add_platform.verify
Sets up platform directories and verifies deepwork install works correctly. Use after implementation to confirm integration.
Best use case
add_platform.verify is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sets up platform directories and verifies deepwork install works correctly. Use after implementation to confirm integration.
Teams using add_platform.verify should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/add-platform-verify/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How add_platform.verify Compares
| Feature / Agent | add_platform.verify | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sets up platform directories and verifies deepwork install works correctly. Use after implementation to confirm integration.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# add_platform.verify **Step 4/4** in **integrate** workflow > Full workflow to integrate a new AI platform into DeepWork > Adds a new AI platform to DeepWork with adapter, templates, and tests. Use when integrating Cursor, Windsurf, or other AI coding tools. ## Prerequisites (Verify First) Before proceeding, confirm these steps are complete: - `/add_platform.implement` ## Instructions **Goal**: Sets up platform directories and verifies deepwork install works correctly. Use after implementation to confirm integration. # Verify Installation ## Objective Ensure the new platform integration works correctly by setting up necessary directories and running the full installation process. ## Task Perform end-to-end verification that the new platform can be installed and that DeepWork's standard jobs work correctly with it. ### Prerequisites Ensure the implementation step is complete: - Adapter class exists in `src/deepwork/adapters.py` - Templates exist in `src/deepwork/templates/<platform_name>/` - Tests pass with 100% coverage - README.md is updated ### Process 1. **Set up platform directories in the DeepWork repo** The DeepWork repository itself should have the platform's command directory structure for testing: ```bash mkdir -p <platform_command_directory> ``` For example: - Claude: `.claude/commands/` - Cursor: `.cursor/commands/` (or wherever Cursor stores commands) 2. **Run deepwork install for the new platform** ```bash deepwork install --platform <platform_name> ``` Verify: - Command completes without errors - No Python exceptions or tracebacks - Output indicates successful installation 3. **Check that command files were created** List the generated command files: ```bash ls -la <platform_command_directory>/ ``` Verify: - `deepwork_jobs.define.md` exists (or equivalent for the platform) - `deepwork_jobs.implement.md` exists - `deepwork_jobs.refine.md` exists - `deepwork_rules.define.md` exists - All expected step commands exist 4. **Validate command file content** Read each generated command file and verify: - Content matches the expected format for the platform - Job metadata is correctly included - Step instructions are properly rendered - Any platform-specific features (hooks, frontmatter) are present 5. **Test alongside existing platforms** If other platforms are already installed, verify they still work: ```bash deepwork install --platform claude ls -la .claude/commands/ ``` Ensure: - New platform doesn't break existing installations - Each platform's commands are independent - No file conflicts or overwrites ## Quality Criteria - Platform-specific directories are set up in the DeepWork repo - `deepwork install --platform <platform_name>` completes without errors - All expected command files are created: - deepwork_jobs.define, implement, refine - deepwork_rules.define - Any other standard job commands - Command file content is correct: - Matches platform's expected format - Job/step information is properly rendered - No template errors or missing content - Existing platforms still work (if applicable) - No conflicts between platforms - When all criteria are met, include `<promise>✓ Quality Criteria Met</promise>` in your response ## Context This is the final validation step before the platform is considered complete. A thorough verification ensures: - The platform actually works, not just compiles - Standard DeepWork jobs install correctly - The platform integrates properly with the existing system - Users can confidently use the new platform Take time to verify each aspect - finding issues now is much better than having users discover them later. ## Common Issues to Check - **Template syntax errors**: May only appear when rendering specific content - **Path issues**: Platform might expect different directory structure - **Encoding issues**: Special characters in templates or content - **Missing hooks**: Platform adapter might not handle all hook types - **Permission issues**: Directory creation might fail in some cases ### Job Context A workflow for adding support for a new AI platform (like Cursor, Windsurf, etc.) to DeepWork. The **integrate** workflow guides you through four phases: 1. **Research**: Capture the platform's CLI configuration and hooks system documentation 2. **Add Capabilities**: Update the job schema and adapters with any new hook events 3. **Implement**: Create the platform adapter, templates, tests (100% coverage), and README updates 4. **Verify**: Ensure installation works correctly and produces expected files The workflow ensures consistency across all supported platforms and maintains comprehensive test coverage for new functionality. **Important Notes**: - Only hooks available on slash command definitions should be captured - Each existing adapter must be updated when new hooks are added (typically with null values) - Tests must achieve 100% coverage for any new functionality - Installation verification confirms the platform integrates correctly with existing jobs ## Required Inputs **Files from Previous Steps** - Read these first: - `templates/` (from `implement`) ## Work Branch Use branch format: `deepwork/add_platform-[instance]-YYYYMMDD` - If on a matching work branch: continue using it - If on main/master: create new branch with `git checkout -b deepwork/add_platform-[instance]-$(date +%Y%m%d)` ## Outputs **Required outputs**: - `verification_checklist.md` ## Guardrails - Do NOT skip prerequisite verification if this step has dependencies - Do NOT produce partial outputs; complete all required outputs before finishing - Do NOT proceed without required inputs; ask the user if any are missing - Do NOT modify files outside the scope of this step's defined outputs ## On Completion 1. Verify outputs are created 2. Inform user: "integrate step 4/4 complete, outputs: verification_checklist.md" 3. **integrate workflow complete**: All steps finished. Consider creating a PR to merge the work branch. --- **Reference files**: `.deepwork/jobs/add_platform/job.yml`, `.deepwork/jobs/add_platform/steps/verify.md`
Related Skills
add_platform
Adds a new AI platform to DeepWork with adapter, templates, and tests. Use when integrating Cursor, Windsurf, or other AI coding tools.
add_platform.research
Captures CLI configuration and hooks system documentation for the new platform. Use when starting platform integration.
add_platform.implement
Creates platform adapter, templates, tests with 100% coverage, and README documentation. Use after adding hook capabilities.
add_platform.add_capabilities
Updates job schema and adapters with any new hook events the platform supports. Use after research to extend DeepWork's hook system.
32-analyze-verify-150
[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
1k-platform-requirements
Documents minimum SDK/OS version requirements for all OneKey platforms. Use when checking platform compatibility, understanding deployment targets, verifying version requirements, or when user asks if their device can run the project. Triggers on minimum version, SDK version, API level, deployment target, platform requirements, iOS version, Android version, Chrome version, Electron version, can I run, environment check, device compatibility, check environment.
1k-cross-platform
Cross-platform development patterns for OneKey. Use when writing platform-specific code, handling platform differences, or working with native/web/desktop/extension platforms. Triggers on platform, native, web, desktop, extension, iOS, Android, Electron, platformEnv, .native.ts, .web.ts, .desktop.ts, .ext.ts, cross-platform, multi-platform.
whisper-transcribe
Transcribes audio and video files to text using OpenAI's Whisper CLI, enhanced with contextual grounding from local markdown files for improved accuracy.
thor-skills
An entry point and router for AI agents to manage various THOR-related cybersecurity tasks, including running scans, analyzing logs, troubleshooting, and maintenance.
lets-go-rss
A lightweight, full-platform RSS subscription manager that aggregates content from YouTube, Vimeo, Behance, Twitter/X, and Chinese platforms like Bilibili, Weibo, and Douyin, featuring deduplication and AI smart classification.
grail-miner
This skill assists in setting up, managing, and optimizing Grail miners on Bittensor Subnet 81, handling tasks like environment configuration, R2 storage, model checkpoint management, and performance tuning.
vly-money
Generate crypto payment links for supported tokens and networks, manage access to X402 payment-protected content, and provide direct access to the vly.money wallet interface.