Kerberos 委派攻击(非约束/约束/RBCD)。当 BloodHound 发现委派配置、或已获取有 SPN 的服务账号/机器账号控制权时使用。通过 S4U 协议滥用可实现跨服务模拟任意用户,常用于域内权限提升和横向移动。
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。
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概要と使いどころ
Kerberos 委派攻击(非约束/约束/RBCD)。当 BloodHound 发现委派配置、或已获取有 SPN 的服务账号/机器账号控制权时使用。通过 S4U 协议滥用可实现跨服务模拟任意用户,常用于域内权限提升和横向移动。
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 Red Hat Ansible Automation Platform (AAP) with HashiCorp Vault Enterprise for dynamic SSH credential management. Use when: (1) Configuring Vault SSH secrets engine with AppRole auth for AAP, (2) Creating AAP credentials backed by Vault signed SSH certificates, (3) Provisioning infrastructure with Terraform/Ansible for AAP-Vault integration, (4) Setting up multi-tenant credential management, (5) Configuring golden images to trust Vault SSH CA, (6) Implementing credential rotation strategies. Based on HashiCorp validated pattern.
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。
Background information about Lendtrain powered by Atlantic Home Mortgage — company history, credentials, founder bio, and contact information for borrower trust-building.
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 you need to apply Java secure coding best practices — including validating untrusted inputs, defending against injection attacks with parameterized queries, minimizing attack surface via least privilege, applying strong cryptographic algorithms, handling exceptions securely without exposing sensitive data, managing secrets at runtime, avoiding unsafe deserialization, and encoding output to prevent XSS. This should trigger for requests such as Review Java code for secure coding. Part of cursor-rules-java project
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。
Checks Next.js Server Actions ('use server') for missing authentication/authorization before mutations, missing input validation, and untrusted client-supplied IDs — treating each action as a public HTTP endpoint.
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。
Harden iOS/Android apps you ship: storage, TLS pinning policy, IPC, and backend trust. You still enforce authz on the server.
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。
Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.
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。
Polished, business-ready design with modern typography, structured layouts, and a trustworthy visual identity.
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。
Conversion-focused design that removes friction and guides users toward action through clarity, trust, and speed.
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-system setup for GNOME apps per language: Python/PyGObject, Rust/gtk-rs, C, Vala, JavaScript/GJS — meson files, blueprint-compiler wiring, Flatpak manifests. Use when choosing a language for a GNOME app or setting up its build.
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. Analyzes diff against the base branch for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues. Use when asked to "review this PR", "code review", "pre-landing review", or "check my diff". Proactively suggest when the user is about to merge or land code changes. (gstack)
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。
Set up gbrain for this coding agent: install the CLI, initialize a local PGLite or Supabase brain, register MCP, capture per-remote trust policy. One command from zero to "gbrain is running, and this agent can call it." Use when: "setup gbrain", "connect gbrain", "start gbrain", "install gbrain", "configure gbrain for this machine". (gstack)
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。
Reference for the Adobe AEM CLI (@adobe/aem-cli, formerly the helix-cli npm package; commands `aem up`, `aem import`, `aem content`) — installation, the local Edge Delivery dev server, .env / AEM_* configuration, HTTPS/TLS, proxy & certificate trust, content sync with da.live, and troubleshooting. Use when installing, running, or configuring the aem/hlx CLI, when `aem up` fails (port conflicts, cert errors, proxy 404s, pipeline vs. local-file confusion), or when migrating from the old helix-cli package. Do NOT use for da.live content-format rules or the DA Source API contract (use da-content); do NOT use for writing EDS block code (use content-driven-development).
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 trust boundaries, input validation, permissions, abuse resistance, secrets, and safe failure behavior.
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。
Pick a product archetype from a curated catalog before any UI gets built — the single biggest lever for not looking vibecoded. Routes the request to one of B2B-productivity, enterprise-trust, premium-consumer, playful-consumer, editorial, brutalist-distinctive, dev-tool, or marketing-landing, and returns a complete design philosophy: typography pair, color logic, motion curve, density, icon stance, reference sites, and a "feels like X" claim. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, classify a product visually, says "what should this look like", "make this feel like [Linear/Stripe/Apple/Duolingo/...]", "give me a design direction", "what's the right aesthetic for this", or when frontend-design routes here during archetype selection. Sub-skill of frontend-design.
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。
Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.
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。
Harden an application against common security risks — validate inputs at boundaries, least-privilege access, safe secrets handling, dependency hygiene, and secure defaults. Load when shipping user-facing code, adding auth/session flows, handling untrusted data, exposing APIs, or the user asks for "security hardening", "OWASP hardening", "secure this feature". Not for skill-library security gates (use secure-skill family).
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。
Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.
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。
Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.
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。
Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.
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。
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
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 development-set disagreements between human annotations and one Coval text LLM judge, then propose a focused prompt revision. Use coval-calibrate-metric for independent trust measurements.
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。
Validate one Coval judge against independent human labels, with grouped development/test separation, class-specific errors and uncertainty. Use for trust claims or calibration; without real labels, prepare review rather than inventing ground truth.
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。
Maps intelligence for local SEO: geo-grid rank tracking, GBP profile auditing via API, review intelligence across Google/Tripadvisor/Trustpilot, cross-platform NAP verification, competitor radius mapping, and LocalBusiness schema generation. Three tiers: free (Overpass + Geoapify), DataForSEO, and DataForSEO + Google. Use when user says "maps", "geo-grid", "rank tracking", "GBP audit", "review velocity", "competitor radius", or "SoLV".
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。