「loops」の検索結果

Run a high-thoroughness end-to-end implementation loop using clean worker worktrees, per-slice planning, implementation, explicit review/fix iteration, orchestrator integration, and final commit/PR drafting. Use when the user explicitly wants a Ralph/Wiggum-style loop, autonomous multi-agent execution, a delegated feature/bugfix carried from confirmed scope through review-ready completion, or RFC-driven implementation that should be reviewed, bumped to In Progress, decomposed into phases, and executed through nested sub-loops. Supports Codex subagents by default and external CLI workers such as OpenCode when explicitly requested and available.

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。

Plan and batch-produce an entire email system for a product. Maps every lifecycle touchpoint (onboarding, activation, conversion, retention, transactional, win-back), generates all emails with quality gates, outputs Loops-ready copy. Use when "create all emails", "email system", "build email sequences", "lifecycle emails", "onboarding sequence", "set up transactional emails", "plan all the emails for [product]", or any request to systematically produce a complete set of emails for a 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。

Reverse-engineer a supplied video into a shot-by-shot generation specification, displayed evidence frames, and paired Chinese-English prompt packages. Use for requests such as 视频反推提示词, 视频转提示词, 从视频生成提示词, 视频逆向提示词, 逐镜头反推, 分镜拆解并生成提示词, 反推视频 prompt, or video-to-prompt; also use for Veo, Sora, Kling, Wan, generic prompt adaptation, and bounded generate-compare-optimize loops. Do not use for ordinary video summaries or basic transcription.

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 browser-use to turn natural-language web tasks into repeatable browser automation backed by Playwright and agent loops. This skill helps an agent open sites, inspect page state, click, type, extract data, and recover from common UI changes with a real automation framework instead of brittle one-off scripts.

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 an existing coding agent into a supervised local work assistant that connects to workplace tools, reusable skills, workflows, and scheduled loops.

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。

Start, watch, and restart a local stack of app processes together instead of juggling many terminals and ad hoc shell loops.

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 ARIS to run Markdown-based agent skills for literature review, idea discovery, cross-model critique, experiment planning, and paper-writing support.

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。

Expert support operations guidance for customer service excellence. Use when designing ticket management systems, creating SLA policies, building support tier structures (L1/L2/L3), optimizing knowledge bases, defining severity levels and escalation procedures, implementing support metrics (CSAT, FRT, TTR, FCR), configuring support tool stacks, or building support-to-CS feedback loops. Covers Zendesk, Intercom, Freshdesk, and help desk best practices.

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。

Expert growth product management guidance for SaaS applications. Use when designing growth loops, optimizing activation and onboarding, building retention systems, creating referral mechanics, running growth experiments, defining north star metrics, or implementing PLG strategies. Covers the full growth lifecycle from acquisition to monetization.

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 a weekly operating review (Friday wrap or Monday plan) — review progress against the 90-day priorities, surface stalled open loops, and set next week's focus. This is the PERSONAL operating review; distinct from /audit (checks the kit's connections/freshness) and /level-up (finds one automation to build). Trigger on "/weekly", "weekly review", "weekly planning", "how did this week go", "plan my week".

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 at the start of a working day (or when the user asks "what should I focus on today / give me my brief / daily standup"). Produces a one-screen daily brief — today's top 3, open loops that need a nudge, what's slipping, and a suggested focus order. Trigger on "/daily", "daily brief", "what should I focus on today", "morning brief", "standup".

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。

Work with the Toss Securities Open API for Korean and US stock market data, stock info, exchange rates, market calendars, account and holdings lookups, order history, buying power, sellable quantity, commissions, conditional orders, rankings, trading trends, market indicators, realtime WebSocket integration, and delegated trading workflows. Use when an agent needs to run user-delegated buy/sell/order-management loops, call or build against developers.tossinvest.com, inspect the Toss OpenAPI schema, generate client code, or operate with TOSS_API_KEY/TOSS_SECRET_KEY credentials.

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。

Aggregate feedback signals across all active loops. Reports health, trajectory, overdue checks, regression warnings, and Goodhart's Law violations.

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。

Edits existing Stitch screens with text prompts — the iteration tool. Change colors, layout, content, or style without regenerating from scratch. Supports refinement loops via output_components 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。

Use for PABCD completion loops. Triggers: cxc-loop, continue until done, HOTL, repeated PABCD, 루프 돌려, 끝까지 해줘, docs-first.

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 imp outputs differ between two paths that should agree - chunk sizes, prefill vs decode, prefix-cache resend, two engines (imp vs llama.cpp vs HF), two builds or kernels - "output changes with chunk size", "first token differs", "logprobs moved", "which layer diverges", "who is right, imp or llama.cpp", "near-tie". Do NOT use for repetition loops or garbage output (check-degeneration), a new arch that loads wrong (add-model-arch), or perf (benchmark-cuda).

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 verifying that a model in the imp inference engine produces coherent output without repetition loops, token-stuck states, or state corruption across turns or streams. Triggers on "degenerates", "check degeneration", "repetition loop", "own own own", "stuck token", "empty content", "multi-turn regression", "does it still work", "NIAH", and after enabling CUDA graphs / changing forward pass / MoE routing / KV cache or KV dtype / GDN state or scan / sparse attention / PDL / speculation (MTP, n-gram) / ragged prefill / batched-decode kernels (smallm, producer quantize) / FA2 softmax.

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。

Start and manage the full heartbeat ecosystem for agent-studio. Registers all 7 heartbeat loops plus auto-reschedule task via CronCreate to keep the agent ecosystem healthy, indexed, informed, and connected.

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。

Detect when agents are stuck in repetitive action loops using a rolling 20-action window with escalating nudges — replan after 3 similar actions, explore after 5, force-done after 8.

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 Claude Code in autonomous iteration loops until task completion — PRD-based feature development, test coverage improvement, lint fixing, documentation generation, dataset creation, or framework migrations. Uses in-session loop mechanism with specialized templates. Triggers on autonomous loop, iterate until done, run until passing, keep going until, ralph, PRD 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。

Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.

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 a SaaS founder is deciding on or building a product-led growth motion — assessing PLG readiness, choosing freemium vs free trial vs reverse trial, defining activation and PQLs, designing self-serve onboarding and conversion triggers, building viral loops, or setting up PLG metrics (NRG, activation rate, TTV, NDR, CAC payback) and cohort analysis. Also for hybrid product-led-sales design and PLG-vs-sales-led GTM decisions. Not for paid acquisition channel mix (use channel-expert) or the mechanics of price points and tiers (use pricing).

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。

Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

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 systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.

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。