'wan' 검색 결과

Build psychological safety in your team using Simon Sinek's Leaders Eat Last Circle of Safety model and Amy Edmondson's research. Use when a leader says "my team doesn't speak up", "people are afraid to fail", "nobody challenges bad ideas", "people only tell me what I want to hear", "I want my team to take more risks", "there's a blame culture", "people hide mistakes", or "I want to create a safe environment". Also trigger when someone describes a team where postmortems turn into blame sessions, where people stay quiet in meetings and then vent privately, where mistakes are hidden until they become crises, or where the same person speaks 80% of the time in every meeting. Psychological safety is not about comfort - it is the condition that makes high performance possible.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Ideate a brand-new game concept with the user, from a blank page or a one-line hint to a finished CONCEPT card. Use it whenever the user wants to come up with a new game — whether the project has no design document yet, or already has one and they're exploring a fresh idea. Through a structured divergence/convergence dialogue it explores ideas, frames the hook, generates and scores concepts (market validation delegated to /unikit-gd-explore), and settles pillars, loops, motivation, and a pre-mortem. Trigger whenever the user wants to make or invent a game, doesn't know where to start, or gives a genre/theme hint, e.g. "let's come up with a game", "I don't know what game to make", "I want to make a roguelike", "a game about zombies", "a farming game", "help me come up with a concept". For researching or dissecting existing games use /unikit-gd-explore; to write an already-chosen concept as the master GDD use /unikit-gd-spec.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate advertising images automatically from a product URL + brand profile. ✅ USE WHEN: - User provides a product URL (e-commerce link) - Want automated product scraping + image generation - Have a brand profile to apply (70+ brands available) - Need funnel-stage targeting (awareness/consideration/conversion) - Want AI to auto-select model, scene, lighting based on brand ❌ DON'T USE WHEN: - User provides local product image file → use morpheus-fashion-design - Don't need a person in the image → use nano-banana-pro - Want manual control over model, scene, packs → use morpheus-fashion-design - Already have hero image, need variations → use multishot-ugc - Need video output → use veed-ugc after image generation INPUT: Product URL + brand name (optional) + funnel stage (optional) OUTPUT: PNG advertising image with product + model

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants text to sound more human, says writing sounds "too AI" or "too ChatGPT," asks to humanize or rewrite a draft to feel natural, or shares content wanting it to feel authentic and less robotic. Also applies to LinkedIn posts, blog drafts, or emails where the user wants a more genuine voice.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Visual Stack's primary tool. Build a UI wireframe and open it in an interactive review workspace where the user comments directly on the page, turning those comments into the next iteration — or point the same workspace at an app that is already running and review the real thing. Use when the user wants a wireframe, UI mockup, screen design or prototype built; wants to review, annotate, mark up or comment on a page, a design, an existing UI, a running app or a website; wants to iterate on a UI; asks to use Visual Stack; or invokes $vstack:review, /vstack:review, or the older /vstack:wireframe.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create visually rich PowerPoint (.pptx) presentations from academic papers, research notes, or any content the user wants in slide format. Uses PptxGenJS + LaTeX formula rendering. Always use this skill when the user wants PPT/PPTX output instead of Beamer/LaTeX slides. Trigger on: powerpoint, pptx, PPT, make a ppt, 做PPT, 做幻灯片, make slides (non-LaTeX), prepare a presentation (when context implies PPT), 做个报告, presentation slides, help me prepare a talk (when not Beamer), convert paper to slides (when PPT implied). Even if the user just says "make slides" or "做个演示" without specifying format, trigger this skill and ask whether they want PPT or Beamer — don't silently default to Beamer. Do NOT trigger on: beamer, latex slides, .tex files, tikz — those belong to the beamer skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Solves complex, open-ended problems using an evolutionary search mechanism inspired by genetic algorithms. Generates a diverse population of candidate answers, scores them against a rubric, then iteratively applies selection, crossover, and mutation to breed progressively better solutions. Use this skill whenever the user wants to explore a solution space deeply, asks for the "best" answer to a subjective or multi-dimensional problem, wants to evaluate competing approaches and distill the strongest, or uses any of these triggers: "evolutionary search", "genetic algorithm", "evolve an answer", "breed solutions", "population-based search", or phrases like "give me multiple approaches and refine the best one". Also trigger when the problem is complex, open-ended, or has no obvious single correct answer and the user wants a high-quality, well-explored result, not just a quick response.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Solves complex, open-ended problems using a knockout tournament mechanism. Generates an initial population of diverse candidate answers, pairs them into head-to-head matches, produces a hybrid "child" solution from each pair, and advances the best of the three through a bracket until a winner emerges. Use this skill whenever the user wants to competitively select the best answer from a field of alternatives, asks for a "tournament" or "bracket" style search, wants a faster alternative to full evolutionary search, or uses phrases like: "run a tournament on this", "bracket-style search", "let solutions compete", "knockout search", "find the best answer by elimination", or "which solution wins?". Also trigger when the problem is open-ended and the user wants a clear winner quickly rather than a deep multi-generation refinement.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run the Tamanu release-branch CUTOFF checks when preparing to cut a new release/X.YY branch: confirm the previous release's hotfixes are merged to main, reconcile the commits since the previous release against the "Merged to main" card list from Linear, and scan for data/reporting/schema changes. Use when the user is cutting a Tamanu release and wants to verify the codebase is ready, wants to check main against the previous release branch, or wants to reconcile commits with a Linear "Merged to main" column (they should paste the card list). Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for verifying an existing release branch's hotfix integrity (see pre-release-checks) or for deciding which tests to run (see scope-tamanu-release-tests).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Weekly review partner that compounds productivity gains over time. Tracks velocity, logs friction, sets next-week targets, recognizes patterns across weeks, and feeds insights back to the audit for the next loop. Use when you want a weekly review, need to identify friction, want to see patterns, or want to update your system map. Part of the architect-system loop. Outputs to system/compounder/week-{date}.md.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Insurance-industry App-in-Skill for read-only data governance, backed by an operator-provisioned Busabase workspace (one Drive node for the file drive plus four Bases for QA pairs, featured information, insurer notices, and user feedback) and trusted export/restore/PDF-text-backfill scripts. Use when the user invokes $kelly-insure-data or /kelly-insure-data, wants an insurance data workspace with UI, needs to review insurance files, metadata completeness, QA pairs, featured information, insurer notices, or user feedback, wants to back up or restore a Kelly Insure Data Busabase workspace from local PDFs, or wants Busabase Drive/Base data surfaced for data quality review and ongoing data governance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Draft or review an employer's Otameshi Tenshoku listing so a candidate can decide whether to apply: check item by item that the work, deliverable, out-of-scope tasks, required experience, support, hours, schedule, location, compensation, contract form and payment timing are stated, find open-ended, unpaid-company-work, guarantee, personal-attribute and unverifiable wording, and confirm that the hours, weekly availability, period, rate and budget hold together. Use when a hiring manager, recruiter or small-business owner wants a listing written from a plan, wants an existing draft or published listing reviewed before or after posting, or wants to know why applications are not coming; do not use to design the work itself (trial-work-design), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post or update the listing on the user's behalf.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare a request to change the terms of a Japanese job offer before accepting it, sorting what the user wants changed (pay components, start date, work style, location, title, probation) by priority, tying each ask to a basis the user can show (the written offer, the job posting, what was said in interviews, their own records), working the timing back from the acceptance deadline, and drafting the message to the employer or recruiting agent. Use when a user has an offer and wants to ask for higher pay, a later start date, more remote days, or other changed terms, or wants to know how to raise it without losing the offer; do not use to predict whether the request will succeed, to set a target figure from market rates, to fabricate a competing offer, to decide whether to accept, or to send anything to the employer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Put two or more Japanese job offers on one basis so they can be read side by side, separating fixed overtime allowance and unguaranteed bonus from the stated annual figure, converting each offer to guaranteed annual pay and to an hourly rate that includes deemed overtime hours, and listing non-monetary terms without scoring them. Use when a user holds more than one offer, wants to know what a stated 年収 actually contains, or wants to see where the ranking changes once the components are separated; do not use to recommend which offer to accept, to score or rank offers, to estimate take-home pay as fact, or to reply to an employer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when the user wants to "synthesize two competing solutions without averaging", "resolve a design/value disagreement by lifting to a higher frame", "止揚 / アウフヘーベン", "矛盾を止揚", "独立案を統合", "平均化せず統合", "contradiction lift", "sublate competing designs", or has two good-but-divergent answers to the same question and wants a higher-order resolution (a selection mechanism) or an honest aporia — not a compromise. NOTE: Use this when two independent solutions diverge and you want to PRESERVE both truth-moments while lifting to a higher frame. Use devils-advocate to stress-test ONE proposal with an assigned opponent, strong-inference to find an unknown cause, and dialectic-loop to validate a claim against a real corpus.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze prompts and offer depth / token-budget options BEFORE answering. Use this skill when the user wants to control token usage, tune response depth, choose between short and long answers, or optimize their prompt. Triggers on: "tokens", "token budget", "depth", "consumption", "short vs long answer", "how many tokens", "save tokens", "answer at 50%", "give me the short version", "I want to control how much you use", "tune your response", "presupuesto de tokens", "profundidad", "responde al 50%", "dame la versión corta", or any equivalent phrasing in English or Spanish. If the user wants to control length, detail or depth -- even without mentioning tokens explicitly -- this skill applies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

superpowers 작업 기록을 읽고 중복 작업, 계획 이탈, 시간과 토큰 사용량을 조사합니다. 기록 파일의 경로와 줄 번호를 근거로 경위를 보고서로 정리합니다.

• 중복 작업과 계획 이탈 조사
• 세션 소요 시간과 토큰 사용량 확인
• 스킬 사용 여부 확인

기능 추가나 버그 수정을 실패하는 테스트와 최소 구현 순서로 진행합니다. 호출자가 쓰는 공개 인터페이스로 동작을 검증하고 내부 구조에 의존하지 않는 테스트를 만듭니다.

• 테스트를 먼저 작성해 기능 추가
• 버그 재현 테스트부터 수정 시작
• 통합 테스트 대상 인터페이스 선정