485건 · 관련도순
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Check that a document's claims about code are actually true by reading the prose, the code, and the tests and reporting (or fixing) where they disagree. Use whenever the user wants to verify a README, guide, spec, or docstring still matches the code; whenever they mention documentation drift, doc-code sync, "is this still accurate", stale docs, or keeping docs/tests/code consistent; before publishing or merging a docs change; or as a periodic doc-accuracy sweep. The agent reads the prose's meaning directly — there is no claim-comment DSL to maintain. Pairs with TDD — the test suite is the deterministic behavioral gate, this skill is the semantic prose-vs-reality review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cross-context adversarial review for deliverables before shipping. Use when producing blog posts, technical recommendations, analysis briefs, code, or any artifact where accuracy matters more than speed. Triggers on "challenge this", "review before shipping", "adversarial pass", "stress test this".
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Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
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Create QA checklists for verifying design implementation accuracy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Investigate performance bottlenecks and validate optimizations without weakening retrieval quality or correctness.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Interactive demo: Generate pseudo-labels from a strong teacher model, build a cheap student function, and evaluate accuracy. Showcases pseudo-labeling and teacher-student distillation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Amazon Bedrock Automated Reasoning for mathematical verification of AI responses against formal policy rules with up to 99% accuracy. Use when validating healthcare protocols, financial compliance, legal regulations, insurance policies, or any domain requiring deterministic verification of AI-generated content.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards. USE THIS SKILL WHEN: - You need to review or audit actuarial models (reserving, pricing, capital) - Someone asks about loss triangle analysis or reserve adequacy - You are evaluating IBNR calculations, chain ladder methods, or Bornhuetter-Ferguson - A project involves insurance pricing, GLM rating models, or ratemaking - You need to assess Solvency II SCR calculations or RBC compliance - Someone mentions actuarial opinions, ASOP compliance, or SOA standards - You are reviewing stochastic models, ESG configurations, or DFA frameworks - A codebase uses actuarial libraries (chainladder, lifetables, ChainLadder R package) TRIGGER PHRASES: "actuarial", "loss reserving", "IBNR", "chain ladder", "premium pricing", "mortality table", "Solvency II", "capital adequacy", "ratemaking", "GLM pricing", "risk-based capital", "reserve analysis", "actuarial opinion"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit existing API documentation for completeness, accuracy, and developer experience. Use when asked to "audit our API docs", "are our docs accurate", or "review documentation quality".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
ML/AI engineer — LLM integrations, prompt engineering, model pipelines, evals, RAG. Use when asked to "add AI to this", "integrate an LLM", "build a RAG pipeline", "evaluate model accuracy", or "improve this prompt".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Documentation reconnaissance for takeover — find all docs, assess accuracy, freshness, coverage, and discoverability, and identify critical knowledge gaps. Use when asked "what docs exist", "documentation assessment", or "knowledge gaps".
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Calculus, series, numerical methods, and optimization for computational mathematics. Covers limits (epsilon-delta, L'Hopital, squeeze theorem), derivatives (rules, optimization, related rates), integrals (Riemann sums, Fundamental Theorem of Calculus, integration techniques), series (convergence tests, Taylor and Maclaurin expansions), differential equations (separable, linear, exact), numerical methods (Newton-Raphson, Euler method, Simpson's rule), and computational pitfalls (floating-point arithmetic, catastrophic cancellation, numerical stability). Use when computing derivatives, integrals, limits, series, solving differential equations, or analyzing numerical accuracy.
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Evaluating online information for credibility, accuracy, and context. Covers the SIFT method (Stop, Investigate, Find, Trace), lateral reading, reverse image search, primary source verification, and common misinformation tactics. Use when assessing the trustworthiness of a web page, article, video, social media post, or any digital claim before relying on it or sharing it.
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Observational drawing and visual perception techniques for art education. Covers contour drawing, gesture drawing, negative space, proportion and measurement, value mapping, spatial depth cues, and the cognitive shift from symbolic to perceptual seeing. Use when teaching drawing fundamentals, analyzing observational accuracy, or developing visual literacy in any medium.
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Guidance for verifying documentation accuracy against running OpenStack infrastructure. Use when detecting documentation drift, validating operations procedures against live systems, or confirming that runbooks produce expected results on the deployed cloud. Implements NASA Product Verification (SP-6105 SS 5.3) applied to documentation artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.