「accuracy」の検索結果

Create QA checklists for verifying design implementation accuracy.

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。

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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。

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.

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。

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.

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。

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

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。

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.

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。

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.

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。

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.

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。

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.

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。

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"

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。

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.

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。

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".

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。

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".

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。

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".

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。

Investigate performance bottlenecks and validate optimizations without weakening retrieval quality or correctness.

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。

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.

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。

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.

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。

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.

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。

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.

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。

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.

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 orchestration or planning agents are producing task plans, task prompts, or TASK.md instructions that must be unambiguous, verifiable, and resistant to hallucination. Applies CLEAR (Concise, Logical, Explicit, Adaptive, Reflective) to structure and write agent task prompts, then adds CoVe (Chain of Verification) checks where accuracy risk is meaningful. Activates on draft task prompts, swarm plans, migration tasks, and multi-step plans requiring independently executable steps.

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。

Explains Chain of Verification (CoVe) prompt design — a 4-step pattern separating generation from independent factual verification. Use when designing prompts that require factual accuracy, reducing hallucinations, checking technical standards or APIs, or producing step-by-step procedures where subtle errors are costly.

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。

OCRs scanned or image-only corpora with vision-language models in three phases. Evaluate selects a system by measured CER or WER against stratified human ground truth. Run builds the production pipeline. Clean corrects raw OCR with LLM and rule-based passes, quality diagnostics, and span-level provenance. Use when choosing an OCR model for a corpus, measuring OCR accuracy, transcribing scans at scale, or correcting raw OCR. Born-digital documents with text layers go to doc-to-markdown.

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。

Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.

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。