「sca」の検索結果

Use when you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code; Improve Spring Boot configuration properties and beans; Review Spring Boot component scanning and profiles; Tune Spring Boot graceful shutdown or virtual threads. Part of Plinth Toolkit

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Read your Instagram niche and profile from real data via Apify, no login. Scan a hashtag for the posts traveling now (likes, comments, owner) to see the format and hook that works. Pull profile stats for any handle, yours or a competitor's: followers, posts, bio, category. Instagram hides who liked or commented on other accounts, so this is discovery plus profiles, not engagers. Triggers on "what works in my niche", "scan the hashtag", "competitor stats". Not for writing captions (use ig-caption-writer).

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TimescaleDB - PostgreSQL extension for high-performance time-series and event data analytics, hypertables, continuous aggregates, compression, and real-time analytics

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Use when you need to run a binary, trace execution, or observe runtime behavior. Runtime analysis via QEMU emulation, GDB debugging, and Frida hooking - syscall tracing (strace), breakpoints, memory inspection, function interception. Keywords - "run binary", "execute", "debug", "trace syscalls", "set breakpoint", "qemu", "gdb", "frida", "strace", "watch memory"

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Structured competitive analysis with feature matrices, SWOT, positioning maps, and UX review. Covers research frameworks, pricing comparison, review mining, and visual deliverables. Use for: market research, competitive intelligence, investor decks, product strategy, sales enablement. Triggers: competitor analysis, competitive analysis, competitor teardown, market research, competitive intelligence, swot analysis, competitor comparison, market landscape, competitor review, competitive landscape, feature comparison, market positioning

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Scans for project documentation files (AGENTS.md, CLAUDE.md, GEMINI.md, COPILOT.md, CURSOR.md, WARP.md, and 15+ other formats) and synthesizes guidance. Auto-activates when user asks to review, understand, or explore a codebase, when starting work in a new project, when asking about conventions or agents, or when documentation context would help. Can consolidate multiple platform docs into unified AGENTS.md.

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Security intelligence for code analysis. Detects SQL injection, XSS, CSRF, authentication issues, crypto failures, and more. Actions: scan, analyze, fix, audit, check, review, secure, validate, sanitize, protect. Languages: JavaScript, TypeScript, Python, PHP, Java, Go, Ruby. Frameworks: Express, Django, Flask, Laravel, Spring, Rails. Vulnerabilities: SQL injection, XSS, CSRF, authentication bypass, authorization issues, command injection, path traversal, insecure deserialization, weak crypto, sensitive data exposure. Topics: input validation, output encoding, parameterized queries, password hashing, session management, CORS, CSP, security headers, rate limiting, dependency scanning.

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Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.

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The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).

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Find leaked secrets in source code, Git history, build artifacts, and infrastructure — and audit the secrets-management posture preventing future leaks. Use when the user mentions 'secrets audit,' 'secret scanning,' 'leaked credentials,' 'API key in code,' 'gitleaks,' 'trufflehog,' 'git history scan,' 'secrets management,' 'vault audit,' 'rotation policy,' 'AWS Secrets Manager,' 'HashiCorp Vault,' 'Doppler,' '1Password Secrets Automation,' 'sealed-secrets,' 'External Secrets Operator,' or needs to find or prevent credential exposure.

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Build, run, and improve a Security Operations Center — alert prioritization, runbook authoring, escalation criteria, on-call structure, alert tuning workflow, MTTD / MTTR / fidelity KPIs, analyst tiering, and shift handoffs. Use when the user mentions 'SOC,' 'security operations,' 'SOC analyst,' 'alert triage workflow,' 'runbook,' 'escalation,' 'on-call,' 'SOC tiering,' 'tier 1 / tier 2,' 'MTTD,' 'MTTR,' 'alert fatigue,' 'alert tuning,' 'shift handoff,' 'SOAR,' or wants to design or improve a security operations team.

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Local Gitleaks scans for staged changes, push ranges, and full history in private repos, with redacted reports. Use to block committed secrets without GitHub Secret Protection.

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Strategic product leadership toolkit for Head of Product including OKR cascade generation, market analysis, vision setting, and team scaling. Use for strategic planning, goal alignment, competitive analysis, and organizational design.

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Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.

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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

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Use when attacking AWS/Azure/GCP cloud — IAM/identity privilege escalation, IMDS/metadata SSRF, Entra device-code & PRT theft, GCP impersonation chains, Kubernetes/container escape, IaC/CI-CD federation abuse

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Use when crossing a Windows security boundary or escaping a sandbox — kernel/user crossing (win32k/dxgkrnl UAF CVE-2025-24983), BYOVD kernel R/W, UAC/COM elevation, AppContainer/LPAC & Chromium-Mojo sandbox escape (CVE-2025-2783), PPL bypass, RPC/ALPC & named-pipe impersonation

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Use when building a client-side browser exploit — V8/JSC JIT type confusion to renderer R/W, V8 heap-sandbox escape, renderer-to-browser sandbox escape (Mojo IPC, GPU/Dawn/ANGLE), Electron/webview IPC abuse, 1-click RCE chains

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Use when escalating privileges on a Linux host — SUID/SGID & GTFOBins, sudo LPE (CVE-2025-32462/32463), capabilities & LD_PRELOAD, kernel LPE (CVE-2024-1086, Dirty Pipe, GameOver(lay)), service misconfig (PwnKit, Looney Tunables), container/namespace escape

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Typography pass covering font choice, modular scale, tracking, leading, weight hierarchy, and micro-typography details. Use when fonts feel generic (default Inter with no reason), the scale is ad hoc, hierarchy is flat, or the user says "fix the typography" / "the text doesn't feel right". Invoke when the user asks for typeset on their UI, or mentions 'typeset' alongside design / UI / frontend work.

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Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows, publication-ready matplotlib house style, and layout using routed visual-evidence checks. Use when checking or creating plot readability, axes, legends, line overlap, table structure, metric reporting, captions, grayscale accessibility, IEEE one-column/two-column layout, figure/table placement, claim-evidence mapping, reviewer first-impression risks, matplotlib/seaborn or ggplot2 figure code, SVG/PDF/TIFF/PNG export, Illustrator/Inkscape/PowerPoint/Figma finishing plans, or IEEE-style engineering evidence graphics.

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Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

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Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.

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Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).

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。