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Use when a startup is approached by a prospective partner and someone has to decide should we sign this partner, at what partner tier (referral / reseller / OEM / SI-consulting / strategic alliance), with what joint GTM commitment, and at what revshare. Classifies partner tier from independent-demand evidence vs. preferential-terms hunting, designs a 90-day joint GTM plan, models revshare against direct-sale margin, and surfaces kill criteria for unwinding under-performing partnerships. For Head of Partnerships, Head of BD, and Founder-CEOs doing reseller agreement, OEM deal, or strategic alliance review — not technical sale enablement, not channel cost economics, not M&A.

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Hourly warehouse health sweep for {{warehouse_schema}}. Checks every monitored table's freshness against its SLA, compares row counts to the table's own trailing baseline, diffs the schema for drift, and checks for failed or stalled loads. Posts anomalies to {{alert_channel}} and drafts a GitHub issue in {{incident_repo}} with the likely cause. Read-only across the warehouse — never modifies data, tables, or pipelines.

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Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

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State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

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Comprehensive offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools) and T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Integrates tooling such as confused for dependency confusion scanning and dependency-check for known vulnerable component detection. Each technique section provides reproducible proof-of-concept patterns, detection guidance for defenders, and engagement-safe execution notes for authorized red team operations.

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Builds remote MCP (Model Context Protocol) servers on Cloudflare Workers with tools, OAuth authentication, and production deployment. Generates server code, configures auth providers, and deploys to Workers. Use when: user wants to "build MCP server", "create MCP tools", "remote MCP", "deploy MCP", add "OAuth to MCP", or mentions Model Context Protocol on Cloudflare. Also triggers on "MCP authentication" or "MCP deployment". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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Use when you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc, folder, or convention; you are asking "should this be a skill, a note, or a chain file?"; you inherited a seat and want to know what SHOULD exist; or you are about to duplicate knowledge that already has a home. Also the trace-to-root: how a seat orients by reading one filename at each level from where it stands up to the fleet.

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Use when the system around you does not make sense yet: you just booted into a seat and do not know how the pieces fit; someone said rig, pod, seat, fleet, topology, or slice and you are not certain what they mean here; you are unsure what kind of rig you are in or what it is for; you do not know how skills reach you or where context comes from; or you are about to act on a guess about how OpenRig works. Gives the runtime mental model fast, so you stop guessing.

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External recon for software supply-chain attack surface — package-namespace squatting candidates, dependency-confusion vulnerabilities, GitHub Actions injection openings, container image registry exposure, SBOM mining, internal-package-name leakage, and CI/CD configuration exposure. Reconnaissance and identification ONLY — actual package publishing / typosquat attacks are EXTERNAL-OFFENSIVE and require explicit written sign-off because they can affect the entire npm/PyPI ecosystem. Use when the target has a public GitHub org, when their build artifacts/SBOMs are reachable, when their docker images are on Docker Hub/GHCR, or when you find internal package names in their JS bundles.

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Operational arsenal for authorized external red-team and bug-bounty recon. Concrete probes, wordlists, regexes, dorks, curl one-liners for: subdomain enum, GraphQL/Swagger/REST discovery, identity fabric (Entra/Okta/ADFS/Google/SAML/M365 deep — Teams/SharePoint/OneDrive), cloud bucket enum (S3/GCS/Azure), CDN/WAF bypass, origin discovery, vendor fingerprinting (Citrix/F5/Pulse/Fortinet/PaloAlto/Cisco/VMware), CI/CD exposure, 48-pattern secret-scan catalog (AWS/GCP/GitHub/Stripe/Slack/Anthropic/OpenAI/Atlassian/DataDog/npm/PyPI), Postman workspaces, breach correlation (HudsonRock/HIBP/DeHashed/IntelX), TLS/JA3 audit, certificate transparency, JS endpoint extraction, package registry leaks, mobile/APK recon, sat imagery, sector-specific recon (healthcare DICOM, finance SWIFT, ICS/SCADA Modbus/BACnet). Detail content in 15 modular reference files, loaded on demand. Use for any authorized recon: scoping, asset discovery, attack-path mapping, secret triage, severity scoring.

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Reparent a batch of GameObjects under a new parent in the currently opened Prefab or active Scene. Per-item failures are reported in the returned status string instead of aborting the batch. Use 'gameobject-find' to locate the GameObjects first.

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Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-loop changes, nor for plain PyTorch, TensorFlow/Keras, other frameworks, deployment, POC/production lifecycle, or experiment workflows.

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Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

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Use this skill when the user needs to locate authoritative information about NVIDIA DOCA without access to the source tree — finding the right docs.nvidia.com page for a library/service/tool, identifying which DOCA libraries are installed and at what version, locating a sample on disk or its public GitHub source, decoding an on-disk path under /opt/mellanox/doca, or recovering from a 404'd or renamed doc URL. Trigger even when the user does not explicitly mention 'DOCA' or 'docs.nvidia.com' — typical implicit phrasings include 'where can I read about this library', 'which version do I have installed', 'where is the sample for X', 'this NVIDIA URL is broken what is the new one', 'what is in /opt/mellanox/doca', or 'where can I ask NVIDIA about this'. Refuse and route elsewhere for hands-on programming patterns, env prep and install verification, library API tutorials, or hardware/firmware mutation — those belong to doca-programming-guide, doca-setup, the per-library skills, and doca-hardware-safety.

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Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

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Provides malware analysis and network traffic techniques for CTF challenges. Use when analyzing obfuscated scripts, malicious packages, custom crypto protocols, C2 traffic, PE/.NET binaries, RC4/AES encrypted communications, YARA rules, shellcode analysis, memory forensics for malware (Volatility malfind, process injection detection), anti-analysis techniques (VM/sandbox detection, timing evasion, API hashing, process injection, environment checks), or extracting malware configurations and indicators of compromise.

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Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake. Answers questions about storage access tiers (hot, cool, cold, archive), when to use each tier, and tier comparison. Provides object storage, SMB file shares, async messaging, NoSQL key-value, and big data analytics. Includes lifecycle management. USE FOR: blob storage, file shares, queue storage, table storage, data lake, upload files, download blobs, storage accounts, access tiers, storage tiers, hot cool cold archive, storage tier comparison, when to use storage tiers, lifecycle management, Azure Storage concepts. DO NOT USE FOR: SQL databases, Cosmos DB (use azure-prepare), messaging with Event Hubs or Service Bus (use azure-messaging).

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Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.

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Cloudflare's official cf CLI (beta) for the public Cloudflare API and Workers projects. Use for finding and running Cloudflare commands from the terminal — sign-in and profiles, cf cli search, cf schema, cf deploy/dev/build, resource commands for DNS, zones, D1, KV, and R2 — and for choosing between cf and Wrangler in a project.

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Design, configure, troubleshoot, or review Cloudflare One Zero Trust and SASE deployments. Use cloudflare-one-migrations for migration planning from other vendors.

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Discover and choose Cloudflare products for apps, APIs, AI agents, storage, networking, and security. Use for architecture and product selection, including when the user describes a need without naming a Cloudflare product; then find the relevant skill or documentation.

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Migrate Cloudflare Sandbox apps from stable @cloudflare/sandbox to @cloudflare/sandbox@next (SDK 1.0 preview). Use sandbox-next for apps already on the preview.

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Turns one-shot external recon into a continuous monitoring program. Covers the scheduled re-scan-and-diff loop (baseline snapshot -> interval sleep -> re-scan -> asset/finding delta -> threshold-gated webhook alert), the scan-to-scan diff engine (new/removed/changed assets by a tracked-attribute table, new/resolved findings by a stable cross-scan fingerprint), adversary CTI / chatter monitoring across six public feeds (ransomwatch, ransomware.live, HackerNews Algolia search, Reddit security-subreddit RSS, GitHub Gist code-search, public Telegram channel scraping) with a source-kind-aware severity engine (leak-site/forum/telegram/paste tiers, CRITICAL through INFO), literal/glob/regex watchlist pattern matching, full-corpus capture with retroactive rescan on new watchlist entries, infrastructure-tracking-over-time discipline (certificate-transparency, passive-DNS, port/service, and typosquat re-enumeration cadence, and what a genuine 'perimeter drift' event looks like in the diff output), a five-state finding-lifecycle state machine (open/triaged/risk_accepted/resolved/false_positive) with per-severity SLA and fingerprint-based cross-scan dedup and auto-resolve/reopen rules, the alert-fatigue trap where a lifecycle-unaware rule re-fires on an already-accepted finding, a durable retry/backoff alert-outbox pattern ('queued is not delivered'), and copy-paste bash-cron plus PowerShell-Scheduled-Task recipes for a re-scan+diff loop with the Slack-compatible webhook payload shape. Passive OSINT and analysis only -- no new active-intrusion technique. Use when setting up ongoing monitoring for a retainer or MSSP engagement, tuning alert thresholds to avoid fatigue, triaging a finding's lifecycle status, investigating adversary chatter about a brand, building a 'what changed on the perimeter since last week' report, or deciding whether a persisting finding should re-alert.

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。