「step」の検索結果

ALWAYS invoke this skill when work wraps up or the user asks how it went - "done or not", "are we done", "what happened", "report", "what's the status of this ticket" - in any language, and when a Stop hook asks for a session report. Produces a ten-line plain- language report: a lead, a checkmark table, and a verdict - fully done, anything needed from you, new debt, safe to close. Every "yes" names its proof; unverified says "not checked". Saves the report so the next session starts from it instead of re-exploring the repo.

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。

ALWAYS invoke this skill before asking the user any technical question or offering options, and whenever they ask to be asked in plain words - "ask simple", "ask me simply", "ask me in plain words" - in any language. ALWAYS invoke it too when the user asks for your view on a technical choice: whether it is worth doing, more than the problem needs, or replaceable by something simpler, and which option you would take. Invoke it even when the code makes the answer look obvious: the checks are what make the answer more than a guess. Rewrites the question in plain words and always ends with one marked recommendation. A structural choice first passes six checks, shown as a table: effort now, simpler substitute, extra work later, lock-in, over-engineering, easy to undo. Doing nothing is always weighed.

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。

ALWAYS invoke this skill before anything hard to undo gets agreed to - a contract, a purchase, a migration, a launch, a price change, a reorganisation - and whenever the user asks "what could go wrong", "what are we missing", "poke holes in this", or for a premortem or a red team, in any language. Assumes the decision already failed and works backwards to find out why, in a fresh agent that had no hand in making it. Sweeps nine areas and shows what each produced, including the empty ones. Ends on one verdict: go ahead, go but fix these first, try it small first, think again, do not do this.

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。

ALWAYS invoke this skill when the user asks where the project as a whole stands - "where are we", "what's the big picture", "what have we built", "what is in this project", "map the project", "what does this thing even do", "what is stale", "what can we delete", "update the roadmap" - in any language, and whenever a session report has just been written. It keeps one file, BIG-PICTURE.md: what the product is, every feature with how far it got, which parts nobody has touched, and what is queued next. Age and wiring are measured from git; how far a feature got comes from the reports or says "not checked". Where a tracker is connected it offers to open the queue as tickets, after a yes and never before. Never invents work, never deletes code.

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。

Incrementally upgrade React Native Storybook across the supported migration paths. Use when upgrading `@storybook/react-native` projects from 5.3.x to 6.5.x, 6.5.x to 7.6.x, 7.6.x to 8.3.x, 8.x to 9.x, or 9.x to 10.x. Detect the currently installed Storybook version, choose only the next migration step, update dependencies and config, regenerate `storybook.requires`, convert remaining `storiesOf` stories to CSF during the `6.5.x -> 7.6.x` upgrade, and stop after a single version hop instead of jumping multiple major versions at once.

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。

Reads existing brand DNA, keywords.csv, and prompts.csv, then produces a plan.csv — a strictly-schema'd content architecture telling the next pipeline step which pages to create, for which keyword/prompt clusters, and in what build order.

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 when the user asks "what's on my plate today", "catch me up", "morning brief", or wants a summary of their day across calendar, reminders and email. The single most common multi-step automation. Powered by LMCP on macOS and Windows.

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。

Coordinate multi-step work across services the user has authorized through Aident Loadout, using live action schemas, connection checks, cost preflight, and explicit approval for side effects.

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 one prompt through headless Codex and capture the result. Use when: wanting a one-shot `codex exec` run or CI step. Not for batches or retries.

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。

Save provenance, decisions, phases, context policy, and next steps as kernel.handoff/v1. Triggers: handoff, save, pause, context, continue later.

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。

Checkpoint durable decisions, fixes, constraints, results, and open next steps from the current Codex task into scoped Waggle project memory.

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 cold email and run outbound email campaigns from an agent with Sendsets. Connect or check sending mailboxes, turn a lead list (CSV or inline) into a multi-step email sequence with follow-ups, validate merge fields, send a test, launch, and read and answer replies. Use when the user asks to send cold emails, email a list of prospects or leads, run an outreach or outbound sequence, or follow up with contacts by email.

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 for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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 the `sendsets` CLI to drive SendSets as a signed-in user - sign in with `sendsets login`, run `sendsets doctor`, then connect the app (`connection`), add or test mailboxes and their warmup (`mailbox`), send product events (`events send`), and build, validate, test and launch campaigns, including app_action and wait_for_event steps, on the hosted service or any self-hosted instance. Use whenever the task is to set up or operate SendSets from a terminal or a script and a `sendsets` binary is available, with no dashboard. For instance recovery and accounts (database-level), use sendsets-ops instead.

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。

Anti-slop skill for PRODUCT UI - dashboards, data tables, forms, multi-step flows, settings, list/detail, app shells. The agent reads the surface, budgets the frame first, and ships dense interfaces that are correct at every edge case (overflow, long labels, empty/error/loading states, i18n expansion, keyboard/a11y). House-system-first; maps to Carbon/Polaris/Atlaskit/Fluent/Primer/Material3/Radix-shadcn/Ant. The counterpart to taste-skill, which owns landing/portfolio/marketing.

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 when strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

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 when running the final pre-submission preflight for an Academy of Management Review (AMR) manuscript — ScholarOne portal, AOM format and length, abstract, anonymization for review, references, ethics/originality, and required files. Theory-only journal; there is no data-availability or replication step.

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 when planning around the two-step proposal→invited-review process of the Academy of Management Annals (Annals) — the twice-yearly proposal cadence, double-blind proposal review, the editor-only full-review development, and timelines. Explains the process; it does not write the proposal (amann-proposal-framing) or run the portal preflight (amann-submission).

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。

Web scraping, crawling, and AI-powered answer extraction at scale

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。

Research a company or idea, define the Ideal Customer Profile, and route to the right next step — either mapping the TAM or finding leads/prospects directly. The entry point for any "find me leads", "map my market", or "who should I sell to" request. Auto-loads when a user provides a company URL or idea and asks for leads or market mapping.

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。

Render a designed 'value prop' video from a config — 3-5 noun-phrase benefit claims (<=4 words each) revealed sequentially over per-SKU product visuals, one crisp editorial frame per claim (hook sticker -> N claim beats -> brand end card). Deterministic PIL/HTML beat renderer frame-stepped via Playwright and encoded with FFmpeg, sound-off legible, hard cuts, uniform pacing. FREE (no paid calls); music is added separately (create-music-elevenlabs). Use for the value-prop format.

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。

Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.

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。

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.

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。

Burned-in captions for a finished vertical video, three kinds. transcribe.py gets word timings from the video's own audio through the GooseWorks proxy (fal Whisper, bills the Ads agent, cents); captions.py burns one to three words at a time with Pillow + ffmpeg (no libass needed), either pinned to a split-screen seam (plate 25% above / 75% below) or at a fixed height, in a plate, outline or one-word serif style, with an optional red hook card; plates.py burns per-beat caption blocks (black, one union silhouette, placed in the emptiest band) for formats with no voice. The last caption (the CTA) holds to the final frame. Use as the last step of any video ad.

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。