799 件 ・ 関連度順
概要と使いどころ
Place, cancel and modify Hyperliquid orders correctly from the desk computer - limit and IOC (market-style) orders, take-profit and stop-loss trigger orders with grouping, client order ids, reduce-only, batch actions, price and size rounding, and how to read every response status. Write path - Execution Trader only, on an approved ticket. Use for any order action and for reconciling by cloid.
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 optimizing, auditing, or preparing to ship web animations or rendering performance work: frame loops, scroll/viewport reveals, mount/unmount transitions, canvas/WebGL lifecycles, reduced motion, lazy rendering, and deferred off-screen work. Use for jank, per-frame allocations, forced reflows, render loops, animations that do not pause off-screen, content-visibility, requestIdleCallback, analyzing Chrome DevTools performance traces for animation or rendering issues, or choosing between browser-driven CSS/WAAPI, minimal JS, phase, and Motion/GSAP. Use when the user explicitly asks about phase APIs or behavior. Do not use during exploratory animation ideation, prototyping, visual iteration, or trying variants, even when the code already imports phase; wait until the user asks for performance guidance, an audit, production implementation, ship-readiness review, or about phase itself.
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。
Selects and composes the Godmode skills for a coding task, explains the activation boundary, keeps workflow skills from being skipped when they materially reduce risk, and identifies the task's current engineering lifecycle state. Use when starting a task with Godmode installed, deciding which skill to invoke, recovering a task after context loss, or checking whether a workflow applies. Not a replacement for the selected skill's instructions or for repository-local rules.
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。
Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, table, and agent (metric, evaluation, trajectory, validation) monitors. Fetches the report, identifies patterns, and suggests tuning.
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。
Improve existing code without changing behavior: scan smells, simplify, dedupe, reuse utilities, and verify after edits. Use for refactor, cleanup, simplify, reduce duplication, extract method, dead code, or code smells. No PR.
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 engine-specific test helper libraries for the project's test suite. Reads existing test patterns and produces tests/helpers/ with assertion utilities, factory functions, and mock objects tailored to the project's systems. Reduces boilerplate in new test files.
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。
Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.
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。
When the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "registration friction," "signup form optimization," "free trial signup," "reduce signup dropoff," "account creation flow," "people aren't signing up," "signup abandonment," "trial conversion rate," "nobody completes registration," "too many steps to sign up," or "simplify our signup." Use this whenever the user has a signup or registration flow that isn't performing. For post-signup onboarding, see onboarding. For lead capture forms (not account creation), see cro.
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。
When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers or wants to build systems to prevent it. For post-cancel win-back email sequences, see emails. For in-app upgrade paywalls, see paywalls.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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。
Analyze Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Combines static source analysis with live run analysis via the Trigger.dev MCP tools (list_runs, get_run_details, get_current_worker).
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 Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4 weeks reduced to one strategist + AI in 5 days. By Gingiris.
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。
Confirms or rejects findings from review-llm-artifacts before deletes or risky refactors. Loads review-verification-protocol-style checks per finding. Use after a review run, when the user wants to reduce false positives, before fix-llm-artifacts on dead code, or when validating a full-project scan.
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。
Mandatory verification steps for all code reviews to reduce false positives. Load this skill before reporting ANY code review findings.
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 insurance coverage and find opportunities to optimize premiums and reduce gaps.
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。
Assess and improve cloud portability — identify lock-in, prioritize abstraction, and design migration paths. Use when asked "how locked in are we", "reduce vendor lock-in", or "plan a cloud 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。
Manage vendor relationships — vendor selection scorecard, contract review checklist, renewal tracking, and vendor consolidation audit. Use when asked to "evaluate this vendor", "review this contract", "track vendor renewals", or "reduce our SaaS spend".
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。
Operational efficiency audit — identify waste, redundancy, and friction across processes, tools, and team workflows. Use when asked to "audit our operations for waste", "where are we inefficient", "what tools are we paying for but not using", or "reduce operational overhead".
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 meeting and communication cadence — what meetings to run, how often, who attends, and what decisions they make. Use when asked to "design our meeting structure", "what meetings should we have", "reduce meeting load", or "build our operating rhythm".
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。
Audit cloud infrastructure costs and produce a concrete optimization plan with specific changes and estimated savings. Use when asked to "how much is this costing", "reduce cloud spend", "cost optimization", "are we overpaying", "cloud bill", or "budget for this infra".
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 onboarding playbook - day 1 through week 4 checklist, access provisioning, context transfer, and success milestones. Use when asked to "build an onboarding process", "how do we onboard new hires", or "reduce time to productivity for new joiners".
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 an edge routing and geo-distribution strategy — latency routing, failover, and edge logic. Use when asked to "geo-route our traffic", "reduce global latency", or "design edge failover".
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 customer support onboarding flow -- first-contact experience, proactive support touchpoints, and setup success checklist. Use when asked to "design our onboarding support", "how do we support new customers during onboarding", or "reduce early churn from setup 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。
Design cost reduction strategies — model tiering, prompt compression, caching, batch inference. Use when asked to "reduce our AI costs", "set up model tiering", or "cut LLM spend".
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。