Agent skill

Architecture Validation

by ArduPilot in ArduPilot/MethodicConfigurator

Validate and update ARCHITECTUREn.md files against actual source code implementation.

GPL-3.0Auto-check passedDevelopment

Install Architecture Validation

skills CLI
$ npx skills add ArduPilot/MethodicConfigurator --skill architecture-validation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ArduPilot/MethodicConfigurator architecture-validation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ArduPilot/MethodicConfigurator.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/architecture-validation .claude/skills/architecture-validation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
architecture-validation
GitHub stars
163
Token cost
~3.8k tokens
SKILL.md length
933 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
GPL-3.0

At a glance

Validate and update ARCHITECTUREn.md files against actual source code implementation.

  • Works in 5 steps: Analyze Source Code Implementation → Assess Code Quality → Update Requirements Section → …
  • Checking if architecture docs are accurate
  • SKILL.md covers Overview, Common Validation Findings, Prerequisites and Architecture Files to Validate, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Architecture Validation is an agent skill from ArduPilot/MethodicConfigurator. Validate and update ARCHITECTUREn.md files against actual source code implementation. Use when checking if architecture docs are accurate, updating implementation status indicators (IMPLEMENTED/PARTIALLY/TODO), or verifying component lists, data flows, and integration points.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The repository describes itself as: A clear ArduPilot configuration sequence. The licence is GPL-3.0.

When your agent uses it

  • Checking if architecture docs are accurate
  • Updating implementation status indicators (IMPLEMENTED/PARTIALLY/TODO)
  • Verifying component lists
  • Integration points

Example prompts

  • “/architecture-validation”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Analyze Source Code Implementation
  2. Assess Code Quality
  3. Update Requirements Section
  4. Validate Architecture Sections
  5. Validate Dependencies

What it can do on your machine

Read from SKILL.md and the folder at commit 9cd3ca1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Architecture Validation loads about 3.8k tokens when it runs. Until then it costs about 76 tokens; SKILL.md has 933 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ArduPilot/MethodicConfigurator at commit 9cd3ca1, republished under its GPL-3.0 licence (© ArduPilot). 933 words, ~3,840 tokens.

Download SKILL.mdSave it as .claude/skills/architecture-validation/SKILL.md (or your agent's skills folder).
name
architecture-validation
description
Validate and update ARCHITECTURE_n_*.md files against actual source code implementation. Use when checking if architecture docs are accurate, updating implementation status indicators (IMPLEMENTED/PARTIALLY/TODO), or verifying component lists, data flows, and integration points.

Architecture Validation Instructions for AI Agents

Overview

This document provides instructions for AI agents to periodically validate and update the architecture documentation files (ARCHITECTURE_n_*.md) against the actual source code implementation. This ensures the documentation remains accurate and up-to-Common Architectural Patterns to Validate:

bash
# Progress callback patterns
grep -r "progress_callback\|callback.*progress" ardupilot_methodic_configurator/

# Platform-specific code
grep -r "platform.system\|Windows\|Linux\|macOS" ardupilot_methodic_configurator/

# Configuration management
grep -r "settings\|config\|preferences" ardupilot_methodic_configurator/

# Internationalization
grep -r "_(\|gettext" ardupilot_methodic_configurator/

# GUI framework usage
grep -r "tkinter\|BaseWindow\|ScrollFrame" ardupilot_methodic_configurator/

Common Validation Findings

Based on validation experience, these patterns commonly need updating in architecture docs:

Integration Points Often Missing
  • Command line argument handling integration
  • Logging system integration
  • GUI framework component integration
  • Configuration/settings system integration (often TODO)
File Structure Often Incomplete
  • Test files not listed
  • Supporting GUI components not mentioned
  • Backend modules providing specific functionality
  • Generated or auto-updated files
Dependencies Often Inaccurate
  • Listed dependencies not actually imported
  • Missing dependencies found in imports
  • Supporting framework components not documented
  • Platform-specific dependencies not noted
Implementation Status Patterns
  • Core functionality usually ✅ IMPLEMENTED
  • Security features often ⚠️ PARTIALLY IMPLEMENTED
  • Error recovery usually ❌ TODO
  • Configuration management often ❌ TODO
  • Advanced features (backup, rollback, retry) usually ❌ TODOase evolves.

Prerequisites

  • Access to the ArduPilot Methodic Configurator codebase
  • Ability to read and analyze Python source files
  • Understanding of software architecture documentation standards
  • Familiarity with the project's coding standards and structure

Architecture Files to Validate

The following architecture files should be validated periodically:

  1. ARCHITECTURE_1_software_update.md - Software Update Sub-Application
  2. ARCHITECTURE_2_flight_controller_communication.md - Flight Controller Communication Sub-Application
  3. ARCHITECTURE_3_directory_selection.md - Directory Selection Sub-Application
  4. ARCHITECTURE_4_component_editor.md - Component Editor Sub-Application
  5. ARCHITECTURE_5_parameter_editor.md - Parameter Editor Sub-Application
  6. ARCHITECTURE_motor_test.md - vehicle motor test Sub-Application

Source Code Mapping

Software Update (ARCHITECTURE_1_software_update.md)
  • ardupilot_methodic_configurator/data_model_software_updates.py
  • ardupilot_methodic_configurator/frontend_tkinter_software_update.py
  • ardupilot_methodic_configurator/backend_internet.py
  • tests/test_data_model_software_updates.py
  • tests/test_frontend_tkinter_software_update.py
Flight Controller Communication (ARCHITECTURE_2_flight_controller_communication.md)
  • ardupilot_methodic_configurator/frontend_tkinter_connection_selection.py
  • ardupilot_methodic_configurator/frontend_tkinter_flightcontroller_info.py
  • ardupilot_methodic_configurator/backend_flightcontroller.py
  • ardupilot_methodic_configurator/backend_mavftp.py
  • ardupilot_methodic_configurator/data_model_fc_ids.py
Directory Selection (ARCHITECTURE_3_directory_selection.md)
  • ardupilot_methodic_configurator/frontend_tkinter_directory_selection.py
  • ardupilot_methodic_configurator/frontend_tkinter_template_overview.py
  • ardupilot_methodic_configurator/backend_filesystem.py
  • ardupilot_methodic_configurator/backend_filesystem_configuration_steps.py
Component Editor (ARCHITECTURE_4_component_editor.md)
  • ardupilot_methodic_configurator/frontend_tkinter_component_editor.py
  • ardupilot_methodic_configurator/frontend_tkinter_component_editor_base.py
  • ardupilot_methodic_configurator/frontend_tkinter_component_template_manager.py
  • ardupilot_methodic_configurator/data_model_vehicle_components*.py
  • ardupilot_methodic_configurator/backend_filesystem_vehicle_components.py
Parameter Editor (ARCHITECTURE_5_parameter_editor.md)
  • ardupilot_methodic_configurator/frontend_tkinter_parameter_editor.py
  • ardupilot_methodic_configurator/frontend_tkinter_parameter_editor_*.py
  • ardupilot_methodic_configurator/frontend_tkinter_stage_progress.py
  • ardupilot_methodic_configurator/backend_filesystem.py

Validation Process

Step 1: Analyze Source Code Implementation

For each requirement in the architecture file, determine implementation status:

  • ✅ IMPLEMENTED: Feature is fully implemented and working
  • ⚠️ PARTIALLY IMPLEMENTED: Feature is partially implemented or has limitations
  • ❌ TODO: Feature is missing or not implemented
Step 2: Assess Code Quality

Examine the following aspects:

  1. Error Handling: Check for comprehensive exception handling
  2. Security: Look for security measures (SSL verification, input validation, etc.)
  3. Performance: Assess efficiency and resource usage
  4. Test Coverage: Find and analyze corresponding test files
Step 3: Update Requirements Section

Update each functional requirement with implementation status:

markdown
### Functional Requirements - Implementation Status

1. **Feature Name** ✅ **IMPLEMENTED**

   - ✅ Specific capability that works
   - ⚠️ Specific capability with limitations
   - ❌ **TODO**: Missing specific capability

2. **Another Feature** ⚠️ **PARTIALLY IMPLEMENTED**

   - ✅ Working parts description
   - ❌ **TODO**: Missing parts description
Step 4: Validate Architecture Sections

Ensure these key architecture sections are accurate and up-to-date:

Data Flow Validation

Review and update the "Data Flow" section to match actual implementation:

markdown
### Data Flow

1. **Phase Name**
   - Current implementation step description
   - How data moves between components
   - Any validation or transformation steps

2. **Another Phase**
   - Actual flow based on source code analysis
   - Integration points and dependencies
Component Validation

Verify that all components listed in the architecture actually exist and function as described. Apply implementation status indicators to all architectural sections:

markdown
### Components - Implementation Status

#### Core Module

- **File**: `module_name.py` ✅ **IMPLEMENTED**
- **Purpose**: Actual purpose based on code analysis
- **Key Classes**:
  - `ClassName`: What it actually does
- **Actual Dependencies**: (verify against imports in source code)

#### Missing Components

- ❌ **TODO**: Components referenced but not implemented
Integration Points Validation

Review and update Integration Points to reflect actual integrations:

markdown
### Integration Points - Implementation Status

- ✅ **Actual Integration**: How it's implemented in the code
- ❌ **TODO: Missing Integration**: Referenced but not implemented
File Structure Validation

Update the File Structure section to include all relevant files found during analysis:

markdown
## File Structure - Implementation Status

```text
main_module.py                    # Description ✅
support_module.py                 # Description ✅
tests/test_main_module.py         # Test coverage ✅

Additional Supporting Files ✅:

  • discovered_dependency.py - Actual purpose from code analysis
markdown

### Step 5: Add Analysis Sections

Include these sections if they don't exist:

```markdown
## Code Quality Analysis

### Strengths

1. **Strength 1**: Description of what works well
2. **Strength 2**: Another positive aspect

### Critical Gaps

1. **Gap 1**: Description of major missing functionality
2. **Gap 2**: Another critical issue

### Security Considerations

- ✅ **Implemented Security Feature**: Description
- ❌ **TODO**: Missing security feature

### Testing Strategy

- ✅ **Implemented Tests**: Description of existing test coverage
- ❌ **TODO**: Missing test coverage areas

## Recommendations for Production Deployment

### High Priority TODO Items

1. **Critical missing feature** with implementation details
2. **Security improvement** with specific requirements

### Medium Priority TODO Items

1. **Enhancement** that would improve the system
2. **Performance optimization** opportunity

### Low Priority TODO Items

1. **Nice-to-have feature** for future consideration
2. **Code cleanup** or refactoring opportunity
Step 6: Validate Dependencies

Check actual dependencies in source files and update the dependencies section:

markdown
### Actual Implementation Dependencies

**Module Name (`filename.py`)**:

- `library_name` for specific purpose (✅ present)
- `another_library` for another purpose (✅ present)

**Missing Dependencies**:

- ❌ `listed_but_unused` - listed in architecture but not used
- ❌ `missing_library` - needed but not listed

Analysis Best Practices

Maintain Status Consistency

Apply implementation status indicators (✅ ⚠️ ❌) consistently across ALL architectural sections:

  • Requirements sections: Mark each requirement with implementation status
  • Data Flow phases: Mark each phase with implementation status
  • Components: Mark each component and its dependencies with status
  • Integration Points: Mark each integration with implementation status
  • File Structure: Mark each file with implementation status
  • Security/Error Handling/Testing: Mark each aspect with implementation status
Discover Supporting Dependencies

Look beyond the main modules to find supporting files:

bash
# Find imports and dependencies
grep -r "from.*import\|import.*" ardupilot_methodic_configurator/
# Find test files
find tests/ -name "*test_module_name*"
# Find supporting GUI components
grep -r "BaseWindow\|ScrollFrame\|tkinter" ardupilot_methodic_configurator/
Show full SKILL.md (389 more words)Show less
Analyze Test Coverage Depth

When assessing test coverage, provide specific metrics:

  • Line count of test files (wc -l test_file.py)
  • Number of test methods (grep -c "def test_" test_file.py)
  • Types of tests (unit, integration, error handling, platform-specific)
  • Coverage gaps (what's not tested)
Markdown Formatting Requirements

Ensure proper markdown formatting to avoid linting errors:

  • Lists: Surround all lists with blank lines before and after
  • Code blocks: Add language specification and blank lines around fenced code blocks
  • Trailing spaces: Avoid trailing spaces at end of lines
  • Status indicators: Use consistent format: ✅ **IMPLEMENTED**, ⚠️ **PARTIALLY IMPLEMENTED**, ❌ **TODO**

Example of proper formatting:

markdown
### Section Title

Introductory text.

- ✅ **Feature Name**: Description of implementation
- ❌ **TODO: Missing Feature**: What needs to be implemented

Next paragraph after list.
Implementation Detail Level

Include specific implementation details discovered in source code:

  • Exact class and function names from the code
  • Key algorithms or logic patterns (e.g., "Uses packaging.version.parse() for semantic versioning")
  • Error handling patterns found (e.g., "Catches RequestException, OSError, etc.")
  • Integration mechanisms (e.g., "Called from __main__.py line 107")
  • Progress tracking mechanisms (e.g., "Uses callback-based progress updates")
Be Specific and Actionable
  • Provide exact file names and line numbers when relevant
  • Ensure TODO items can be implemented by developers
  • Include both positive findings and areas for improvement
Senior Developer Perspective

Analyze the code as an experienced senior developer would:

  • Consider production readiness, not just functionality
  • Evaluate security implications of design decisions
  • Assess maintainability and technical debt
  • Think about edge cases and error scenarios
Common Patterns to Look For

Security Patterns:

bash
grep -r "verify=\|ssl\|https\|security" ardupilot_methodic_configurator/

Error Handling Patterns:

bash
grep -r "try:\|except\|raise\|logging" ardupilot_methodic_configurator/

TODO/FIXME Comments:

bash
grep -r "TODO\|FIXME\|XXX" ardupilot_methodic_configurator/

Validation Checklist

Before submitting architecture updates:

  • All source files have been examined for actual implementation
  • Test coverage has been assessed with specific metrics (line counts, test methods)
  • Security considerations have been evaluated against actual code patterns
  • Data Flow section reflects actual implementation flow
  • Component descriptions match source code with exact class/function names
  • Integration Points section updated with actual integrations found
  • File Structure section includes all discovered supporting files
  • Dependencies are accurate and verified against actual imports
  • Status indicators (✅ ⚠️ ❌) are applied consistently across ALL sections
  • Implementation details include specific code patterns and mechanisms
  • TODO items are specific and actionable with technical requirements
  • Markdown formatting follows project standards (lists, code blocks, spacing)
  • All file paths and module names are correct and verified
  • Recommendations are prioritized appropriately (High/Medium/Low)
  • Documentation is comprehensive but concise
  • Supporting dependencies discovered via grep/find commands are included

Example Analysis Output

When updating an architecture file, follow this format:

markdown
### Functional Requirements - Implementation Status

1. **Version Check** ✅ **IMPLEMENTED**

   - ✅ Checks current version against latest via GitHub releases API
   - ✅ Handles network connectivity issues with proper exception handling
   - ✅ Validates version format using `packaging.version.parse()`

2. **Download Management** ⚠️ **PARTIALLY IMPLEMENTED**

   - ✅ Downloads from verified GitHub sources with SSL verification
   - ❌ **TODO**: No checksum or signature validation of downloaded files
   - ❌ **TODO**: No resume capability for partial downloads

## Code Quality Analysis

### Strengths

1. **Comprehensive Error Handling**: All major exception types are caught and handled
2. **Extensive Test Coverage**: 574 lines of comprehensive unit tests

### Critical Security Gaps

1. **No File Integrity Verification**: Downloaded files are not validated with checksums
2. **No Backup Mechanism**: Current installation could be corrupted without recovery

## Recommendations for Production Deployment

### High Priority TODO Items

1. **Implement file integrity verification** using checksums from GitHub release assets
2. **Add backup mechanism** before attempting installation

This format ensures consistent, actionable documentation that accurately reflects the implementation status.

© ArduPilot, GPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/architecture-validation of ArduPilot/MethodicConfigurator.

Open the folder on GitHubat commit 9cd3ca1

Compare with similar skills

Architecture Validation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Architecture Validation

What does Architecture Validation do?

Validate and update ARCHITECTUREn.md files against actual source code implementation. Architecture Validation is an agent skill from ArduPilot/MethodicConfigurator.md files against actual source code implementation.

When should I use Architecture Validation?

Architecture Validation fits situations like: checking if architecture docs are accurate; updating implementation status indicators (IMPLEMENTED/PARTIALLY/TODO); verifying component lists; integration points.

How do I install Architecture Validation in Claude Code?

Run `npx skills add ArduPilot/MethodicConfigurator --skill architecture-validation -a claude-code`. Or copy the skill folder (.github/skills/architecture-validation in ArduPilot/MethodicConfigurator) into .claude/skills/architecture-validation in your project. Claude Code loads it when a task matches its description.

How do I install Architecture Validation in Codex?

Run `npx skills add ArduPilot/MethodicConfigurator --skill architecture-validation -a codex`. Or copy the skill folder (.github/skills/architecture-validation in ArduPilot/MethodicConfigurator) into .agents/skills/architecture-validation in your project. Codex loads it when a task matches its description.

Can I use Architecture Validation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArduPilot/MethodicConfigurator --skill architecture-validation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/architecture-validation, .gemini/skills/architecture-validation, .github/skills/architecture-validation and .opencode/skills/architecture-validation in your project.

What does Architecture Validation need to run?

SKILL.md names no scripts, command-line tools or credentials: Architecture Validation is instructions for the agent only. Our summary lists: Python 3.

Does Architecture Validation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Architecture Validation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Architecture Validation use?

Architecture Validation is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Architecture Validation use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Architecture Validation?

Skills that share tags, products or a category with Architecture Validation: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Architecture Validation?

ArduPilot (a GitHub organization) maintains it in ArduPilot/MethodicConfigurator, which has 163 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on October 7, 2026.

Source: ArduPilot/MethodicConfigurator on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.