ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec.
$ npx -y skills add DevelopersGlobal/ai-agent-skills --skill idea-to-spec --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/idea-to-specContext preview
The summary Claude sees to decide when to auto-load this skill.
Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec.
name: idea-to-spec description: Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec. category: think applies-to: [claude, gemini, cursor, copilot, any] version: 1.0.0
Vague ideas produce vague implementations. This skill transforms any idea — no matter how fuzzy — into a concrete specification with clear scope, acceptance criteria, and non-goals.
1. Write: *"Users currently can't [do X], which causes [pain Y]."* 2. Identify: who has this problem? How often? What's the impact? 3. Distinguish problem from solution — don't spec a solution until the problem is understood.
**Verify:** You can state the problem without mentioning any implementation.
4. Write 3–7 acceptance criteria in this format:
Given [context] When [action] Then [outcome]
5. Each criterion must be binary — either it passes or it doesn't. 6. Include negative cases: *"Given X, the system must NOT do Y."*
**Verify:** A QA engineer can test each criterion without asking for clarification.
7. **In scope**: List what is explicitly included. 8. **Out of scope**: List what is explicitly excluded — as important as what's included. 9. **Open questions**: List any decisions that still need resolution before implementation.
**Verify:** The out-of-scope list has at least 2 items.
10. Performance: response time, throughput, scale targets. 11. Security: auth requirements, data sensitivity. 12. Reliability: uptime SLA, acceptable error rate.
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Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and…
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