236건 · 관련도순
Reliability gate for answers. Forces evidence-based reasoning, explicit uncertainty, and “insufficient information” instead of guesses. Use when user says “be 100% sure”, “no hallucinations”, “only if verified”, “grounded answer”, or when stakes are high.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reliability gate for answers. Forces evidence-based reasoning, explicit uncertainty, and “insufficient information” instead of guesses. Use when user says “be 100% sure”, “no hallucinations”, “only if verified”, “grounded answer”, or when stakes are high.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
万能复刻 / universal website copy-remix workflow with evidence-based reference recon. Use when the user wants to copy, clone, recreate, remix, migrate, or rebuild any reference website's look, interaction feel, scroll rhythm, hero motion, layout structure, visual system, deployed static assets, or landing-page experience into a new website. Trigger for requests like 万能复刻, 万能网站复制, 复刻官网, 一比一复刻, 授权复制, 高仿但换产品, 参考某官网做自己的官网, RMUX 风格, Linear/Raycast/Apple/Vercel 风格迁移, hero 动效复刻, 滚动体验复刻, 官网换皮重构, 网站侦察, 复刻验收, WebGL/Canvas/Three.js 效果还原.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates specialized AI agents with optimized system prompts using the official 4-phase SOP methodology from Desktop .claude-flow, combined with evidence-based prompting techniques and Claude Agent SDK implementation. Use this skill when creating production-ready agents for specific domains, workflows, or tasks requiring consistent high-quality performance with deeply embedded domain knowledge.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Improve an existing skill or repository through isolated proposals, reproducible evaluation and evidence-based acceptance. Use for self-evolve or skill improvement.
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Investigation flow (Glob -> Grep -> Read), evidence-based research with file:line references, structured output format for AI consumption. Use for pattern discovery, implementation research, and codebase investigation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Write balanced, evidence-based performance reviews for employees. Covers achievements, development areas, and goal setting using the SBI (Situation-Behavior-Impact) feedback model. Produces reviews that are fair, legally defensible, and growth-oriented. Use during annual or semi-annual review cycles. Do not use for disciplinary actions, PIPs, or compensation calculations.
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Provide constructive, evidence-based research mentoring feedback on student manuscripts, proposals, analyses, code, figures, and presentations. Use for advisor-style review that teaches the next improvement without impersonating an advisor or making unsupported scientific judgments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Explore and debate options with fresh documentation and research before committing. Use when facing a real decision with multiple plausible approaches — library/framework choice, architecture call, API or schema design, algorithm selection, or any communication / strategy call where being wrong has cost. Enumerates relevant research domains, looks up current docs and evidence-based methods, weighs options on correctness and elegance, resists bloat. Do NOT use for divergent ideation (brainstorm), extracting user intent (elicit), or reviewing already-written code (quality-review).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Diagnose Azure application and infrastructure issues with a structured, evidence-based workflow. Use this skill when users ask to troubleshoot outages, latency, failures, configuration drift, or anomalies. Root cause analysis using logs, metrics, traces, and dependency mapping.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a problem requires structured step-by-step reasoning before acting. Prevents jumping to conclusions or guessing root causes without evidence. Triggers on complex debugging, multi-step analysis, hypothesis verification, or when mk:fix invokes diagnosis phase. NOT for being stuck on approach (too many implementations, forced assumptions, innovation block) — use mk:problem-solving instead.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when stuck on approach (not debugging). Strategic unsticking via 7 non-default techniques: simplification cascades, collision-zone thinking, meta-pattern recognition, inversion, scale game, first principles, via negativa. Triggers on complexity spiraling, innovation block, recurring patterns across domains, forced-assumption solutions, scale uncertainty, told-it's-impossible, or bloated systems needing subtraction. For evidence-based root-cause debugging, use mk:sequential-thinking instead.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematic debugging with root cause investigation. Produces a diagnostic report; mk:fix owns remediation and mk:build-fix owns compile/build failures. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively suggest when the user reports errors, unexpected behavior, or is troubleshooting why something stopped working. NOT for applying fixes without investigation (see mk:fix); NOT for step-by-step evidence-based reasoning (see mk:sequential-thinking).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when user says "simmer this", "refine this", "hone this", "iterate on this", or asks to improve a specific artifact over multiple rounds. Runs an iterative refinement loop with investigation-first judges that read the code, understand the problem, and propose evidence-based improvements. Auto-selects single judge or multi-judge board based on complexity. Works on any artifact type: documents, prompts, specs, emails, creative writing, API designs, pipelines, codebases. Supports multi-file workspace targets, runnable evaluators, and open-ended optimization (model selection, pipeline topology, prompt tuning).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Provides a checklist of five evidence-based measures to enhance the technical success of adrenal venous sampling procedures. Triggers include when preparing for AVS and asking 'How can I maximize success rate?' or troubleshooting failed attempts.
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Provides evidence-based guidelines for preoperative optimization and postoperative monitoring in patients with primary aldosteronism undergoing adrenalectomy. Triggers include when planning surgery or starting treatment and asking 'What perioperative care is needed?' or managing complications such as resistant hypertension, hypokalemia, or suspected cortisol co-secretion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when auditing UI code for interface-guideline compliance (accessibility, forms, animation, images, performance, UX copy) and/or validating a deployed page against Lighthouse 95+ targets. Produces evidence-based findings (file:line + Lighthouse JSON) and turns confirmed pendencies into Draft SPECs via write-specs. Do NOT use to design/build UI from scratch (use design) or to fix findings inline — fixes go through the SPEC pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when planning the migration of a legacy system or monolith — analyzes the source codebase (current repo, or a repo URL cloned to a temp dir) and produces an evidence-based Strangler Fig migration plan with per-domain SPECs. Default target is .NET (Blazor WebAssembly, .NET MAUI, ASP.NET Core); other stacks allowed by user decision. Do NOT use to implement code or when approved SPECs already cover the migration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Verify like Fable 5 — obsessive, systematic, evidence-based quality assurance woven into your reasoning. Use this skill when you have just written or modified code, need to confirm something works, are running tests, or need to validate output against requirements.
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Use when a bug has multiple plausible causes across layers — competing hypotheses, validation loops, evidence-based conclusions — even when the user just says 'why is this happening?'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Diagnose and fix CJK (Korean, Japanese, Chinese) text-wrapping issues in web UIs. Systematically traces mid-syllable breaks, orphaned glyphs, and awkward line splits through a layered fix strategy covering global CSS cascade, component-library overrides, headline balancing, and translation-level phrase binding. Produces an evidence-based diagnosis and verification report before any code change is applied.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Brutally honest AGENTS.md/CLAUDE.md review backed by real A/B test evidence. Not just opinions — actual proof that your rules are dead weight. Roasts instruction files for bloat, slop, and redundancy, then proves it by running evals. Use when user says "roast my agents.md", "roast my CLAUDE.md", "prove my rules are useless", "eval roast", or wants entertaining evidence-based feedback on their AI config files. Also triggers on "audit my instructions" or "are my rules helping".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.