3,897 件 ・ 関連度順
概要と使いどころ
Security audit and vulnerability scanning for AI agent skills before install. Detects prompt injection, dangerous code, exfiltration, credential harvesting, and supply chain risks. Use when evaluating untrusted skills or gating installs.
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。
Generate changelogs and release notes from Conventional Commits with semver bump detection, Keep a Changelog formatting, and monorepo scopes. Use when preparing releases, enforcing commit standards, or automating release notes.
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 REST API designs for quality, consistency, and breaking changes. Lints OpenAPI specs, generates scorecards, and detects breaking changes between versions. Use when designing APIs, reviewing contracts, or managing API versioning.
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。
Cascades strategy from boardroom to individual contributor and detects misalignment. Use when teams pull in different directions, OKRs don't connect, departments optimize locally at company expense, or strategy doesn't translate to execution.
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。
This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".
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 across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD workflows.
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 data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
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 UI/UX designs for quality, AI-generated slop, and accessibility. Use when reviewing designs, detecting slop patterns, validating WCAG compliance, checking design-token adherence, or reviewing responsive design across breakpoints.
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。
Cut software and services spend through seat-utilisation analysis, redundant-tool detection, and renewal-timing leverage. Use when auditing SaaS spend, preparing a renewal negotiation, or hunting a budget-reduction target.
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。
Pre-landing PR review with two-pass checklist. Pass 1 critical: SQL safety, race conditions, LLM trust boundaries, enum completeness. Pass 2 informational: side effects, magic numbers, dead code, test gaps. Adversarial review auto-scaled by diff size. Scope drift 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。
Time series analysis and temporal pattern detection for scientific data
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。
One-time deployment configuration. Detects platform, gathers URLs and health checks, persists to CLAUDE.md for future land-and-deploy runs.
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。
Performance regression detection. Captures TTFB, FCP, LCP, bundle sizes, request counts. Regression thresholds: timing >50% or >500ms. Baseline comparison, trend tracking, budget compliance scorecard.
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。
Live-site visual QA audit with fix loop. 10-category checklist (~80 items) including AI Slop Detection (10 blacklisted patterns). Atomic commits per fix. Before/after screenshots. A-F grading. Parallel cross-model code-level design audit. Modes: Full, Quick, Deep, Diff-aware, Regression.
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。
Self-update mechanism. Detects git vs vendored install. Auto-upgrade option with escalating snooze backoff. Syncs global and local copies. Extracts CHANGELOG bullets post-upgrade.
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。
Infrastructure-first security audit across 14 phases: attack surface mapping, secrets archaeology (git history), supply chain analysis, CI/CD pipeline security, LLM/AI threat detection, OWASP Top 10, STRIDE threat modeling. Confidence gates, exploit scenarios, parallel verification, trend tracking. Read-only.
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。
Scope deviation 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。
Analyze project state, detect plan drift, and create prioritized reconstruction plans
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。
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
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。
Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches across nodes, planning upgrades, documenting cluster configuration, or troubleshooting version-related issues on HyperPod. Triggers on requests about versions, compatibility, component checks, or upgrade planning for HyperPod clusters.
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 creating, improving, finding, or auditing agent skills - the user says 'create a skill', 'do I have a skill for X', 'improve the X skill', 'which skill should I use', asks whether a skill exists for a task, or wants to validate, test, evaluate, package, or health-check skills. Also use for skill ecosystem maintenance (duplicate detection, stale skills, trigger collisions) and advisor checkpoints.
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。
Initialize a PM Brain — a markdown-native second brain for a product operator (PM, product lead, founder, or anyone accountable for one product or initiative) doing judgment-heavy work with scattered inputs. Detects greenfield vs. migration mode, runs a focused interview, copies the deterministic scaffold into the working directory, populates placeholders from interview answers, runs a self-test, and commits. Use when invoked via `/pm-brain` or when the user asks to set up a PM Brain.
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。
Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project. Also extracts injected system prompts from providers that override Claude's identity. Fully self-contained — copy the code below and run, no extra packages beyond httpx. Use when the user wants to verify a Claude API key or endpoint, check if a third-party Claude service is authentic, audit API providers for Claude authenticity, test multiple models in parallel, or discover what system prompt a provider has injected.
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 the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.
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。