「questions」の検索結果

Builds a role scorecard, interview loop, sourcing plan, reference-check guide and close plan for a hire, and grades an existing job description or loop. Use when you say 'I need to hire a PM', 'write a job description', 'interview questions', 'how do I run references', 'first sales hire', 'first marketer', 'head of product search', 'scorecard' or 'how do I close a candidate'. Draws on 210 Lenny's Podcast guests including Adam Ward, Keith Rabois and Lauren Ipsen.

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Broad Jev interrogation: generate up to 50 context-specific questions about a plan, spec, design, code artifact, or any topic — using all primitive shapes and structured forms to surface hidden problems before they ship.

日本語の概要は準備中です。原文の説明を表示しています。

Send Telegram messages, files, and alerts via bot API; read replies; ask questions with inline buttons and wait for the answer (approve-from-phone). Supports multiple bots and named chat targets. Use when the user wants to send a Telegram message or alert, get notified on Telegram when a task finishes or needs input, ask for approval via Telegram, or wire Telegram notifications into hooks, cron jobs, or CI. Triggers: 'telegram', 'send me a telegram', 'alert me on telegram', 'ask me on telegram', 'notify me when done'.

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Use when the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

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Build and maintain a Markdown guide that helps humans understand a repository through real behavior, concepts, and evidence. Use when a user asks to understand a repo, generate or update repo-docs, answer repo-architecture/onboarding questions, seed docs for a new project, sync docs after code changes, or delete generated repo docs.

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Translate a complete mathematical modeling contest problem sentence by sentence into precise modeling language, preserve every substantive condition and definition, expose hidden constraints, draw a mandatory Mermaid cross-question flowchart, audit omissions, and deliver one easy-to-open Markdown report. For Huawei Cup (CPGMCM) graduate-contest problems, additionally prepend a plain-language layer that rewrites the background and questions as a primary-school word problem with a mandatory term-mapping table. Optionally render the finished report as one standalone HTML page on request. Use whenever a user supplies a CUMCM, CPGMCM, or other modeling problem and asks to interpret, translate, unpack, read closely, identify requirements, or avoid missing details before modeling.

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Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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Decision rubric for promoting a prose prompt into a typed DSPy Signature. This is an ENHANCE overlay on top of the [[dspy]] library skill: it does NOT teach DSPy syntax — it answers the coder-agent decision "when do I stop hand-writing a prompt string and declare it as a `dspy.Signature`, and how do I name/describe its fields so the optimizer and the calling code both get a clean contract." Activate when: a prompt string grows past ~50 lines; the LM output is consumed by code (parsed, branched on, stored) rather than read by a human; the same prompt is reused across >1 call site; or a teammate asks "should this be a Signature?". Do NOT activate for one-shot throwaway prompts, or for HOW-TO questions about DSPy modules /optimizers/compile — defer those to the [[dspy]] skill and the [[agentsop-dspy]] workflow skill. Search keywords: typed prompt, structured prompt, DSPy Signature, prompt as a function, prompt contract, when to formalize a prompt.

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Screens biomedical / life-science papers for signs of data fabrication, image manipulation, and statistical anomalies, using the detection techniques distilled from the field's canonical exposure platforms (PubPeer, Data Colada, Science Integrity Digest, For Better Science) and tools (ImageTwin/Proofig, statcheck, GRIM/GRIMMER, Problematic Paper Screener, Seek & Blastn). Use when asked to check a paper/figure for image duplication, blot splicing, impossible statistics, paper-mill or tortured-phrase signals, research integrity, or "is this data faked"; or when a user shares a figure, Western blot, supplementary dataset, or DOI and asks whether it looks manipulated. Reports observable anomalies as questions for clarification — it never accuses anyone of fraud.

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Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.

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Podcast knowledge workflows powered by Podwise CLI: search podcasts and episodes by keyword, monitor followed shows for new releases, find popular episodes, ask questions and extract insights from transcript content, process Podwise episode URLs, YouTube videos, Xiaoyuzhou links, and local audio or video files to retrieve transcripts, summaries, chapters, Q&A, mind maps, highlights, and keywords — plus catch up on your backlog, refine your listening taste, generate weekly recaps, export episode notes to PKM tools, research topics across podcasts, debate episode ideas, and generate language learning cards. Use when the user wants to find, summarize, transcribe, or extract insights from any podcast or audio content, or manage their listening library.

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Align on the shape of a change through an interview, then implement it. Escalates open product decisions and settles the implementation shape in conversation. Use when the user asks to "discuss this change", "align on this change first", "ask me questions first", "interview me then implement", "agree on the approach before coding", or wants the shape of a single change settled before any code is written.

日本語の概要は準備中です。原文の説明を表示しています。

Retrieval-augmented generation (RAG) skill for the D&D 5e System Reference Document (SRD). Use when answering questions about D&D 5e core rules, spells, combat, equipment, conditions, monsters, and other SRD content. This skill provides agentic search-based access to the SRD split into page-range markdown files.

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Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

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Use when starting any offensive-security engagement or task — establishes how to find and invoke the right skill before any action (including clarifying questions, recon, exploitation, or reporting)

日本語の概要は準備中です。原文の説明を表示しています。

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

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Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.

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Explain US architecture professional-practice terms, phases, deliverables, roles, delivery methods, AIA contract families, CSI documentation systems, or NCS at a high level. Use for questions such as “what is a CD set?” or “what does 50% CDs mean?”; do not use for code/jurisdiction, outline-spec, agreement-scope, or work-plan work.

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Research transit, walking, cycling, pedestrian infrastructure, and airport access for a site. Use for mobility, accessibility, or walkability questions tied to a location.

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Research climate, sun, flood, seismic, soil, contamination, and topography for a site. Use for environmental site analysis or hazard questions tied to a location.

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Research population, income, age, housing, and employment around a site. Use for census, demographic, or local-market questions tied to a location.

日本語の概要は準備中です。原文の説明を表示しています。

Get the transcript, on-screen text, summary, and chapters of any video or audio with Scribiz. Use when the user wants to transcribe a YouTube, TikTok, Instagram, podcast, or local media file, generate SRT or VTT subtitles, summarize a video, ask questions about a video, or turn a video into a blog post, show notes, or social posts.

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Background context for the Electronic Bookkeeping Act (電子帳簿保存法) in the shinkoku tax filing plugin. Contains requirements for electronic bookkeeping, scanner storage, mandatory electronic transaction data storage, and shinkoku's compliance status. This skill is not user-invocable — Claude loads it automatically when responding to electronic bookkeeping compliance questions.

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Display shinkoku's current capabilities, supported personas, and known limitations. Use when the user asks "what can you do?", "what's supported?", or similar questions.

日本語の概要は準備中です。原文の説明を表示しています。