「task」の検索結果

Quick read-only health check — session context, workspace mounts, tool availability, and task snapshot. Use when the user asks for system status or runs /status.

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 Gmail integration to NanoClaw. Can be configured as a tool (agent reads/sends emails when triggered from WhatsApp) or as a full channel (emails can trigger the agent, schedule tasks, and receive replies). Guides through GCP OAuth setup and implements the integration.

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。

Manus-style file-based planning for multi-step work. Keep task_plan.md, findings.md, progress.md in your run workspace so the filesystem is your working memory. Use for any research/build with 5+ tool calls. Adapted for OpenMark sub-agents from Ahmad's planning-with-files v3.0.0.

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。

HTML-first persistent planning skill. Generates a single self-contained plan.html with interactive phases, drag-and-drop tickets, sliders, mockups, and embedded JSON state. Survives /clear via session catchup, tamper-protected by SHA-256, mirrors across 17 IDEs, ships 10 templates across Thariq's 9 categories, exports back to Markdown on demand. Use when asked to "plan it", "make me an html plan", "show me the plan", "render the plan", or when starting any multi-step task that needs a navigable artifact instead of a markdown wall.

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。

Routes general Convex requests to the right project skill. Use when the user asks which Convex skill to use or gives an underspecified Convex app task.

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。

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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。

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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 wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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 the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.

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 says /update, @update, update project docs, or asks to sync task.md, changelog.md, and files.md after completing work.

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。

Routes general Convex requests to the right project skill. Use when the user asks which Convex skill to use or gives an underspecified Convex app task.

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 the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.

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。

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes. Triggers: "BatchClient java", "azure batch java", "batch pool java", "batch job java", "HPC java", "parallel computing java".

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。

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

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。

Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and 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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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。

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

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。

Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.

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。

Orchestrates Android development tasks including project creation, deployment, SDK management, and environment diagnostics using the `android` command-line tool.

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。

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

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 x402 payment execution to AI agents — per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents need to pay for APIs, services, or other agents.

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 after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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 automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, schedul...

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。

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimi...

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。