ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
Build in verifiable increments. Never implement more than can be tested right now. Ship partial working systems over complete broken ones.
$ npx -y skills add DevelopersGlobal/ai-agent-skills --skill incremental-coding --agent claude-codeHow it fires
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/incremental-codingContext preview
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Build in verifiable increments. Never implement more than can be tested right now. Ship partial working systems over complete broken ones.
name: incremental-coding description: Build in verifiable increments. Never implement more than can be tested right now. Ship partial working systems over complete broken ones. category: build applies-to: [claude, gemini, cursor, copilot, any] version: 1.0.0
The biggest risk in software development is building a lot of code that doesn't work. Incremental coding limits this risk: build a little, verify it works, build more. At every step, the system is in a known-good state.
1. What is the smallest possible thing you can build that provides value and can be verified? 2. It doesn't have to be feature-complete — just correct and verifiable. 3. Example: "Add the endpoint skeleton with hardcoded response" before adding business logic.
**Verify:** The first increment can be verified in under 5 minutes.
4. Build only the first increment. 5. Run tests. Verify manually if needed. Confirm it works. 6. Commit this working state. 7. Repeat for the next increment.
**Verify:** There is a working commit after each increment.
8. Integrate with the real system as early as possible — not at the end. 9. Test against real dependencies (DB, API, etc.) as early as possible. 10. Fake integrations (mocks) should be replaced with real ones by the end.
**Verify:** By completion, all mocks replaced with real integration.
| Excuse | Rebuttal | |--------|----------| | "I need to build it all to know if it works" | No. Build the first piece and test it. Uncertainty is always reducible. | | "Integration is at the end" | Integration pain is proportional to time since last integration. Integrate continuously. |
AI agent skills for production grade applications
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
Design stable, versioned, self-documenting APIs. Easy to use correctly, hard to use incorrectly. Apply Hyrum's Law from day one.
Automated quality gates from commit to production. Every merge to main is potentially shippable. No manual steps in the deployment path.
Get layered, context-aware explanations of unfamiliar code. Understand what it does, why it was written that way, and how to work with it safely.
Structured code review focusing on correctness, security, and maintainability. Correctness before style. Every reviewer comment must be actionable.
Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.