「how to」の検索結果

Use when the user has a fuzzy idea and wants to shape it into a concrete project spec before planning or building. Triggers on: "brainstorm this", "I have an idea for...", "help me think through this project", "what should I build", "spec this out". Also catches vague feature descriptions needing structured questioning to clarify scope. Does NOT write code, plan implementation, review strategy docs, or run strategy interviews — produces a WHAT/WHY spec through dialogue, not a HOW plan.

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。

Conduct rigorous, citation-backed desk market research: quick go/no-go market scans, market sizing (TAM/SAM/SOM), competitor analysis, customer demand signals, and trend/macro (PESTEL) analysis. Use this skill whenever the user wants to research, size, validate, or enter a market — "is there a market for…", "how big is the market for…", "who are the competitors of…", "should I build/launch/sell…", "validate this business idea", "market research for…", "market entry" — in any language and for any country's market (e.g. "nghiên cứu thị trường", "étude de marché", "市場調査", "análisis de mercado"), even when the user never says the words "market research". Also use it when another skill or workflow (strategy-board, design-thinking, a product brief) needs external market facts delivered into its fact base or research folder.

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。

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

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。

A/B test design — produce an experiment spec with hypothesis, primary metric, MDE, sample size, run time, and decision rule. Also determines when NOT to A/B test and what to do instead. Use when asked to "design an A/B test", "should we test this", "experiment design", "how do we know if this works", "what's the sample size", or "set up an experiment".

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。

Build onboarding playbook - day 1 through week 4 checklist, access provisioning, context transfer, and success milestones. Use when asked to "build an onboarding process", "how do we onboard new hires", or "reduce time to productivity for new joiners".

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。

Run human-to-agent migration - audit which roles can be agent-assisted or replaced, design the transition playbook, and manage the offboarding of displaced roles. Use when asked to "which roles can agents replace", "how do we transition to AI-first ops", or "build our agent migration plan".

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。

Close a specific deal — diagnose why a deal is stalling, answer an objection reply, write a tailored proposal, design the closing sequence, or navigate procurement. Use when a specific deal is stuck, "how do I close this", "write a proposal for this customer", "help me get to yes", or "how do I answer this objection".

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。

Hacker News post crafter — given a product, feature, or story produces a ready-to-post HN submission (title ≤80 chars, no marketing), honest body text with technical depth, no outbound links, predicted reception analysis, and comment-response templates for likely pushback. Use when asked to "write an HN post", "craft a Show HN", "prepare our Hacker News launch", or "help me post on HN".

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。

Developer relations playbook builder — produces a DevRel program design covering community platform, contributor program tiers, ambassador criteria, event strategy, and success metrics. Use when asked to "build a DevRel program", "design our developer community", "create a contributor program", "start a developer advocacy program", or "how do we grow our dev community".

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。

Design ticket triage system -- routing rules, priority tags, queue structure, and first-response automation. Use when asked to "design our ticket routing", "how should we tag tickets", "set up our helpdesk", or "reduce time to first response".

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。

Provides quick-start guidance and a unified entry point for Vector features, SDK usage, and integrations. Use when users ask how to work with Vector, its TS SDK, features, or supported frameworks.

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。

Practical argument analysis and construction -- how to identify, evaluate, reconstruct, and build real arguments in natural language. Covers argument identification (premises, conclusions, intermediate steps), charity and steel-manning, argument mapping, the distinction between deductive validity and inductive strength, burden of proof, and the rhetorical context in which arguments operate. Use when the goal is to engage with real-world argument rather than to formalize it.

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。

The logical structure of mathematical proof -- how first-order logic, natural deduction, and semantic reasoning combine to produce rigorous mathematical arguments. Covers the logical skeleton of direct proof, contraposition, contradiction, cases, and induction; the role of definitions, lemmas, and theorems; how informal mathematical prose maps to formal deductive structure; and the difference between proof-as-object and proof-as-activity. Use when the question is "why is this a valid proof?" rather than "what proof technique applies?"

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。

Analyzing patterns of continuity and change over time in historical contexts. Covers periodization (how and why we divide history into eras), identifying turning points, measuring rates of change, and recognizing long-term patterns and trends. Use when analyzing temporal structure in history, debating period boundaries, or assessing whether a moment represents genuine transformation or surface-level disruption.

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。

Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

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。

Networking fundamentals as they apply to cloud infrastructure — virtual networks, subnets, routers, NAT, floating IPs, security groups, VLAN/VXLAN segmentation, load balancing, SDN concepts, and how Neutron-style cloud networks sit on top of physical topology. Covers TCP behavior at scale, congestion control, tail latency, overlay networks, and the operational gotchas that come from network layering. Use when designing cloud network topology, debugging cross-AZ latency, or reviewing a proposed VPC/SG design.

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。

Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is.

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。

Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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。

Decide which plugin component type to use and how to organize components at scale. Covers the component lifecycle (discovery and activation phases), decision framework for choosing between commands, skills, agents, hooks, and MCP servers, and organization patterns for each component type. Use when asking "which component type should I use", "command vs skill vs agent", "when to use a hook vs MCP server", "component lifecycle", "how to organize plugin components", "plugin structure patterns", or "scale a plugin with many components".

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。

Quantitative cost measurement for technical research — token injection costs, payload sizes, context window consumption, and file-level counts from actual repo files. Use when a technical-researcher orchestrator needs the cost dimension of adding or changing something: how many tokens will it inject, how big are the artifacts, what is the context window impact? This is NOT blast-radius analysis (which files break) — it covers size, tokens, and performance cost only.

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。

Builds or audits a literature review, producing an evidence map, the closest prior work, source clusters, a gap verdict, and a synthesis plan for the introduction. Use when the user asks whether a contribution is novel, who has studied a question, what the literature establishes, or how to organize a review, or wants a reading list, Zotero export, or pile of papers turned into one. For systematic reviews or meta-analyses it produces the protocol scaffold and routes registration (PROSPERO or OSF) and screening to dedicated tools.

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。

Use whenever the user wants to find, shortlist, vet, or enrich US web development firms — building, refreshing, or rebuilding marketing sites, landing pages, ecommerce, WordPress/Webflow/Shopify, headless CMS, microsites, and web frontend work. Triggers on "find a web developer for a marketing landing page", "shortlist three Webflow agencies in California", "rebuild our ecommerce site on Shopify", or "pull contact info for these 8 web dev shop domains", even when described indirectly (redesign and rebuild our site, ship a microsite). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for custom backend/API/mobile/internal-tool work — anything beyond a website. Defer to find-marketing-agency when scope spans broader marketing beyond the build. Skip in-house web-engineer hires, "how do I build X" DIY questions, hosting/CMS-product comparisons, non-US firms, individual freelancers.

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。

Remove the AI tells readers react to in a Facebook Page post: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, announcement auto-pilot. Also --mode audit (under-80 sweet spot, hook, engagement bait), --mode profile (learns how you write) and --mode interview (fills your Story Bank: the stories, scars and numbers posts are made of). Not for writing from scratch (use fb-post-writer). Keywords: humanize, de-AI my post, interview me, audit before posting.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for GraphQL schemas, persisted queries, RPC manifests, generated clients, OpenAPI drift, hidden operations, and contract-to-handler mismatches. Use when the user asks to inspect GraphQL or RPC requests, compare client contracts to live handlers, recover hidden operations, trace generated clients, or explain how schema or contract drift produces the decisive behavior. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。