「what」の検索結果

Your For You page for content creators. Scrapes tracked competitors across social media platforms, scores what's performing, and turns it into actionable, differentiated content ideas backed by real engagement data. Use this whenever the user wants competitor/creator research, a content feed or "for you" page, trending-topic ideas in their niche, to see what's working on social, to track what creators are posting, or to generate video/post briefs from what's performing — even if they don't say "find ideas." First run walks through setup.

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Turns open-ended requests into things to think WITH instead of answers to accept. Audits the hidden assumptions inside a topic, inverts them into sharp "what if" provocations, branches each one outward, and delivers a mind map plus written wonderings. Portable version, no vendor-specific tools required, works on any conversational assistant. Fire this when the person signals they want to open up a space and think alongside the assistant: "wonder about X", "what are the weird angles on X", "help me think about what this could be", "I want to explore, not decide yet". Do NOT fire when the same topic is attached to a request for concrete, usable, or decided output: a named deliverable, a count ("give me 5 names"), a deadline, or a "which / what should I pick" question. When it is genuinely unclear which they want, this skill opens by asking one line rather than branching. Not for factual lookups or executing a plan already chosen.

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。

Turns open-ended requests into things to think WITH instead of answers to accept. Audits the hidden assumptions inside a topic, inverts them into sharp "what if" provocations, branches each one outward, and delivers a fully-expanded mind map image plus the written wonderings. Fire this when the person signals they want to open up a space and think alongside you — "wonder about X", "what are the weird angles on X", "help me think about what this could be", "I want to explore, not decide yet" — or when they invoke /wonder or /wonderpill. Do NOT fire when the same topic is attached to a request for concrete, usable, or decided output: a named deliverable, a count ("give me 5 names"), a deadline ("ideas I can ship this week"), or a "which / what should I pick" question — those want straight answers, not a wonder pill. When it is genuinely unclear which they want, this skill opens by asking one line rather than branching. Not for factual lookups or executing a plan already chosen.

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Answer questions about code, architecture, and technical decisions — no implementation. Trigger on questions asking 'why', 'what does this do', 'what is the purpose of', 'explain', 'what's the difference', 'compare', or 'what are the tradeoffs' — even when referencing specific files, code snippets, or inline code. The key signal is the user wants to UNDERSTAND something, not change it. Do NOT trigger for requests to build, fix, plan, review, research, or add/modify code.

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Learns the words a user habitually writes to their coding agent that make its answers worse ("be 100% sure", "don't assume", "full review", "are you sure?", "all fine, right?"), from what they actually typed in their recent Claude Code sessions. Shows what each one does, what went wrong after it, and what to say instead, then writes the habit words block that maps each word to the checks it should trigger, so the checks run without the words. Use when asked to learn, read, refresh or review the user's habit words or prompts, "which of my words make you worse", from setup-first-pass's profile step, or when the start-of-session check says the habit words are due.

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Code review focused exclusively on over-engineering. Finds what to delete: reinvented standard library, unneeded dependencies, speculative abstractions, dead flexibility. One line per finding: location, what to cut, what replaces it. Use when the user says "review for over-engineering", "what can we delete", "is this over-engineered", "simplify review", or invokes /ponytail-review. Complements correctness-focused review, this one only hunts complexity.

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Act as a product-manager copilot that turns evidence into product decisions and artifacts: backlogs and prioritization, PRDs and user stories, metrics and OKRs, experiments, launch plans, feedback triage, and pricing/packaging, kept in a per-product workspace. Use whenever the user wants to decide what to build, spec it, or ship it: "write a PRD for…", "prioritize our backlog", "what should we build next", "plan OKRs", "define our north star metric", "design an A/B test", "plan the launch", "triage this customer feedback", "platform or feature?", in any language ("viết PRD", "ưu tiên tính năng nào", "quản lý sản phẩm", "プロダクト管理", "产品经理"), even when they never say "product manager". Also use when the user returns to a product workspace this skill created (a directory holding `product.md`, `state.md`, and `decisions.md`). Not for deep user discovery (design-thinking), desk market facts (market-researcher), or company-level bets (strategy-board).

Generate a stage-appropriate marketing plan based on your company's MRR/stage. Uses the marketing-by-stage playbook to tell you exactly what to do (and what NOT to do) at pre-launch, early ($0-10K MRR), growth ($10-100K MRR), or scale ($100K+ MRR). Use when "what should I do for marketing", "growth plan", "marketing plan", "I just raised a round", "marketing strategy", "what's the right marketing for my stage", "GTM strategy", or any request for a stage-appropriate marketing roadmap.

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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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Structured marketing brainstorming when direction is unclear. Use when the agent doesn't know which skill to run, the user is vague about what they need, there are multiple valid marketing paths, or someone says 'I don't know where to start', 'what should we market', 'explore approaches', 'help me think through this', 'marketing ideas', 'what campaign should I run', 'where do I start with marketing', 'what's our marketing plan', or 'I have no idea how to promote this'. Explores 2-3 approaches and recommends the best path forward with a specific next-skill handoff. Even vague frustration like 'nobody knows about my product' or 'how do I get users' should trigger this when no specific channel or tactic is mentioned.

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Explain what slushpile is, what each skill does, what order to run them in, where the workspace files live, and how to set up a voice agent. Use when the user asks how slushpile works, what to run next, or how to fix a workspace problem.

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Adopt Prisma Next into a new project, onto an existing database, or as the first move after a bootstrap tool dropped you into a scaffold. Use for "what can I do with Prisma Next", "what can I do next with Prisma", "where do I start", "what should I do first", "just ran createprisma", "createprisma", "npx createprisma", "npx create-prisma", "first steps", "first query", "I have a scaffolded Prisma Next project what now"; for `pnpm dlx prisma-next init` greenfield setup; and for `prisma-next contract infer` + `db sign` against an existing database. Also covers the connect-write-read first-arc orientation, the day-to-day commands (`contract emit`, `db init`, `db update`, `migration plan`, `migrate`, `db schema`, `db verify`), and routing to `prisma-next-contract` / `prisma-next-queries` / `prisma-next-runtime` for the next move. Flags: --target, --authoring, --schema-path, --probe-db, --output.

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Generate a prioritized review queue of registry skills and catalog items that need attention. Use this skill whenever someone asks: "what needs review?", "what's overdue for audit?", "where are the weak spots in the registry?", "what should I audit next?", "run a health check on the registry", "show me flagged skills", "find problems in the registry", "what skills need fixing?", or "triage the registry". This is the triage layer — it surfaces candidates ranked P0–P4 so that focused work (via /gaia-audit) targets the right things first. Run this before any audit pass. Also invoke proactively when a PR changes registry/named/ at scale, or before a release, to catch regressions early.

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The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.

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Query and answer questions about Volcengine API specifications. Use when the user asks about API parameters, error codes, request methods, enum values, required fields, response structures, pagination, parameter dependencies, service capability lists, API availability, or API comparisons, even if they do not explicitly say "API". Typical intent includes checking what an action supports, which fields are required, what values a parameter accepts, why an API returns a specific error, how to pass nested or body parameters, how pagination works, what an API response contains, whether a batch operation exists, what services or versions expose an operation, or how two APIs differ. Use API Explorer data as the authoritative source and preserve constraints, examples, and caveats found in the spec. Answer in Chinese or English. When the user needs runnable SDK code, language-specific examples, or SDK configuration, hand off to volcengine-sdk-generator. When they need CLI operations, hand off to volcengine-cli.

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Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

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Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.

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What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).

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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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Look up NHS England genomic tests for rare and inherited diseases. Use this skill whenever a clinician asks what genetic test to order for a condition, which genes are covered for a specific diagnosis, what commissioning category a genomic test falls under (Core, Specialised, or Highly Specialised), or which tests belong to a specialty group (Neurology, Cardiology, Endocrinology, etc.). Also trigger for questions like what panel is used for a condition, whether there is an NHS test for something, what the R number is for a condition, or whether anything has changed in the genomic test directory. Source: NHS England National Genomic Test Directory for Rare and Inherited Disease v9.0 (April 2026).

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On-demand "what's new" generator. Main context (which knows the impending change) writes keepachangelog-style entries to a fragment file at `.claude/state/whatsnew/<slug>.json` (gitignored, transient). An optional per-project `project.json → whatsnew.route_workflow` names a routing workflow that consumes the fragment and renders it wherever the project wants (a site page, a release body, a docs page). Not a workflow phase — it never blocks a commit, and it never writes `CHANGELOG.md` (that file is owned solely by semantic-release in CI).

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The kpopper method: a knowledge record (GROUNDING.yaml) for work that gets revisited - what is known, where each piece came from, and what would make each conclusion wrong. Use when asked what kpopper is or how to keep a record, when choosing which kpopper skill fits a moment, or when no narrower one does. The occasions have their own skills: ground (read the record before answering), record (write what the work found), map (map existing materials), annotated-doc (a standalone HTML document with evidence), hub (the record's visual home), consolidate (hypotheses and merges), watch (background checks).

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[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.

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Verified lookup pack for crouton's schema→CRUD generation domain — the fieldsFile JSON format, the complete field-type catalog with aliases, the naming contract, the exact artifact list one generate run produces, the seed/default-layout pipeline, dialect defaults, and rollback semantics. Use when writing or reviewing a collection schema, when asking "what field types exist / what does `crouton config` actually generate / where did this file name come from / why did I get a pg schema", when a generated file's name or path doesn't match the docs, or when removing/regenerating a collection. Trigger phrases — "what field types are supported", "schema JSON format", "what files does crouton generate", "naming convention for collections", "rollback a collection", "dialect default". The generation WORKFLOW (steps + sign-off gates) lives in the `crouton` skill — this is the reference it doesn't carry.

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