__plugin_aldc_… form). A server that is not loaded is unavailable; do not simulate it
sshadowsdk.al-lsp-for-agents/*, #usages, semantic definition/reference queriesClaude LSP with the existing AL LSP for Agents companion; discover the actual operations and project context first. VS Code tool identifiers are not Claude tool names
ms-dynamics-smb.al/*, vscode.mermaid-chat-features/*VS Code tool identifiers do not exist here. Use only a separately discovered Claude capability; never rename community tools as Microsoft tools
vscode/memory, vscode/*Not available; keep state in the canonical .claude/plans/ artifacts of the project
todoTodoWrite for in-session tracking; durable state stays in .claude/plans/
"load skill-x"The Skill tool (aldc:<skill-name>) or read ${CLAUDE_PROJECT_DIR}/.claude/skills/<skill-name>/SKILL.md
.github/instructions/*.instructions.md (applyTo)${CLAUDE_PROJECT_DIR}/.claude/rules/*.md (paths:); /aldc:al-initialize copies them to the project's .claude/rules/ for auto-apply in the main session; inject inline for subagents exactly as the contract says
.github/skills/, .github/plans/…, docs/templates/${CLAUDE_PROJECT_DIR}/.claude/skills/, .claude/plans/… (project-owned, aldc.yaml -> plans.root), ${CLAUDE_PROJECT_DIR}/claude-plugin/docs/templates/; already rewritten below
python … runtime helpers, --aldc-rootPaths already point at ${CLAUDE_PLUGIN_ROOT}; use python or python3, whichever exists (stdlib only)
aldc.yaml${CLAUDE_PROJECT_DIR}/aldc.yaml when the project has one, otherwise ${CLAUDE_PROJECT_DIR}/claude-plugin/aldc.yaml

Read ${CLAUDE_PROJECT_DIR}/claude-plugin/docs/claude-al-tooling.md for this surface’s LSP/MCP bindings, then ${CLAUDE_PROJECT_DIR}/.claude/skills/skill-migrate/references/cli-al-tools.md for the shared role/evidence contract. The Claude guide supersedes older capability-availability examples, never role responsibilities or human gates.

Build and Deploy AL Extension

Your goal is to build and deploy the AL extension for <DeploymentType from $ARGUMENTS> environment.

Select Deployment Strategy

Based on #codebase select the appropriate deployment strategy Ask and confirm with the user before proceeding.

Deployment Types

Based on the deployment type, use the appropriate strategy:

Development Environment

  1. Build: Use al_build to compile the project
  2. Review: Present build results for human approval
  3. Deploy: Use al_incremental_publish for rapid iteration (requires approval)
  4. Verify: Check for any compilation errors

Testing Environment

  1. Build: Use al_build with full validation
  2. Package: Create .app file with al_package
  3. Review: Present package details for human approval
  4. Deploy: Use al_publish to deploy with debugging enabled (requires approval)
  5. Test: Ensure all unit tests pass

Production Environment

  1. Build: Use al_build with strict validation
  2. Package: Create release package with al_package
  3. Validation: Verify package integrity and dependencies
  4. Documentation: Generate deployment checklist and present for review
  5. Human Gate: MANDATORY - Manual approval required before any production action
    • Note: Automated deployment to production is intentionally disabled as safeguard
    • All production changes require explicit human authorization

Existing Package Deployment

  • Use al_publish_existing_extension when deploying pre-built .app files
  • Verify package compatibility with target environment

Full Dependency Package

  • Use al_full_package when creating packages with all dependencies
  • Useful for offline installations or isolated environments

Error Handling

Monitor the output for:

  • Compilation errors
  • Dependency conflicts
  • Publishing failures
  • Permission issues

Post-Deployment Verification

After deployment:

  1. Verify extension appears in Extension Management
  2. Check all functionality works as expected
  3. Validate permissions are correctly applied
  4. Monitor for any runtime errors

Frequently asked questions

What does the Al Build AI skill do?

Build, package, and deploy AL extensions to Business Central environments. ALDC workflow (Copilot prompt al-build); invoke explicitly.

Why use Al Build on TypingMind?

Because you install it once and use it with any model. Al Build is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Al Build in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/javiarmesto/ALDC-AL-Development-Collection/tree/main/.claude/skills/al-build. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Al Build?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Al Build?

As many as you like. As long as a model supports skills, you can use Al Build with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Al Build AI skill free?

Yes. It is published on GitHub by javiarmesto under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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