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Problem framing is the critical first step before any implementation, teaching agents to detect vague or incomplete requirements, pause execution, and ask clarifying questions. This skill reduces hall
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npx skills add https://github.com/diegosouzapw/awesome-omni-skill --skill problem-framingコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Token-efficient tracking for AI orchestration. CLI-first for status updates (~50 tokens), agent fallback for complex ops (~1KB). Use when: updating task status, querying blockers, creating progress files, validating phases.
AshAi extension guidelines for integrating AI capabilities with Ash Framework. Use when implementing vectorization/embeddings, exposing Ash actions as LLM tools, creating prompt-backed actions, or setting up MCP servers. Covers semantic search, LangChain integration, and structured outputs.
This skill should be used when solving hard questions, complex architectural problems, or debugging issues that benefit from GPT-5 Pro or GPT-5.1 thinking models with large file context. Use when standard Claude analysis needs deeper reasoning or extended context windows.
SOC 職業分類に基づく
SKILL.md を表示中
| id | SKL-problem-PROBLEMFRAMING |
| name | Problem Framing |
| description | Problem framing is the critical first step before any implementation, teaching agents to detect vague or incomplete requirements, pause execution, and ask clarifying questions. This skill reduces hall |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
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Problem framing is the critical first step before any implementation, teaching agents to detect vague or incomplete requirements, pause execution, and ask clarifying questions. This skill reduces hallucinations, prevents wasted effort, and improves first-shot success rate by ensuring the agent understands the problem before attempting to solve it. It provides systematic methods for ambiguity detection, question formulation, and problem restatement to establish clear understanding between humans and AI agents.
# Example implementation following best practices
def example_function():
# Your implementation here
pass
.env.example keys: API_KEY, DATABASE_URL (no values)| Type | Focus Area | Required Scenarios / Mocks |
|---|---|---|
| Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| Integration | DB / API | All external API calls or database connections must be mocked during unit tests |
| E2E | User Journey | Critical user flows to test |
| Performance | Latency / Load | Benchmark requirements |
| Security | Vuln / Auth | SAST/DAST or dependency audit |
| Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
request_iderror_rate, latency, queue_depth(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)