build-and-compile
Building, compiling, and resolving dependency issues across languages
Thorough verification of completed work before declaring done
Claude Code
$ npx -y skills add vstorm-co/pydantic-deepagents --skill verification-strategy --agent claude-code
Codex
$ npx skills add vstorm-co/pydantic-deepagents --skill verification-strategy -a codex -y
Cursor
$ npx skills add vstorm-co/pydantic-deepagents --skill verification-strategy -a cursor -y
Gemini CLI
$ npx skills add vstorm-co/pydantic-deepagents --skill verification-strategy -a gemini-cli -y
GitHub Copilot
$ npx skills add vstorm-co/pydantic-deepagents --skill verification-strategy -a github-copilot -y
OpenCode
$ npx skills add vstorm-co/pydantic-deepagents --skill verification-strategy -a opencode -y
Windsurf
$ npx skills add vstorm-co/pydantic-deepagents --skill verification-strategy -a windsurf -y
name: verification-strategy description: "Thorough verification of completed work before declaring done" tags: [testing, verification, benchmark] version: "1.0.0"
How to verify your work is correct before declaring a task complete.
After implementing a solution, go through ALL of these:
ls -la /path/to/expected/output wc -c /path/to/output # byte count file /path/to/output # format detection head -5 /path/to/output # content preview
If you can find the test script, READ IT:
Tests often check things you didn't expect:
| What you think is right | What the test actually checks | |------------------------|-------------------------------| | Output on stdout | Output in a specific file | | Human-readable numbers | Exact decimal precision | | Relative file paths | Absolute file paths | | "Close enough" answer | Exact match | | Code that runs | Code under a specific size limit |
Before declaring done, ask yourself: 1. Did I create/modify ALL the files the task asks for? 2. Did I use the EXACT paths, names, and formats specified? 3. Does my code compile/run WITHOUT errors? 4. Does my output match what automated tests would expect? 5. Have I cleaned up any debug output or temporary files?
Open-source, self-hosted Claude Code - a terminal AI assistant and the Python framework behind it. Tool-calling, sandboxed execution, multi-agent teams, skills, checkpoints, unlimited context - on Pydantic AI, any model.
Building, compiling, and resolving dependency issues across languages
Working with diverse data formats: binary, text, structured, and custom
Systematic exploration of unknown environments before starting work
Writing efficient code that handles large data and tight constraints
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.