「que」の検索結果

Use to structure a vague marketing request into a well-scoped brief before any real work begins. Triggers on "shape this campaign", "plan this launch", "scope this content", "marketing plan", "growth plan", "content strategy", "outbound plan", or when invoked as the /mshape slash command. Produces a filled brief (campaign, content, optimization, research, or pipeline) that downstream marketing skills can act on. Do not use for work already well-defined — go straight to execution. For engineering task shaping see prompt-shaper; for game-design intake see game-design-shaper.

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Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

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。

Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.

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。

Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

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 writing, reviewing, or architecting Rust code — systems programming, async services, CLI tooling, web backends, or any work in `.rs` files. Triggers on editing `.rs` or `Cargo.toml` files, or mentions of "Rust", "Tokio", "Axum", "cargo", "borrow checker", "lifetime", "trait object", "async Rust", "crate", "rustc", or explicit requests to "build this in Rust" / "rewrite X in Rust". For adversarial security review of Rust code see security-reviewer.

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。

Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.

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。

Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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。

This is the MistEye security gate skill. It is triggered by pre-installation risk checks (including Skill/MCP dependency manifests), pre-access security checks for domains or URLs, IoC malicious detection, and supply-chain risk blocking, especially for common requests such as "take a look at this address/website." It must use `https://app-api.misteye.io/functions/v1/detect`; in dependency and external-link scenarios it has the highest priority. If the API returns `status="malicious"` or any `matches`, the action must be blocked and explicitly reported as "blocked". Only after the first installation is complete should the user be reminded that daily patrols can be enabled with OpenClaw or Hermes (default once per day).

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。

Uses the local pruner indexer to gather relevant code context before making changes, debugging, refactoring, or answering questions about this repository.

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。

Generate synthetic code context for LLM coding tasks. Automatically use this before making code changes, fixing bugs, refactoring, or answering questions about the codebase. Provides execution paths, relevant files, symbols, tests, and code snippets.

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 an explicitly requested, unusually strict dygo maintainability audit that seeks structural simplification, removes accidental complexity, and challenges weak abstractions without changing behavior.

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。

Design, implement, or review dygo HTTP APIs and public App SDK contracts. Use for Record APIs, metadata and boot endpoints, query behavior, response envelopes, compatibility, and public Go interfaces.

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。

Implement or review dygo Studio behavior in Vue, TypeScript, Pinia, TanStack Query, Vue Router, the dygo design system, and framework renderers. Use for Studio application logic, navigation, state, API integration, and shared components.

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 or review dygo durable Jobs, Job Executions, queues, workers, retries, idempotency, and Schedules. Use for background or recurring business 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。

Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP 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。

Guide technical communication for software developers. Covers email structure, team messaging etiquette, meeting agendas, and adapting messages for technical vs non-technical audiences. Use when drafting professional messages, preparing meeting communications, or improving written communication.

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 a detailed refactor plan with tiny commits via user interview, then file it as a GitHub issue. Use when user wants to plan a refactor, create a refactoring RFC, or break a refactor into safe incremental steps.

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。

Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear, features are complex (>2 days), or involve cross-team coordination. Ask two core questions - Why? (YAGNI check) and Simpler? (KISS check) - to ensure clarity before coding.

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。

Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.

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。

Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like "make me a GIF for Slack of X doing Y".

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。

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

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。

Review failing CI checks on pull requests, diagnose root causes, and fix issues. Covers common failures: tests, linting, type checks, security scans, and builds.

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 learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.

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。

Execute comprehensive SQL injection vulnerability assessments on web applications to identify database security flaws, demonstrate exploitation techniques, and validate input sanitization mechanisms.

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。