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概要と使いどころ
Fetch brand SVG logos and cloud architecture icons (AWS, Azure, GCP) from theSVG. Use when the user asks for a brand logo, company icon, framework mark, service icon, or any "icon/logo for X" where X is a real brand or cloud service. Returns ready-to-use CDN URLs or raw SVG markup.
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。
Process use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".
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Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.
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。
Org-grade attack-surface mapping: given a company's legal identity, discover its ENTIRE owned internet footprint — corporate family -> owned domains -> owned netblocks/ASN -> live assets — with attribution discipline, not just DNS breadth. The org-first attribution pyramid (legal entity -> LEI/registration -> corporate family -> owned domains -> owned netblocks/ASN -> live assets). Corporate-identity resolution via the GLEIF LEI API (legal name -> LEI, exact-LEI direct-children expansion, downward-only depth-capped BFS, NEVER a name re-resolution), SEC-EDGAR full-text search + Exhibit-21 subsidiary entity names, OpenCorporates entity corroboration, Wikidata SPARQL corporate graph (P856/P355/P749/P1830). Domain attribution via reverse-WHOIS (WhoisXML preview-then-purchase quota guard, SecurityTrails associated-domains — both paid), crt.sh O= certificate-transparency organization pivot (keyless), infrastructure correlation (shared NS/MX/SaaS-TXT, netblock membership, reverse-DNS PTR), and the independent-evidence combiner (1-prod(1-w_i), rule of three, OwnerTier NONE/WEAK/MODERATE/STRONG/CONFIRMED) — discover-only `related:` candidates are NEVER auto-scanned. Netblock/ASN attribution via org-first RIR queries (ARIN Whois-RWS org-handle search, RIPE DB organisation + inverse-org search) that recover 'dark netblocks' with no DNS link to the seed, ASN discovery (RIPEstat + BGPView union), the HYPERSCALER-SCOPE GUARD (never attribute a whole AWS/GCP/Azure/Cloudflare announced range to a tenant — keep only the seed-containing block, tag shared_hosting_cdn), and org-identity-seeded internet-scan-index queries (Shodan/Censys/ZoomEye/FOFA/BinaryEdge org: filters + an always-on keyless crt.sh fallback). Promote-to-scan triage ranks forgotten discover-only netblocks by remote-access exposure (gateway-vendor/KEV/control-plane/datastore port scoring) into an operator queue. Attribution confidence rubric + anti-patterns (namesake grafting via GLEIF name re-resolution, single-signal ownership, hyperscaler over-attribution, privacy-WHOIS pivot poisoning, RIR org-name collisions). Passive/keyless-first OSINT only — every paid-key dependency is enrichment on top of a keyless core, never a hard requirement. Use when mapping an organization's full corporate-family internet footprint, resolving a legal entity to its LEI/subsidiaries, discovering domains/netblocks/ASNs an org owns beyond its one seed domain, auditing M&A/shadow-IT sprawl, or scoping an engagement that starts from a company NAME rather than a domain.
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。
Organization-grade cloud and supply-chain attack-surface discovery: S3/GCS/Azure Blob bucket discovery via observed-name mining (CNAME/cert-SAN/Wayback) and bounded two-class permutation (6 prefixes x 15 suffixes on trusted tokens, bounded target-bound expansion on subdomain stems), existence (HEAD/GET) vs public-listing confirmation, object-key triage into 9 value tiers (database dumps, credentials, IaC state, kubeconfig, VCS dirs, config, archives, PII, logs), dangling-CNAME bucket-takeover detection, and the ownership-gated severity model that stops an unattributable public bucket from becoming a false CRITICAL; the fully offline AWS-account-ID recovery from a leaked AKIA/ASIA/AROA access key (base32 decode, runnable stdlib Python, canonical test vector, AWS-documentation-example-ID screening); dependency-confusion confirmation for npm/PyPI (internal-signal classifier -- private-registry binding vs org-namespace match -- paired with a read-only public-registry 404 check and the npm scope-claimability nuance the public search API misses); and passive cloud-native/container/Kubernetes/CI control-plane fingerprinting (Lambda URLs, API Gateway, Cloud Run, App Service, kubelet/etcd/K8s API/dashboard, Jenkins/GitLab/Argo CD) as an org-attribution and exposure surface. Passive/discovery only -- no exploitation, no credential submission, no active control-plane confirmation (a stage-6 validate_cloud active tier is described but out of scope). Use when enumerating a target's cloud storage footprint, recovering an AWS account ID from a leaked key, confirming a supply-chain dependency-confusion vector, or fingerprinting cloud-native/K8s/CI infrastructure for an authorized external recon engagement.
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。
Google Calendar CLI for listing calendars, viewing/creating/updating events, and checking availability.
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。
Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.
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。
Turn multiple product shots, event footage, travel clips, gameplay moments, UGC/product footage, B-roll, or mixed media into social-ready reels, highlights, recaps, or montage-style short videos from existing project media.
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。
Deploy to Cloudflare (Workers, R2, D1), Docker, GCP (Cloud Run, GKE), Kubernetes (kubectl, Helm). Use for serverless, containers, CI/CD, GitOps, security audit.
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。
Whenever diagrams or mermaid is mentioned
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。
自监控指标、告警代码标准
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 workflow, known as RIPER-5, is designed exclusively for handling complex software engineering tasks such as system design, architectural refactoring, bug diagnosis, performance optimization, and multi-component integration.
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。
按 Conventional Commits 规范编写提交信息。在创建 git commit 时使用。
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。
提交或推送前的安全检查，扫描 API Key、访问令牌等敏感信息。
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。
E2E Testing Standards Guide. Use this rule when you need to write or execute E2E tests, including test naming conventions, test workflows, and test behavior templates. This rule should be used for end-to-end integration testing scenarios to ensure test standardization and maintainability. Example: When writing new E2E tests, use this rule to ensure proper test format and workflow compliance.
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。
Design document writing conventions. Use when writing or reviewing technical design documents.
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。
OMC agent catalog, available tools, team pipeline routing, commit protocol, and skills registry. Auto-loads when delegating to agents, using OMC tools, orchestrating teams, making commits, or invoking skills.
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。
Code review behavioral standards. Reference during code review to ensure consistent quality checks from a QA perspective.
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, run, debug and ship 3D city games in Godot 4 — procedurally generated cities (block grids, districts, weighted building typologies, MultiMesh batching, facade shaders, terrain, day/night, landmarks), first-person controllers, headless verification, Blender-generated .glb assets, rigged character and animation packs, real OpenStreetMap-derived cities, WebAssembly export, and cloud GPU streaming on GCP. Use this skill whenever the work involves Godot or a .gd/.tscn/.gdshader/project.godot file; whenever someone wants to start, set up, clone onto a new machine, or stand up a 3D game or city generator; whenever they mention procedural city, city generator, block grid, building typology, MultiMesh, facade shader, terrain heightmap, navmesh, Godot export templates, export presets, headless Godot, .glb pipeline, Blender asset generation, character rigs or borrowed animations; and whenever they want to deploy or share a game — web export, SharedArrayBuffer/COOP/COEP, Selkies, pixel streaming, GPU VM, or "let people play this without installing anything." Reach for it even on vague asks like "get this running", "why is my city slow", "my model faces backwards", "make it work on a cloud machine", or "set this up somewhere else too" when a Godot 3D project is in play.
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。
Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker CLI; wants to fine-tune, SFT, RFT, or RL a model on Tinker; asks about Tinker models, context windows, LoRA rank, sampling params, loss functions (cross_entropy, importance_sampling, ppo, cispo, dro), Datum construction, forward_backward or optim_step; wants to sample from, download, resume, merge, or export a Tinker checkpoint; or asks what a Tinker run will cost. Whenever Tinker work is initialized in a project, this skill also creates a TINKER_PRICING.md there so prices sit next to the training code. Reach for it even on vague asks like "train a model on this data" or "get inference working" when Tinker is the platform in play.
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。
Verified prompt templates and structures for Seedance 2.5 / 2.0 video generation, distilled from the highest-performing published cases on goodcase.ai. Use when the user asks to write, revise, or review a Seedance video prompt, or asks how to prompt Seedance for a specific kind of shot (narrative short, ad/commercial, UGC talk-to-camera, dialogue with lip-sync, action, product reveal, retro home video, travel film, pet or animal lead, comedy skit, horror, fantasy or sci-fi spectacle, 3D cartoon, car spot, fashion film, sports, storyboard image to video, and similar). Also use when the user names Seedance, 即梦视频, or Seedance 2.5/2.0 directly.
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。
Prepare FDA/EMA regulatory submissions (IND/NDA/BLA/510(k)/PMA/MAA) in CTD format. Generate module outlines (Modules 1-5), run ICH compliance checks (ALCOA+, E6 GCP, Q1-Q12), verify data integrity, and produce submission-ready dossiers with cover letters and checklists.
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Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
Condenses a full study into conference-submission abstract format. Use when adapting a manuscript abstract or study summary to meet a specific conference's word limit, structured format (Background/Methods/Results/Conclusion), character limits, or required section headings. Also triggers on "adapt my abstract for [conference]", "shorten my abstract to 250 words", "reformat for ASCO/ASGCT/SfN/AACR", "I need a conference abstract", or "cut my abstract to fit the word limit".
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。