clarification

Intent compiler for AI agents — converges vague requests into typed IntentSpec contracts (probe, ask, or halt before routing), the input layer for routers and typed-decision models like Jev & Laya

Template-guided Clarifying Question Generation for Web Search Clarification (CIKM'2021).

AskBench: LLM question-asking/clarification benchmark & dataset with evaluation and training code (paper: arXiv 2602.11199).

Let your Pi agent ask instead of guessing, keep working, and remember your answers.

A tiny Agent Skill that makes Codex, Claude Code, and Gemini CLI ask before they guess.

Structured and minimal ask_questions tool for Pi planning and clarification flows

Public beta docs and examples for a pre-reasoning observability controller for LLM and agent workflows.

Interactive benchmark for evaluating LLMs on clarifying ambiguous code requirements. Paper: arXiv:2607.00711

`util.inspect` with additional type information

Udacity R based analysis of how the chemical contents of wind impacts quality.

Clarify your Pi editor draft before sending — Alt+G to rewrite, /clarify to pick model.

🧭 Stop guessing. Start building. A 95%-confidence requirement clarification gate for Codex and AI agents—ask less, learn more, execute safely.

Intent clarification skill and bilingual demo for turning vague human input into precise language people and AI agents can act on.

DSH 需求澄清优化插件：自然语言 → 反问确认 → 紧凑 agent 指令（可选 DeepSeek LLM 增强）

Private, local-first executive work orchestrator for macOS. Clarification-driven; runs local Ollama models overnight.

DPO-trained decision layer for coding agents — raises pass@1 from 0.419 → 0.639 on frozen 14B generator (p<0.000001)

Improve collaboration efficiency: faster clarification, faster context injection.

ClarifyLaw: hybrid retrieval and dynamic clarification for Korean legal QA.

Clarification on the Qwen-image-2