「tip」の検索結果

Parallel agent orchestration for OST exploration. Fan-out multiple solution explorations, fan-in results to compare and select winners.

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。

Reviews startup financial models for investor readiness: validates unit economics, stress-tests runway scenarios, and benchmarks metrics against stage-appropriate targets. Use the full review, about 10–15 minutes, when the user provides a financial model (Excel, CSV, or a model pasted as text) and asks for review, validation, runway analysis, or unit-economics scoring. For a single money question with figures to compute, such as 'how long do I have?' or 'is a 4x burn multiple bad?', use its shorter quick check, so the answer comes from the calculator and dated benchmarks, not mental arithmetic.

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。

운영 중인 소상공인(가게·점포·자영업자)을 위한 지원사업 조사 스킬. 소상공인24·기업마당 공고를 전수 수집하고 가게 프로필(업종·지역·업력·직원수·매출)로 자격을 판정해 "오늘 신청할 것"부터 보고한다. 보조금·바우처, 정책자금(대출·보증), 교육·컨설팅 전 범위. 사용자가 "우리 가게 지원사업", "소상공인 지원금", "정책자금 알아봐줘", "가게 시설개선 지원", "사장님 지원 뭐 있어", "소상공인24 조사", "폐업/재기 지원" 등을 요청하면 이 스킬을 사용한다. 재조사("새로 나온 거 있나", "지난번 이후 뭐 올라왔나")는 diff 모드로 사용. 반대로 "우리 아이템", "창업지원", "예비창업", "K-Startup", "TIPS", "R&D 과제", "입주공간", "액셀러레이팅" 등 아이템·프로젝트 기반 창업지원 탐색은 이 스킬이 아니라 ir-search를 사용한다. 신호가 섞이면(예: "온라인 셀러 지원금") 어느 쪽인지 한 번 묻는다. 사용자 신분보다 요청 목적이 우선이다 — 가게 사장이라도 신규 기술 아이템 R&D를 찾으면 ir-search. 한국 전용.

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 code/PR/diff review and refactor audits. Triggers: review this, before merge, antipattern, 리뷰, 코드 리뷰, 머지 전에 확인.

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。

Unified entry point for creating or updating OpenCode tools (skills, agents, commands) with dependency awareness. Orchestrates the creation of multiple related artifacts in a single session. Use when user says "create opencode tool", "new opencode tool", "oc-tool-dev", or wants to create/update an OpenCode artifact and may need supporting artifacts.

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 fund economic models with sensitivity across deployment pace, exit multiples, and fee/carry structures for LP and GP returns. Use when modeling fund economics, projecting LP net returns, or analyzing fee-adjusted performance.

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 probability-weighted valuation models with multiple exit scenarios, timing assumptions, and risk-adjusted returns. Use when building growth equity valuations, modeling scenario-weighted outcomes, or analyzing risk-adjusted returns.

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。

Calculates borrower debt capacity with cash flow coverage, leverage multiples, and stress-tested servicing ability. Use when sizing debt facilities, analyzing leverage capacity, or determining optimal capital structure.

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。

Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes.

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 CodeQL and Semgrep static analysis with SARIF output for vulnerability detection, code quality assessment, and security compliance scanning across multiple languages.

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。

Git-aware smart revert for tracks, phases, and tasks. Handles rewritten history, finds related commits, and provides safe rollback with multiple confirmation gates.

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。

Structured interactive questionnaire framework for gathering requirements from users. Uses A/B/C/D/E multiple choice patterns with additive vs exclusive question classification.

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。

Concurrent investigation of independent failures. Use when multiple unrelated issues need parallel resolution.

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。

Byzantine consensus voting for multi-agent decision making. Implements voting protocols, conflict resolution, and agreement algorithms for reaching consensus among multiple 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。

Xiaohongshu (Little Red Book) infographic series generator with multiple style options. Breaks down content into 1-10 cartoon-style infographics. Use when user asks to create "小红书图片", "XHS images", or "RedNote infographics".

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。

Each routine does one thing and does it well - extract when routines have multiple responsibilities

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 multiple Claude agents to investigate and fix independent problems concurrently

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, implement, or improve an internationalization and localization strategy for UI content, formatting, and regional adaptation. Use when the user asks to add i18n, localize, translate, support multiple languages, handle regional formats, manage locale switching, or build a multilingual product.

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 EZ_math_model model selection is unclear after the modeling decision tree, the problem spans multiple domains, or modeler needs several candidate approaches before writing modeling_plan.md.

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。

Prevents premature execution on ambiguous requests. Analyzes request clarity using 5W1H decomposition, surfaces hidden assumptions, and generates structured clarifying questions before work begins. Use at the start of any non-trivial task, or when a request could be interpreted multiple ways. Triggers on "뭘 원하는건지", "요구사항 정리", "clarify", "what exactly", "scope", "requirements", "정확히 뭘", "before we start".

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。

Send transactional or notification emails via the Resend API — text and HTML content, multiple recipients, CC/BCC, reply-to, and file attachments. Single Python script with stdin pipe support. Triggers on 'send email', 'transactional email', 'email notification', 'Resend API', 'send via Resend'.

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 skill enables cross-model dialogue between Claude and Gemini with shared visual memory. Use when the user wants to generate images, have visual dialogues with AI, create scientific illustrations with continuity, or have multiple AI perspectives respond to the same prompt. Key trigger phrases: "generate an image", "visual dialogue", "ask the daimones", "resonance field", "Minoan tarot", "cross-model", "KV cache", "MESSAGE TO NEXT FRAME".

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 and edit high-quality images using Google's Nano Banana Pro (Gemini 3 Pro Image) AI model. Use this skill when working with AI image generation or editing tasks including (1) Creating images from text prompts with professional quality, (2) Editing existing images with natural language instructions, (3) Multi-turn iterative image refinement, (4) Product photography and marketing visuals, (5) Creating infographics and diagrams with text, (6) Photorealistic and artistic image generation, (7) Batch image generation workflows, (8) Testing and troubleshooting Gemini image generation API. Supports up to 4K resolution, multiple aspect ratios, reference images, and advanced prompting techniques for cinema-quality results.

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。