idea-to-spec
Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec.
git clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skills /tmp/idea-to-spec && cp -r /tmp/idea-to-spec/skills/idea-to-spec ~/.claude/skills/idea-to-specSKILL.md
## Overview 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. ## When to Use - At the start of any new feature - When a request is ambiguous - Before creating tasks or writing any code ## Process ### Step 1: Capture the Core Problem 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. ### Step 2: Define Success 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. ### Step 3: Define Scope 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. ### Step 4: Define Non-Functional Requirements 10. Performance: response time, throughput, scale targets. 11. Security: auth requirements, data sensitivity. 12. Reliability: uptime SLA, acceptable error rate. ## Verification - [ ] Problem statement written without mentioning implementation - [ ] Acceptance criteria in Given/When/Then format - [ ] Each criterion is binary (pass/fail) - [ ] Explicit out-of-scope list - [ ] Open questions listed ## References - [think-before-coding skill](../think-before-coding/SKILL.md) - [task-decomposition skill](../task-decomposition/SKILL.md)
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
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.