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Al Pr Prepare

Community
javiarmesto
al-pr-prepare

Prepare a clean, documented pull request draft for AL features or fixes with summary, testing notes, and checklist. ALDC workflow (Copilot prompt al-pr-prepare); invoke explicitly.

Overview

Publisherjaviarmesto
RepositoryALDC-AL-Development-Collection
Skill nameal-pr-prepare
Stars
115
Forks
37
Bundled files
Instructions only
LicenseMIT
Links
  • 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 Pr Prepare 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-pr-prepare
  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-pr-prepare .claude/skills/al-pr-prepare
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Al Pr Prepare 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 Pr Prepare 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 Pr Prepare 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-pr-prepare.prompt.md (sha256 e6c5e60c501f779c8e3d899bc7d3f326a3fe0d545c767540a48f07ebc483e8ab). 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.

AL Pull Request Preparation

Your goal is to prepare a pull request draft for the branch <Branch from $ARGUMENTS> summarizing all modifications, test evidence, and validation steps.

🔒 Human Gate: Pre-PR Review

Before generating PR draft document:

  1. Review code changes - Present summary of all modifications
  2. Security check - Confirm no sensitive data in commits
  3. Quality validation - Verify tests pass and build succeeds
  4. Human approval required - Obtain confirmation before creating PR draft

Process

1. Change Analysis

Inspect Branch Differences

Use codebase to analyze modifications:

codebase: Compare <Branch from $ARGUMENTS> with main branch

Use githubRepo to gather context:

githubRepo: Get branch information and commit history

Gather:

  • Modified files and line counts
  • New files added
  • Deleted files
  • Commit messages and references
  • Related issues or work items
Classify Changes

Categorize into:

  1. New Features - New AL objects, functionality, APIs
  2. Bug Fixes - Corrections, refactors, optimizations
  3. Tests - Test codeunits, scenarios, data
  4. Configuration - app.json, permissions, dependencies
  5. Documentation - README, comments, API docs

2. Extract Metadata

Find References: Scan commit messages for:

  • Issue references (#123)
  • Work item IDs
  • Requirement numbers

Pattern matching:

  • Fixes #123
  • Closes #456
  • Related to WORK-789

Identify Reviewers: If <Reviewer from $ARGUMENTS> is specified, include in the draft.

3. Generate PR Draft

Create /reports/pr-draft.md with this structure:

markdown
# Pull Request: [Feature/Fix Title]

**Branch:** `<Branch from $ARGUMENTS>`
**Target:** `main`
**Author:** [Author Name]
**Date:** [Current Date]

## Overview

[2-3 sentence description of changes]

**Type of Change:**
- [ ] New Feature
- [ ] Bug Fix
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Documentation
- [ ] Configuration Change

## Related Issues

- Closes #[issue-number]
- Relates to #[issue-number]

## Changes Summary

### File Changes

| Category | Files | +Lines | -Lines |
|----------|-------|--------|--------|
| New Features | [#] | [#] | [#] |
| Bug Fixes | [#] | [#] | [#] |
| Tests | [#] | [#] | [#] |
| Docs | [#] | [#] | [#] |
| **Total** | **[#]** | **[#]** | **[#]** |

### AL Objects Modified

#### New Objects

| Type | ID | Name | Purpose |
|------|----|----- |---------|
| Table | [ID] | [Name] | [Purpose] |
| Page | [ID] | [Name] | [Purpose] |
| Codeunit | [ID] | [Name] | [Purpose] |

#### Modified Objects

| Type | ID | Name | Changes |
|------|----|----- |---------|
| TableExt | [ID] | [Name] | [Description] |
| PageExt | [ID] | [Name] | [Description] |

#### Deleted Objects

| Type | ID | Name | Reason |
|------|----|----- |-------|
| [Type] | [ID] | [Name] | [Reason] |

## Technical Details

### Architecture Changes
[Describe design pattern or architecture changes]

### Dependencies
**New:** [List new dependencies]
**Modified:** [old version → new version]
**Removed:** [List removed dependencies]

### Database Changes
- [ ] New tables
- [ ] New fields
- [ ] Modified fields
- [ ] New keys

**Migration Notes:** [Any data migration needed]

### API Changes
**New Endpoints:**
- `GET /api/[endpoint]` - [Description]

**Modified Endpoints:**
- `[Method] /api/[endpoint]` - [Changes]

**Breaking Changes:** [List breaking changes]

### Events
**Published:** [New events and purpose]
**Subscribed:** [New subscribers and purpose]

## Testing

### Test Scenarios

1. **Scenario:** [Description]
   - **Steps:** [How to test]
   - **Expected:** [Expected result]
   - **Result:** ✅ Pass / ❌ Fail

### Automated Tests

- ✅ Unit Tests: [X/Y passed]
- ✅ Integration Tests: [X/Y passed]
- ✅ Code Coverage: [X]%

### Performance Impact

- [ ] No impact
- [ ] Improvement: [Details]
- [ ] Potential impact: [Mitigation]

## Review Checklist

### Code Quality
- [ ] Follows AL naming conventions
- [ ] Follows AL style guidelines
- [ ] No compiler warnings
- [ ] Error handling implemented
- [ ] Logging adequate

### Security
- [ ] No hardcoded secrets
- [ ] Permissions reviewed
- [ ] Input validation
- [ ] No SQL injection risks

### Testing
- [ ] All tests pass
- [ ] New code has tests
- [ ] Edge cases tested
- [ ] Manual testing done

### Documentation
- [ ] Code comments added
- [ ] API docs updated
- [ ] README updated
- [ ] Help text added

### BC Specific
- [ ] Object IDs in range
- [ ] Event patterns correct
- [ ] Page layouts user-friendly
- [ ] Translations handled

### Deployment
- [ ] Build succeeds
- [ ] Package creates
- [ ] Deployment steps documented
- [ ] Rollback plan ready

## Deployment Notes

### Steps
1. [Deployment instructions]
2. [Configuration changes]
3. [Verification steps]

### Prerequisites
[Requirements for deployment]

### Rollback Plan
[How to rollback if needed]

## Screenshots

### Before
[Previous state]

### After
[New state]

## Additional Notes

### Known Issues
[Limitations or known issues]

### Future Enhancements
[Potential improvements]

### Breaking Changes
[Breaking changes affecting existing functionality]

## Reviewer Notes

**Suggested Reviewers:**
- <Reviewer from $ARGUMENTS> - [Reason]

**Focus Areas:**
1. [Area to review carefully]
2. [Another focus area]

**Questions:**
[Specific questions or concerns]

---

**Generated by:** AL PR Preparation Workflow
**Generated on:** [Timestamp]

Output

Primary: /reports/pr-draft.md Format: Complete markdown document ready for PR creation

Success Criteria

  • ✅ PR draft file created under /reports/pr-draft.md
  • ✅ Changes summarized by category
  • ✅ All modified AL objects documented
  • ✅ Related issues referenced
  • ✅ Review checklist complete
  • ✅ Deployment notes clear

Common PR Patterns

Feature Addition

  • Emphasize new capabilities
  • User benefit focus
  • Comprehensive testing
  • Document APIs/events

Bug Fix

  • Reference original issue
  • Explain root cause
  • Show before/after
  • Include regression tests

Refactoring

  • Explain motivation
  • Show no functional changes
  • Highlight improvements
  • Demonstrate coverage

Performance Optimization

  • Include benchmarks
  • Show improvements
  • Document approach
  • Note trade-offs

Tips

  • Be concise but thorough
  • Use tables for structure
  • Include file names and line numbers
  • Link to related documentation
  • Provide context for changes
  • Make reviewer's job easy
  • Include visual evidence
  • Anticipate questions
  • Document decisions
  • Keep security visible

Next Steps

For final validation:

@AL Development Conductor   # TDD orchestration with review subagent
@AL Implementation Specialist   # Direct testing and fixes

PR draft ready for GitHub submission.

Frequently asked questions

What does the Al Pr Prepare AI skill do?

Prepare a clean, documented pull request draft for AL features or fixes with summary, testing notes, and checklist. ALDC workflow (Copilot prompt al-pr-prepare); invoke explicitly.

Why use Al Pr Prepare on TypingMind?

Because you install it once and use it with any model. Al Pr Prepare 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 Pr Prepare 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-pr-prepare. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Al Pr Prepare?

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 Pr Prepare?

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

Is the Al Pr Prepare 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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