Results for “clarification-questions”

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

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`.

Improve unclear UX copy, error messages, microcopy, labels, and instructions. Makes interfaces easier to understand and use.

Improve unclear UX copy, error messages, microcopy, labels, and instructions. Makes interfaces easier to understand and use.

Identify and improve unclear, confusing, or poorly written interface text to make the product easier to understand and use.

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

Short clarifying interview before starting non-trivial engineering work — surfaces the actual requirement, what's explicitly out of scope, and what proves it's done. Use before writing code when the task's requirement, scope, or definition of done isn't already unambiguous. Not for trivial or fully-specified changes — that's ceremony, not clarity.

Proceed through ordinary ambiguity by making reasonable assumptions, keeping momentum, validating as you go, and ending with a clear recap of decisions, changes, verification, and residual risk.

Refines task understanding through iterative questioning before executing, using the Joyride extension for human input.

Define business questions before starting data analysis to ensure the analysis is meaningful and aligned with business goals.

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.

"Make it interpretable" is four different requests.

对 wiki 提问，综合检索相关页面后回答，好的回答可 crystallize 回 wiki

Answers factual questions by searching the web, fetching primary sources, and cross-checking claims before citing them.

Provides a structured methodology for solving nontrivial, ambiguous problems by framing outcomes, selecting evidence, routing specialists, choosing options, and verifying results.

Deliberative consultation with Codex CLI. Ask, evaluate, critique, iterate until workable agreement.

Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Use when the user requests fact checking or provides relevant inputs for this workflow.

Explain any concept in plain words with one concrete analogy - clear enough for a smart child or a busy outsider, never condescending, never wrong.

Deliberative consultation with Gemini CLI. Ask, evaluate, critique, iterate until workable agreement.

Write FAQ pages with comprehensive answers. TRIGGERS - Use when user needs help with faq-writer related tasks.

Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

Clarify vague requirements through exploratory questioning and option generation before committing to research or implementation.

Enforces running verification commands and confirming output before claiming work is complete, fixed, or passing, ensuring evidence precedes any success claims.

Find a unifying insight that eliminates multiple components, collapsing complexity by abstracting common patterns.

Use to design and audit how customers are told they are interacting with AI, or that AI was involved in a decision about them, and to evidence that it happened. Trigger for "do we tell customers it's a bot", "AI disclosure requirements", "should the bot say it's not human", "customer asked if they were talking to a person", AI transparency obligations, or evidencing that an AI-assisted decision was explained.

Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base — WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples) — plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".

Use when content is verbose, repetitive, or padded and needs tightening while preserving meaning

Verify the article’s factual, product, citation, and link integrity before visual production.

Write FAQ pages with comprehensive answers. TRIGGERS - Use when user needs help with faq-writer related tasks.

Guides users to uncover the real question behind surface problems, reframe decisions, and challenge assumptions through structured questioning.

Use when answering questions about IFRS standards, IAS standards, IASB, financial reporting, revenue recognition, lease accounting, impairment, financial instruments, expected credit loss, ECL, consolidation, business combinations, fair value measurement, first-time adoption, deferred tax, provisions, insurance contracts, journal entries, disclosure requirements, IFRS compliance checks, audit support, GAAP differences, goodwill, or transitioning from local GAAP to IFRS.

Verify code changes work by running them. Proves through execution, not just inspection.