「the」の検索結果

Runs the owner's Monday morning briefing as a two-link chain — business-pulse for the cross-connector snapshot of cash, sales, and pipeline, and report-builder for any saved KPI reports the owner has already defined — then reads the week ahead straight from Calendar and mail and merges all three parts into a single one-page Monday document, delivered per the owner's stored output preference. Requires no connectors and degrades gracefully, building from whatever of the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), Calendar, DocuSign, Expensify, Gmail or M365, HubSpot, PayPal, Ramp, RingEx Chat, Shopify, Slack, Stripe, and TikTok Ads is connected, or from pasted and uploaded data when none are. Trigger on "Monday brief," "start my week," "what do I need to know this week," "weekly briefing," "catch me up before Monday," or when the owner schedules a recurring start-of-week update.

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 the owner's content operation as a standing thing rather than a one-off: keeps a rolling posting calendar, learns the owner's voice, generates on-brand Canva graphics, writes captions that sound like them, repurposes one asset into every channel format, and stages posts for approval instead of publishing on its own. Pulls product photos and prices from Shopify when connected; with no connectors, drafts the calendar and asset briefs for manual posting. Use this whenever the owner talks about posting, content, or social — including "I need to post more," "we've gone quiet on Instagram," "what should we post this month," "make the content," "give me a month of posts," "write this in my voice," "repurpose this," or "set up our content calendar." Reach for it for a single post as readily as for a full quarter. One handoff: a finished, approved campaign brief goes to canva-creator; this skill owns the standing operation.

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。

Suggest, pick, or vet a business, startup, or product name that stays true to the founder's cultural origin while working professionally in the markets they want to sell into. Use when someone is naming a company, brand, or product and cares about how it lands across languages and regions — for example a name that sounds right at home but might read oddly to English speakers, or an authentic name they want to check before committing. Trigger this for any request about choosing a business name, checking if a name "works" abroad, spotting bad meanings in other languages, or making a name sound trustworthy in a specific market — even if the person doesn't say the word "skill".

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。

Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool 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。

The project's Files under `/drives`: the user's own folder (their desktop, also at `~/Desktop`) and the folders shared with you, synced with the Kortix web app and every other session. Load before you read or save a file the user should keep, look for the user's notes, preferences or context, keep memory across sessions, or when you see a file named `... (conflict <date> <time>)...`.

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。

How to do feature work in this repo with git worktrees via `pnpm worktree`. Each worktree gets its own branch, its own block of app ports, its own node_modules, and by default shares the primary checkout's standard local Supabase DB for fast setup; pass `--db` for its own database (a copy of the primary inside the same Postgres) when the work needs migrations or destructive data changes. Load WHENEVER starting a feature, bugfix, refactor, experiment, or any change you'll want to run/test in isolation; whenever the user mentions worktrees, isolated/parallel dev instances, running multiple branches at once, or 'spin up a worktree'; and whenever you need the exact non-interactive `pnpm worktree` commands and flags. Enforces: one canonical branch per objective, one worktree per branch — join the existing worktree instead of creating another.

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。

Agentic end-to-end tests with e2e, the e2e runner. Covers scaffolding e2e.config.ts, picking the Playwright browser engine or the agent-device mobile engine, starting the app under test from the config, driving flows with agent.act, judging with agent.assert, agent.waitFor, and agent.extract, pinning values with screen, app, browser, and expect, shaping the agent (context, system prompt, tools, personas), the replay cache, the e2e CLI, reading .e2e/report.json, and bug bashes (parallel explore runs proven with repro tests). Use when a project depends on e2e, when asked for end-to-end, browser, mobile, or agentic UI tests, to bug bash or hunt for bugs, or when an e2e run fails.

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 continual-harness refinement protocol — how an agent inspects its recent trajectory for failure signatures and improves its own harness (agent prompts, sub-agents, skills, memory) in place, mid-session, landing changes on the session branch immediately and promoting them to `main` only through a change request. Load this skill when a refinement prompt arrives mid-session, when the `harness-reflector` agent runs, or whenever you decide your own scaffolding needs repair. The sub-agent fan-out in this skill is for the top-level `harness-reflector` run only; if you loaded this as a subagent, do the four passes yourself and skip the fan-out.

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。

Develop, build, and debug the LangBot core backend and web frontend. Use when working inside the LangBot repository — backend (Python/Quart, src/langbot/pkg), the Vite/React web UI, HTTP API controllers/services, Alembic migrations, or the MCP server. Covers the dev environment (uv, pnpm), repo layout, the API auth model (user token / API key / global key), adding API endpoints, and the rule that API changes must update the MCP server and skills. Triggers on "langbot backend", "langbot dev", "langbot api", "add langbot endpoint", "langbot migration".

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。

Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.

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。

Turn a Wren project's context layer into a shareable, browser-side GenBI web app and deploy it to the user's Vercel or Cloudflare account. Orchestrates the full flow: `wren genbi build` returns a project-hydrated build instruction, the agent authors the app from scratch into apps/<name>/, then register → verify → deploy produce a shareable URL. Use this skill whenever the user wants to: build a dashboard from their Wren project, make a shareable analytics app, deploy their context layer as a web app, host a GenBI app on Vercel or Cloudflare Pages, or asks for a 'genbi app'.

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。

Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof.

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。

Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions 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。

Starts a CARLA UE 5.8 server from a ue58-dev tree — the editor binary in -game mode (the working headless server), the packaged shipping build, or the full editor — with offscreen/nullrhi rendering, custom RPC and streaming ports, and native ROS 2 via the single-dash -ros2 flag. Probes a running server, and stops one cleanly despite the 15-character process-name truncation. Use when the user asks to "run/start CARLA UE5", "launch a ue58 server", "run headless", "publish ROS 2 topics from UE5", or a client cannot reach the simulator.

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 CARLA on Unreal Engine 5.8 from the ue58-dev branch using its CMake build system — configure a preset, then build the carla-unreal / carla-unreal-editor / carla-python-api-install / launch targets — with ROS 2, DLSS, RSS and cook-scope options, and the engine-side build of the CarlaUnreal UE 5.8 fork. There is no Makefile and no Util/BuildTools in this tree, so every UE4 `make` recipe is invalid here. Use when the user asks to "build CARLA UE5", "build ue58-dev", "compile CARLA with cmake", "rebuild the Python API", or hits a CMake configure or target failure.

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 the Autoware autonomous-driving stack against CARLA on UE 5.8 over CARLA's native ROS 2 interface — no bridge process. Reports the DDS contract (rt/vehicle/status/* out, rt/control/command/* in), the Autoware-only sensor blueprints, generates the lanelet2 + point-cloud map artifacts Autoware needs instead of an .xodr, and drives the two shipped modes (classical NDT stack, or the camera-only VAD end-to-end model). Use when the user asks to "run Autoware on CARLA", "connect an AV stack", "drive the ego from Autoware", "generate a lanelet2 map", or asks which topics CARLA exchanges with Autoware.

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。

Imports FBX meshes into CARLA on UE 5.8 as spawnable props — drives the editor headlessly to import the mesh, derives the prop size from its measured bounds, and registers it in PropParameters.json so it appears in the blueprint library as static.prop.<name>. Handles the FBX unit-scale problem, verifies the result against a running server, and can revert cleanly. Use when the user asks to "import a prop", "add a custom mesh/object to CARLA", "make my model spawnable", or a prop imports but never shows up in the blueprint library.

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。

Directly drives a specific vehicle on a running CARLA server — raw VehicleControl (throttle/steer/brake/reverse/hand-brake), Ackermann speed control, vehicle lights, opening/closing doors, and physics-control tuning; ros-info reports the native ROS 2 command topics (vehicle_control_cmd, ackermann_control_cmd) for the hero. Targets the ego by default. Use when the user asks to "drive/steer the car", "accelerate/brake/reverse", "set a target speed", "turn on the headlights/blinkers/brake lights", "open a door", "drive the car from ROS", or "change the vehicle's mass/drag/physics". This is manual control (autopilot off) — for TM-driven traffic use control-traffic.

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。

Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

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。

Required before any hive-browser CLI command. The browser is driven from the terminal by running `hive-browser <command> ... --json` via terminal_exec — not via MCP tools. Teaches the browser lifecycle rules (the bridge attaches to the USER'S running Chrome — never kill or launch browser processes; timeouts are transport issues, not crashes), the screenshot + coordinate workflow (hive-browser interact with a fractional coordinate) that reaches shadow-DOM inputs selectors can't see, the viewport-fraction coordinate rule (not pixels), rich-text editor quirks ("send button stays disabled" failures), and CSP gotchas. Covers Chrome via CDP through the GCU Beeline extension.

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 the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack for a new platform.

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。

Create, convert, read and edit Office documents locally with GenOffice's command line. Build a new presentation (pptx) from a brief through a checked outline, per-page spec and render pipeline, a new spreadsheet (xlsx) from CSV or JSON data with formulas, a new Word document (docx) from Markdown or HTML, or a PDF; convert between pdf, docx, xlsx, pptx, md, html and csv; read the structure and text of an existing file (including the user's own docx / xlsx / pptx as source material) and apply structured edits to it. Use whenever the user asks for a slide deck, presentation, spreadsheet, workbook, report, Word document or any real Office file, a format conversion, a rewrite of part of an existing document, or wants the result opened in the GenOffice editor. Documents are processed locally; only search, image and media send the query or the referenced file to the provider configured in GenOffice.

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。

Add regression test cases to the Bifrost provider harness (the Postman collection run via `make run-provider-harness-test`) based on a merged PR or a GitHub issue. Fetches the PR/issue, traces the affected wire path in the codebase, checks existing harness coverage, designs cases following harness conventions, inserts them into tests/e2e/api/collections/provider-harness.json without reformatting the file, and validates via the augment and filter scripts. Invoked with /harness-test-writer <PR# | issue# | URL> or /harness-test-writer (prompts for a reference).

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。

Hunts for the other instances of a bug already found — the variants of one root cause across a codebase. Use immediately after a vulnerability, logic bug, or bad pattern turns up in a specific file and the question becomes where else it occurs, including the bare conversational form ("are there others like this?", "is this the same bug?"). Also for generalizing one known instance into a CodeQL or Semgrep query for its whole pattern family, and for triaging a set of look-alike candidates against a known root cause. Not for initial discovery with no bug in hand.

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。