Results for “open-ended-questions”

Turn a decision you can't fully answer into a questionnaire for someone else to fill in.

Pending Questions -- Responder Answering Skill

把一件用户自己答不出来的事，变成一份交给别人填的问卷（Markdown 文档），可异步发出也可开会一起填。只访谈"发送"本身——发给谁、要拿回什么——问题则对准收件人知道而用户不知道的那段缺口。

Presents a multiple-choice question to the user via the AskUserQuestion tool, parsing up to four options from a simple command.

Socratic deep interview with mathematical ambiguity gating before explicit execution approval

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.

Provides a general step-by-step procedure for handling requests related to end, size, and fs, with placeholders for specifics.

Facilitates a multi-perspective discussion on Austrian economics between Hayek, Mises, and Claude, analyzing user questions through their distinct philosophical lenses.

Answers questions using a structured wiki knowledge base, returning cited synthesis with wikilinks, counter-arguments, and knowledge gaps.

Turns a decision the user can't fully answer into a questionnaire for someone else to fill in. Use when the user says "turn this into a questionnaire" or needs knowledge pulled out of an expert.

Researches an open-ended question — options, possible solutions, prior art, trade-offs, or how something works — and produces a durable, evidence-backed, adversarially-validated report that recommends an option without committing the team to any artifact. Use when you want to research approaches, weigh options, survey prior art or the state of the art, or understand how something works before committing to a direction. Does not diagnose a bug, failure, or root cause — use investigate. Does not specify a feature — use plan-a-feature. Does not create or update a coding standard — use coding-standard. Does not compare two concrete artifacts for gaps — use gap-analysis. Does not assess an existing module's architecture — use architectural-analysis. Does not capture feedback on Han's own skills — use han-feedback.

Layer 1d of the keyword research pipeline. Mines question-shaped keywords from Semrush phrase_questions (per surviving seed) plus People-Also-Ask strings from the SERP. Appends rows to keyword-ideas.csv with source=question_mining (or merges to source=both when the keyword already exists) and a question_subtype column. Cap of 100 rows per run.

Mental models and decision frameworks for building autonomous agents that continuously learn, explore, and accumulate skills in open-ended environments without human supervision

Enter explore mode to think through ideas, investigate problems, and clarify requirements before implementation, with optional OpenSpec artifact creation.

Gera até cinco perguntas dirigidas para resolver pontos ambíguos do requirements e integra as respostas no documento. Use quando o usuário digitar "/reversa-clarify", "reversa-clarify", "esclarecer dúvidas" ou pedir para tirar pontos abertos do requirements antes de planejar. Etapa opcional do ciclo forward, entre `/reversa-requirements` e `/reversa-plan`.

Route active project/repo memory requests into one honest packet: memory-layer choice, load-context, search-context, store-conclusions, setup-integration, or repo-packer route-out. Use when agents need searchable decisions, manifests, stable links, handoff notes, and small “read this first” packets across sessions. Route long-lived markdown knowledge bases to `llm-wiki`, structural graph memory to `graphify`, human-authored vault organization to note/vault skills, and one-shot repo packing to tools like Repomix, Gitingest, or Code2Prompt.

Approach an app, tool, codebase, website, or product as a genuine first-time user with zero insider context — try it out using only the information provided, get confused where a real newcomer would, and surface the "obvious" questions that people who built or use the thing daily can no longer see. Use this whenever the user wants a first-time-user test, a fresh perspective, a naive walkthrough, a cold read of their README/onboarding/docs/UI, or says things like "pretend you've never seen this", "would a new user get this?", "test my app like a beginner", "what would confuse someone new?", or "give me fresh eyes on this". Also use it when someone wants to find gaps in onboarding, docs, or first-run experience — even if they don't say "fresh eyes" explicitly.

Clarifies ambiguous or conflicting requests by researching first, then asking only judgment calls. Use when prompts say "grill me", ask hard questions, request relentless interrogation, pressure-test assumptions, clarify scope/requirements, define success criteria, or request system-design/optimization decisions.

Extracts and formalizes domain terminology from a conversation into a consistent glossary saved to a local file.

Imported skill faq_database from openai

Runs a relentless interview that sharpens a plan or design. Use when the user wants to be grilled on an idea, pressure-test a plan, or refine a design through questioning.

Runs a relentless interview that asks every frontier question at once, round by round. Use when the user wants to be grilled on a design or map a design tree.

Cross-runtime 2-stage pipeline for Claude Code, Codex/OMX, and Gemini/Antigravity/OMA: trace causal hypotheses, inject evidence into deep-interview style requirements crystallization, then hand off to the right runtime planner/executor.

Grilling

Interviews users relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree.

Adversarial one-question-at-a-time questioning to verify deep reading comprehension of alignment pages, specs, or any document

Deliberative consultation with another Claude model via Task tool. Opus consults Sonnet, Sonnet consults Opus. Ask, evaluate, critique, iterate until workable agreement.

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Socratic deep interview with mathematical ambiguity gating before execution

Socratic coach for breaking down problems to fundamental truths. Use when users want to think through a problem deeply, challenge assumptions, or find innovative solutions. Triggers on requests like "help me think through this", "let's break this down", "what are my blind spots", "I'm stuck on a problem", "challenge my assumptions", or explicit requests for first-principles thinking.

Empty States

Turn scattered closed deals into a win/loss program that changes what you ship and what you say. Picks who to interview, gives you the question guide, codes themes across deals so a pattern beats a single anecdote, and turns the pattern into a specific messaging or product change. Built for B2B GTM, product, and RevOps leaders, customizable to your motion and your CRM. Trigger on "start a win-loss program", "why are we losing deals", "run win-loss interviews", "what patterns are in our closed-lost", "who should I interview", or any win/loss or closed-deal analysis.

OpenTelemetry observability patterns: traces, metrics, logs, context propagation, OTLP export, Collector pipelines, and troubleshooting

Fan one question out across every connected source and return a single ranked, deduplicated, attributed result set.