「zero」の検索結果

Enable nullable reference types in a C# project and systematically resolve all warnings. USE FOR: adopting NRTs in existing codebases, file-by-file or project-wide migration, fixing CS8602/CS8618/CS86xx warnings, annotating APIs for nullability, cleaning up null-forgiving operators, upgrading dependencies with new nullable annotations. DO NOT USE FOR: projects already fully migrated with zero warnings (unless auditing suppressions), fixing a handful of nullable warnings in code that already has NRTs enabled, suppressing warnings without fixing them, C# 7.3 or earlier projects. INVOKES: Get-NullableReadiness.ps1 scanner script.

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Senior PM agent with 6 knowledge domains, 30+ frameworks, 12 templates, and 32 SaaS metrics with formulas. Pure Markdown, zero scripts.

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。

To create new CLI skills following Anthropic's official best practices with zero manual configuration. This skill automates brainstorming, template application, validation, and installation processes while maintaining progressive disclosure patterns and writing style standards.

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。

Neon is a serverless Postgres platform that separates compute and storage to offer autoscaling, branching, instant restore, and scale-to-zero. It's fully compatible with Postgres and works with any language, framework, or ORM that supports Postgres.

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。

Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.

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。

0ティックパルスの論理、構成方法、および使用に関するガイドラインです。

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

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。

Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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。

Build content-focused websites with Astro — zero JS by default, islands architecture, multi-framework components, and Markdown/MDX support.

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。

bu skill covers Dağıt:ing HashiCorp Vault for centralized secrets management across cloud environments, including dynamic secret generation for databases and cloud providers, transit encryption, PKI certificate management, and Kubernetes integration. It addresses eliminating hardcoded credentials from application code and CI/CD pipelines by implementing short-lived, automatically rotated secrets.

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。

Enforce Kubernetes network segmentation using Calico CNI network policies and global network policies to control pod-to-pod traffic, restrict egress, and implement zero-trust microsegmentation.

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。

Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.

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。

Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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。

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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。

Configuring Google Cloud Identity-Aware Proxy (IAP) to enforce per-request identity verification for Compute Engine, App Engine, Cloud Run, and GKE services using access levels, context-aware policies, and programmatic access with service accounts.

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。

Database migrations, schema evolution, data transformation, and zero-downtime deployments. Use when creating migrations, evolving database schema, transforming data, or planning deployment strategies.

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。

Tell the user (or an agent) exactly what machine they are on — chip, cores, RAM, NPU TOPS, memory bandwidth, security posture, DSH tier, and the right Ollama model to pull. Refreshes the canonical profile at agents/core/.mesh/machine.json on every call. Reads the result via agents.core.machine — zero re-probing in application code.

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 network engineer specializing in cloud and hybrid network architectures, security, and performance optimization. Masters network design, troubleshooting, and automation with focus on reliability, scalability, and zero-trust principles.

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 infrastructure security engineer specializing in DevSecOps, cloud security, and compliance frameworks. Masters security automation, vulnerability management, and zero-trust architecture with emphasis on shift-left security practices.

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。

The default code-driven browser path for interactive browsing work: drives any browser from code with token-efficient a11y snapshots (57% smaller via compactSnapshot), ref-based clicks, coordinate control (CUA), viewport pinning, CAPTCHA solving (reCAPTCHA, Turnstile, hCaptcha, slider, OCR), Chrome MV3 APIs (tabs/bookmarks/history/downloads/topSites), network snoop (read API JSON instead of DOM), flight-recorder trace (jsonl + HAR + screenshots), OOPIF-aware snapshots, dialog policy, human handoff for login/OTP, device emulation, request routes, stealth via CloakBrowser as the default owned engine with zero exposed CDP ports, and an attached engine that drives the user's own signed-in browser through BrowserSkill (connectBrowserSkill, bskSnapshot, one-click onboarding via bskOnboard). MUST USE for interactive browser work: scraping, blocked/WAF/JS-rendered pages, logins, the user's logged-in sites and open tabs, extension popups, form filling, screenshots, web QA, CAPTCHAs. The ulw-research browsing lane runs on ultimate-browsing, not this skill.

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。

Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

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。

Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

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。

Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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。