「what calls」の検索結果

Sets direction, allocates capital and attention, and makes the calls no one else can make. Use this when a decision spans more than one function, when priorities conflict and something must be cut, when a plan needs pressure-testing before commitment, or when the question is what the organization should do rather than how to do it. Also use to route a request to the right executive when it is unclear who owns it.

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Audits and rewrites landing pages, homepages, and sales pages to increase conversion — diagnosing why a page is not converting, rewriting headlines, hero copy and calls to action, and prioritizing which changes to test first. Use this when a page underperforms, when someone pastes a URL or screenshot asking what is wrong, when writing a new page, or when deciding which conversion changes are worth testing.

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Detects overlooked, underrepresented, weakly resolved, or poorly validated populations and subgroups within a biomedical research area so users can identify more precise and meaningful study populations. Always use this skill when the real question is not just what is under-studied, but which populations, strata, or subgroups are missing, thinly represented, superficially analyzed, pooled without resolution, or insufficiently validated in the current evidence base. Focus on meaningful subgroup gaps rather than generic calls for diversity.

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House style for user-facing documentation — voice, scope, structure, and what to leave out. Use this skill whenever writing, editing, or reviewing anything a user reads to learn how to use a tool — help center articles, getting-started guides, tutorials, feature docs, README usage sections, in-app help, release notes, or FAQ entries. Trigger it even when the request is phrased plainly — "document this feature", "write docs for X", "explain this to users", "write a README for this library", "turn these notes into a guide" — and even when no style guidelines are mentioned. Also use it to review existing docs for tone, bloat, or leaked implementation detail. Do not use it for internal engineering docs, architecture write-ups, RFCs, or code comments.

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Use when asked 怎么给老师发消息, 跟班主任沟通, 孩子在学校被欺负怎么跟老师说, 对老师的做法有意见, 请假怎么写, 家长群里怎么说话, or write a message from a parent to a teacher in China. Produces a ready-to-send WeChat or note message in Chinese, polite and specific, for the situation (asking, leave, a concern, a disagreement, bullying, thanks), a calmer version if the parent is upset, what to say if the teacher calls back, the escalation path if it is not resolved, and 家长群 etiquette.

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Decide which of your recurring rituals to put on autopilot — and which to keep manual. Use when asked what to automate, how to set up recurring AI runs, which reports or briefings could run on a schedule, or to design an automation charter for a team. Produces a ritual inventory with automate/assist/keep-manual calls, guardrails per ritual, and a rollout order.

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Use when starting or appending to a modpack project dev-log. Creates <project>/docs/dev-log.md if absent; appends a dated entry on subsequent calls. Triggers - 'log this', 'add to dev log', 'record what I did', 'note this change', 'devlog'.

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Analyze a creator, influencer, founder, competitor, or brand social account to explain its positioning, content pillars, winning posts, hooks, formats, series, calls to action, and audience response. Use when the user wants to understand why an account works and what patterns to test without copying it.

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Recreate a static graphic ad (Pinterest pin, IG/FB feed image, poster) from a reference image, swapping in a new brand's product and new copy while keeping the reference's layout, composition, and visual energy. ALWAYS generated with GPT Image 2 in edit-the-reference mode (fal-ai/gpt-image-1/edit-image, a billed FAL generation); the HTML/goose-graphics overlay is only an optional text-finishing step, never the generator. The static-graphics counterpart to the video remix-ad skill; this is what the app calls when a user picks a reference ad and wants it for their own product.

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Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.

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Drives a multi-turn conversation with a LiveKit agent running locally to see what it does. Use when the user says "test my agent", "try my agent", "does this work", "why did it call that tool", "it says the wrong thing when I ask X", "test this change", or whenever you have edited an agent and need to check how it behaves. Wraps `lk agent debugger`: start the agent in text mode, send turns, read the tool calls, handoffs, errors and logs behind each reply, and restart after an edit. It runs without audio or a LiveKit room at one LLM call per turn, so it is the preferred way for a coding agent to live-test during development, and the default when the user says "test" without naming unit tests or simulations.

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Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.

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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

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Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.

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Before removing code that looks redundant, wasteful, or "wrong" based on log observations or code review instinct, grep the test suite for tests whose names describe the exact behavior you're about to remove. A test whose title literally describes the "weird" thing (e.g. "calls play() even when player reports playing", "retries even when first call succeeded", "skips cache even when fresh") is a load-bearing workaround, and the production log you're reacting to is probably the workaround working as designed — not a bug. Use when: (1) Tempted to remove a function call that "looks redundant" based on a production log, (2) About to "simplify" a conditional that seems to always take the same branch, (3) Reviewing code and thinking "why would anyone write this?", (4) A log line shows a state that seems impossible or contradictory (e.g. playing=true but positionMs=0 for seconds), (5) CI surfaces a failing test whose name describes exactly what your fix removed.

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Write outbound promotional copy for a product or project: launch and update posts for community platforms and social media, store page descriptions and short blurbs, landing page headlines and calls to action, press-style announcements, and the naming of a product for another language or market. Covers what may be disclosed publicly, keeping claims traceable to shipped changes, and writing a headline that carries information instead of hype. Use when drafting or revising anything aimed at people who do not use the product yet, and when deciding whether a link, key, price, or unreleased detail can appear in public copy.

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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"

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Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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Prepare a meeting-ready alternative-investments brief for a client, household, IRA, trust, or named account before a review — pulls the alts book from iCapital or Addepar (positions, NAV vs. commitment, called and unfunded amounts, contributions/distributions/subscriptions/redemptions, lockups, and the platform's own IRR/TVPI/DPI/RVPI) and sits it beside the liquid book and IPS target from the connected portfolio system (Orion Connect, Addepar, or Envestnet), then flags cash needed for capital calls, alts % vs. target, concentration, unfunded concentration, and stale as-of data. Cites the source platform on every figure and keeps disclosures on the page. Does not recommend funds, forecast NAVs/IRRs, or simulate allocation changes. Triggers on "alts brief", "/alts-brief", "prep the [client]'s alts review", "what's going on with [client]'s PE/private investments", "how's [client] doing on their alts", or any request to review a household's alternative investment positions before a meeting.

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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"

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Update IWE with change explanations. Agent calls update.sh, parses CHANGELOG, explains what changed, helps adapt updates to user customizations.

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Evaluate alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.

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Gives Claude Code operators a live dashboard for multi-agent sessions, tool calls, file activity, and nested task progress so debugging starts from what the agents are actually doing.

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Use when the user asks 'is my site secure', 'run a security audit', 'check for vulnerabilities', 'was my site hacked', or 'fix the vulnerable plugins'. Runs the Respira security audit, reads its indicators, coverage and known vulnerabilities honestly, never calls a partial scan clean, and fixes what it finds one approved step at a time.

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