'rspec' 검색 결과

Coordinates a named team of Codex workers with concrete responsibilities and shared progress records. It selects available collaboration tools, manages communication and separate working copies when needed, and handles integration, team archival, and cleanup after the work ends.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Splitting related implementation work across cooperating agents
• Investigating several aspects of a clearly defined problem
• Inspecting team progress and member responsibilities

Turn implementation plans and specifications into an ordered tasks.md checklist. Organize work around user stories, meaning goals expressed from a user's perspective, with file paths, dependencies, parallel work opportunities, and independent verification criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an implementation plan into a concrete checklist
• Organizing development work by user story
• Identifying task dependencies and parallel work

Clarify an existing feature specification before implementation planning. The skill prioritizes consequential gaps, asks focused questions one at a time, updates the specification with accepted answers, and summarizes unresolved decisions and recommended next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resolving unclear requirements before implementation planning
• Defining error behavior and measurable completion criteria
• Recording agreed answers directly in a specification

Prepare code review requests with an implementation summary, requirements, and the exact Git commit range. Assemble reviewer instructions and organize follow-up actions by severity after a feature is completed or before changes are merged.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Requesting a review after completing a feature
• Preparing review materials before merging changes
• Getting a fresh perspective on a difficult bug

Develop workflows for satellite imagery and geographic data, using guidance and code examples to calculate vegetation indicators, classify land cover, analyze terrain, and find road routes. Covers GIS, software for working with location-based information, and spatial machine learning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracking vegetation changes in satellite imagery
• Classifying land cover with machine learning
• Aggregating location data by region

Coordinate multiple AI agents under a lead coordinator for research, development, and review. Assign specialized roles, use voting to make shared decisions, preserve collective knowledge, and pause or resume work through managed sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Divide application development among specialized agents
• Compare technical options through research and voting
• Coordinate code reviews from several perspectives

YOLO mode. Spawns 4 parallel C-suite agents (CEO, CTO, CFO, COO). Each analyzes the business from their perspective using ALL available data. Produces unfiltered Hard Truths report. After user types YOLO, autonomously runs the business for a day using /loop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Convene an AI executive board of directors (CEO, CFO, COO, CLO, CISO sub-agent personas) to vet a business idea, product concept, new service offering, M&A target, or operational initiative — and deliver an integrated board memo with a Go/No-Go recommendation. Use this skill whenever the user wants an idea vetted, stress-tested, or reviewed from multiple executive perspectives; asks to "present this to the board," "run this by the boardroom," "vet this idea," "poke holes in this plan," or "prep me for a board meeting"; or shares a business plan, pitch, proposal, or initiative document and asks for structured executive feedback. Also trigger when the user asks for a Go/No-Go decision, risk review across finance/legal/security/operations, or preparation for presenting an initiative to real leadership.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

[omh] Vague, underspecified request: one-question-at-a-time clarification. Use when the user says: deep-interview, interview me, clarify, feature shaping, ambiguous product request, one question, 要件を詰めて, 曖昧な要求.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze a startup from three perspectives — VC investor, job applicant, and CEO/founder — covering market, traction, unit economics, team, defensibility, runway, equity value, culture, and competitive position, with a verdict for each. Use this skill whenever the user wants to evaluate a startup or private tech company: due diligence, whether to invest in it or join it, weighing a startup job offer, company health, outlook, or competitive position, or open-ended asks such as "what do you think of [company]" or "research [company] for me".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Spatial depth design with isometric views, vanishing points, and layered elements that guide attention through 3D-like realism.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Credentials come from explicit plugin configuration or pro-workflow-specific CLI variables. Persists transcript to a wiki page when --wiki <slug> is passed. Use when the user wants multiple AI perspectives, consensus-building, or the "LLM Council" approach for high-stakes reviews, plan critique, or contested learning rules.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate CHANGELOG.md entry from recent commits in conventional format. Also syncs the website changelog page. Use this skill whenever the user asks to: generate a changelog, document what changed between tags, or create a new CHANGELOG entry. If you see requests like "write the changelog for v0.17", "what changed since last release", this is the skill to use. Do NOT manually edit CHANGELOG.md without this skill — it ensures proper formatting, user-perspective writing, and website changelog sync. For full release workflows (Release PR review, tests, draft assets, publication, announcements), use /release instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Code review behavioral standards. Reference during code review to ensure consistent quality checks from a QA perspective.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Content moderation API integration using OpenAI Moderation, Perspective API, and others

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive multi-perspective review using specialized judges with debate and consensus building

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review GitHub pull requests with detailed, multi-perspective code analysis using parallel subagents. Use this skill whenever the user wants to review a PR, asks for code review on a pull request, mentions "review PR", "check this PR", "look at pull request", or references a PR number or GitHub PR URL. Do NOT use for local uncommitted changes — this skill only reviews pull requests on GitHub.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn a company update into 3-7 structurally distinct, journalist-shaped story angles. Runs the fact through proven newsroom lenses (perspective, abstraction, news values, data, contrarian, news peg) and refuses rephrasings, invented facts, named-journalist guesses, and AI-marketing slop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review code changes from multiple perspectives with context bootstrap, depth-tier rigor, and structured findings output.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Two pipelines ending at a scanner-look PDF. (1) Phone photos of paper documents (contracts, stamped certificates, receipts, forms, handwritten notes) → clean scanner-quality PDF: perspective rectification + noteshrink whitening + A4 assembly + mandatory whole-document check. Trigger: "把照片 做成扫描件", "photos to scanned PDF", "make this look scanned", "手机拍的 文档转 PDF", "盖章文件扫描", replacing pages in an existing scanned PDF, any CamScanner-like request. (2) A digital document with no signature yet (rendered docx/PDF, confirmation form, contract draft) → make it look hand-signed and scanned: synthesize a handwriting-style signature, composite it onto the signature line, apply the same scan-look post-processing. Trigger: "帮我做个手写签名", "生成签名盖到这份文件上", "做成签过字的扫描件", "synthesize a signature", any request for a document that needs to look signed without a real photographed signature. Do NOT hand-roll levels/contrast enhancement for scan-look — tried and rejected twice; this skill's pipeline is the proven one.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective. Use when asked if a product is agent-ready, to audit a site or API for AI usability, to prepare for agentic traffic, or when agents keep failing against your product. Produces a scored readiness report with per-surface findings and a prioritised fix list. For optimising a single article for AI citation use aeo-optimizer; for designing the MCP server itself use mcp-server-spec.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.