2,109 件 ・ 関連度順
概要と使いどころ
Reference for the ripdpi-dns-resolver crate: DoH/DoT/DNSCrypt/DoQ/ODoH, health scoring, bootstrap IPs, tamper diagnostics. Use when touching encrypted DNS resolution, failover, or tamper detection.
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。
Test execution reference: framework detection (pytest/jest/vitest/go/cargo/junit) and correct run/coverage commands, plus failure-analysis steps. Use when running tests, analyzing test failures, or verifying coverage after code changes.
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。
Autonomous novel writing CLI agent with web workbench (JiaOS Studio) - use for creative fiction writing, standalone short-fiction packages, cover generation, novel generation, style imitation, chapter continuation/import, EPUB export, AIGC detection, and fan fiction. Native English support with 10 built-in English genre profiles (LitRPG, Progression Fantasy, Isekai, Cultivation, System Apocalypse, Dungeon Core, Romantasy, Sci-Fi, Tower Climber, Cozy Fantasy). Also supports Chinese web novel genres (xuanhuan, xianxia, urban, horror, other). Multi-agent pipeline, two-phase writer (creative + settlement), stronger long-form chapter craft rules, hook-ledger payoff checks, 33-dimension auditing, token usage analytics, creative brief input, structured logging (JSON Lines), multi-model routing, custom OpenAI-compatible provider support, and JiaOS Studio web UI for visual book management, short-fiction runs, cover generation, chapter review, real-time writing progress, market radar, and analytics.
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。
Expert insurance claim adjuster with 15+ years in property/casualty. Specializes in coverage analysis, damage quantification (RC/ACV), liability determination, fraud detection, and settlement negotiation. Use when: insurance claim, damage assessment, coverage dispute, liability analysis, fraud investigation, claim settlement.
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。
Analyze insurance claims workflow systems for intake optimization, adjudication rules, subrogation, litigation management, and settlement analytics. Use when: 'audit claims process', 'review FNOL workflow', 'evaluate claims automation', 'check subrogation recovery', 'assess claims fraud detection', 'optimize claims cycle time', 'review Guidewire ClaimCenter setup'.
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。
Integrate API quality gates into CI — linting, breaking change detection, and coverage checks. Use when asked to "add API checks to CI", "detect breaking changes in CI", or "set up an API quality gate".
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。
Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".
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。
Compare API performance across versions — regression detection and root cause analysis. Use when asked "did performance regress", "compare API latency across versions", or "why is this slower".
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。
Expert guide for API rate limiting, bot protection, DDoS mitigation, brute-force prevention, and abuse detection / Panduan ahli untuk rate limiting API, perlindungan bot, mitigasi DDoS, pencegahan brute-force, dan deteksi penyalahgunaan.
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。
Real-time log monitoring, crash detection, and auto-hotfixing code without human intervention / Pemantauan log real-time, deteksi kerusakan, dan perbaikan kode hotfix otomatis tanpa intervensi manusia.
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。
Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.
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。
Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi.
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。
Expert guide for autonomous API schema drift detection, OpenAPI / gRPC breaking change monitoring, self-healing SDK adapter generation, and zero-downtime client-server synchronization / Panduan ahli deteksi drift skema API otonom, pemantauan breaking change OpenAPI / gRPC, pembuatan adapter SDK self-healing, dan sinkronisasi client-server tanpa downtime.
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。
Deeply analyzes application architecture and structure to perform audit, bottleneck detection, and code/performance optimization / Mempelajari arsitektur dan struktur aplikasi secara mendalam untuk melakukan audit, deteksi bottleneck, serta optimasi performa dan kode.
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。
Elite frontend design taste for building, reviewing, and polishing web interfaces. Use whenever the user wants to design, redesign, shape, critique, audit, polish, or improve any UI (landing pages, portfolios, dashboards, product UI, components, forms, onboarding, empty states) or asks to make something look better / premium / modern, fix the styling, add or fix animations, or make a design feel less generic ("AI slop"). Covers typography, color, spacing, layout, visual hierarchy, motion, micro-interactions, component states, accessibility, responsive behavior, UX copy, and anti-pattern detection.
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。
Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mertens conjecture, Polya conjecture). Use when discovering patterns, analyzing sequences, generating conjectures, or working with combinatorial identities.
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。
Catalog of informal fallacies with definitions, canonical examples, and detection heuristics. Covers relevance fallacies (ad hominem, straw man, red herring, appeal to authority, appeal to emotion), presumption fallacies (false dichotomy, complex question, begging the question), ambiguity fallacies (equivocation, amphiboly, accent), causal fallacies (post hoc, slippery slope, correlation-causation), and statistical fallacies (hasty generalization, cherry-picking). Use when evaluating arguments for rhetorical or informal reasoning errors.
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。
Primary and secondary source analysis for historical reasoning. Covers source classification, sourcing (author/context/purpose), corroboration across multiple sources, contextualization within time and place, and bias detection. Use when evaluating historical evidence, assessing source reliability, or constructing evidence-based historical arguments.
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。
Climate physics, forcing, feedback, and attribution. Covers the greenhouse effect and radiative forcing, climate sensitivity, fast and slow feedbacks, the paleoclimate record, detection and attribution science, regional climate impacts, and the distinction between weather, climate variability, and forced climate change. Use when reasoning about global warming mechanisms, interpreting temperature or CO2 records, evaluating attribution claims for extreme events, or distinguishing signal from natural variability.
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。
Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven decisions.
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。
Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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。
Manages skill-creator integration with GSD workflows including skill loading, session observation, bounded learning guardrails, and pattern detection. Use this skill whenever: executing any GSD phase (to ensure relevant skills are loaded first), starting or resuming a work session (to check for pending suggestions), the user asks about skills, patterns, or skill-creator status, performing code changes that might represent repeating patterns, the user corrects Claude's output (highest-signal observation), or when skill refinement, creation, or suggestion review is discussed. Critical for maintaining the adaptive learning layer.
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。
Produce animated source control visualizations using Gource. This skill handles installation of Gource and ffmpeg, detection of repository metrics, preset-based configuration, single and multi-repo log generation, ffmpeg video encoding pipeline, headless rendering for server environments, caption generation from git tags, GitHub avatar resolution, and GSD output delivery. Use this skill whenever the user wants to visualize repository history, create a code evolution video, see project timeline animations, generate Gource videos, combine multiple repos into one visualization, or produce any kind of source control visualization. Also trigger when the user mentions "Gource", "repo visualization", "code history video", "project evolution animation", or asks to "show me what we 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。