Results for “sca”

Assesses NumPy code for cuPyNumeric migration readiness by analyzing source code against an API support manifest and GPU-scaling idioms, producing a structured verdict with per-finding reasoning.

Perform static audit and dynamic conformance scanning of OpenAPI specifications using the 42Crunch platform to identify OWASP API Security Top 10 vulnerabilities.

Submits, retrieves, and manages headless B2C Commerce storefront orders via SCAPI ShopAPI Orders or legacy OCAPI, including SLAS authentication, order amendment, status retrieval, and notifications.

Scaffold and develop AI agents using OpenAI Agents SDK patterns, covering agent definition, tools, guardrails, handoffs, context, RAG pipelines, streaming, API routes, testing, and debugging.

Extract text from PDFs and scanned documents. Use web_extract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.

Scan for unused exports, unreachable code, orphaned files, and stale dependencies

Design and operate databases avoiding common scaling, reliability, and data integrity traps.

The choice is not about scale or sophistication.

使用AgentShield扫描您的Claude代码配置（.claude/目录），以发现安全漏洞、配置错误和注入风险。检查CLAUDE.md、settings.json、MCP服务器、钩子和代理定义。

Scaffold a new mobile page/screen using the StyleSeed layout patterns

Scaffold a new mobile page/screen using the StyleSeed layout patterns

Scaffold a new mobile page/screen using the StyleSeed layout patterns

Scaffold a new ACF Composer block: ACF block, ACF Composer, block registration, Gutenberg block, block view, Blade block, InnerBlocks, block.json, block scaffold, acf:block, custom block, block category, block icon, block supports, block variants, block styles, is-style, create-block, enqueue block, block CSS, block JS, custom element, block preview parity — full custom element architecture with scoped CSS and theme variations.

4-stage funnel that screens all 500+ Hyperliquid perps down to the top trading opportunities. Scores setups 0-400 across smart money, market structure, technicals, and funding. BTC macro filter, hourly trend gate (counter-trend = hard skip), cross-scan momentum tracking. Near-zero LLM tokens — all computation in Python. Use when scanning for new trading opportunities on Hyperliquid, evaluating setups, or checking market conditions.

Review GitHub PRs and GitLab MRs with structured analysis including blast radius, security scanning, test coverage delta, breaking change detection, and performance impact.

Audit environment-variable hygiene and secrets safety across local development and production with practical scanning, drift awareness, and rotation readiness.

Runs a multi-phase verification pipeline including build, type-check, lint, tests, security scan, and diff review to ensure code quality before creating a PR.

Walks through a step-by-step wizard to configure brand colors, typography, and scaffold the first screen for a new app using the StyleSeed design system.

Rapidly classify malware samples against known family signatures using YARA rules, covering rule writing, scanning, and integration with analysis pipelines.

Scan for Bluetooth Low Energy devices, enumerate GATT services and characteristics, and detect security vulnerabilities such as unencrypted data exposure and known vulnerable device fingerprints.

Provides a comprehensive penetration testing toolchain with 20+ security tools (Nmap, Nuclei, SQLMap, FFUF, Hashcat, etc.) exposed via MCP servers for authorized vulnerability scanning, exploitation, and reporting.

Audits Claude Code configuration files for security vulnerabilities, misconfigurations, and injection risks using AgentShield, covering CLAUDE.md, settings.json, MCP servers, hooks, and agent definitions.

Audits Claude skills and commands for quality, offering Quick Scan for changed skills and Full Stocktake modes with sequential subagent batch evaluation.

Manage Gate entities — validation checkpoints that WorkItems, scopes, or policy applications must pass (code review, security scan, tests). Use when defining or updating a checkpoint for v2 workflow surfaces — code review, tests green, security scan, stakeholder approval.

Simulate and detect software supply chain attacks including typosquatting via Levenshtein distance, dependency confusion testing, package hash verification, and vulnerability scanning with pip-audit.

Compress large language models using knowledge distillation from teacher to student models, covering temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Evaluate a screen's information density by analyzing cognitive load, content prioritization, scanning patterns, and progressive disclosure, with actionable fixes for each dimension.

Scans US equities for daily +20%/-20% movers, classifies catalysts and chart context, updates forward outcomes, and summarizes cohort patterns for research.

Scans a codebase to extract design tokens, typography, colors, spacing, and brand patterns, then writes a Design Context section to CLAUDE.md without asking questions.

Scans local repos and AI agent skill folders for security drift, saves trusted baselines, and compares changes after updates to highlight risky new capabilities.

This skill should be used when the user asks to "run pentest commands", "scan with nmap", "use metasploit exploits", "crack passwords with hydra or john", "scan web vulnerabilities with nikto", "enumerate networks", or needs essential penetration testing command references.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Generate a new package or app in the monorepo following established project conventions