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Develop attack detection for SCADA systems that monitor and control industrial equipment. Compare network traffic and process measurements with normal behavior to identify unauthorized commands, altered sensor data, communication disruption, and indicators of known industrial malware.

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• Introducing intrusion detection in a SCADA environment
• Investigating suspected attacks on industrial control systems
• Creating detection rules for industrial protocol abuse

Unwrap layers of obfuscated PowerShell malware to recover hidden commands, additional malicious code, and server addresses. Combines script structure analysis with execution tracing and decoding tools to support incident investigation and detection work.

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• Investigating an obfuscated PowerShell script
• Recovering hidden commands and additional malicious code
• Extracting suspicious URLs, IP addresses, and file paths

Build a GitLab CI/CD pipeline with automated checks for vulnerable code, dependencies, container images, running applications, and exposed secrets. Covers scanner configuration, consolidated findings, and approval policies for serious vulnerabilities before production deployment.

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• Add automated security scans to .gitlab-ci.yml
• Check dependencies and container images for known vulnerabilities
• Detect credentials and API keys committed to source control

Develop time-series and event-data solutions with TimescaleDB, an extension for PostgreSQL. The skill provides organized references for installation, time-based tables, ongoing aggregate calculations, compression, and performance troubleshooting.

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• Learn how to install and use TimescaleDB
• Design tables for time-series and event data
• Implement time-bucket and continuous aggregates

Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.

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• Scale application capacity automatically as demand changes
• Review CPU and memory allocations against actual usage
• Change the number of application replicas manually

Scans code for security vulnerabilities with CodeQL by tracing data across functions. Builds and checks analysis databases, models project-specific APIs, and saves findings in SARIF, a structured results format, with broad or focused scan options.

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• Scanning a codebase for security vulnerabilities
• Tracing untrusted input through custom APIs
• Choosing broad or high-precision security scans

Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

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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.

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Use when a WINNING ad has to be scaled without breaking it — readiness checks, vertical scaling at plus 20 to 30 percent per step, horizontal scaling into new audiences, creatives, and channels, learning-phase reset risk, how Advantage+ and Performance Max behave when scaled, creative refresh cadence, and the signals that mean stop. Trigger on 'scale ads', 'increase the ad budget', 'scale a winner', 'CPA went up after I scaled', 'how fast can I scale', 'it worked at 50 a day and broke at 200'. Not for — fixing an account that is not working yet, see `03-performance-eval-global`; warm audience plans, see `56-retargeting-plan-global`; proving the winner first, see `19-ab-test-setup-global`.

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Resizes, scales, and adjusts video resolution and dimensions using GPU-accelerated NVENC encoding via Volcengine LAS. Video resizing, video scaling, video upscaling, and video downscaling — change video resolution, enlarge or shrink video dimensions, and adjust video size for different platforms and screens. Video compression by reducing resolution and video transcoding and re-encoding with specific dimension constraints. Supports flexible min/max width and height ranges with aspect ratio preservation strategies (increase, decrease, or disable), including landscape to portrait conversion. Async submit-poll workflow with batch support. Use this skill when the user wants to resize or scale video resolution (upscale/downscale), change video dimensions for different platforms, compress videos by reducing resolution, transcode/re-encode videos with GPU NVENC, adjust aspect ratio including landscape-to-portrait conversion, adapt videos for mobile/web/social media, or batch process multiple videos.

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Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 on SecNumCloud infrastructure). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.

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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.

中文简介正在准备中。当前显示原文说明。

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.

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指导你从固定提交安装外部代码审计技能，先检查源码，再处理已有安装的备份与失败恢复。本技能只负责安装，不执行审计；安装后需另行调用。

• 安装现有代码的审计技能
• 为大规模重构准备审计环境
• 准备审计源码中附带的第三方代码

Use Scapy, a Python library for working with network packets, to inspect saved captures or authorized live traffic. Summarize protocols and connections, identify suspicious patterns, craft test packets, and export findings as structured JSON reports.

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• Investigating saved network captures
• Summarizing traffic volumes, protocols, and connection endpoints
• Checking for SYN floods and DNS data leakage indicators

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

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• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously

Run an AWS Security Agent scan on the workspace — uploads the source to AWS, scans it with the managed Security Agent service, and returns ranked, verified findings with code locations and remediations. Use when the user asks to scan code, find vulnerabilities, run a security scan or review, check security issues, check scan status, show findings, list recent scans, or stop a scan.

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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.

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Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

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Runs a security scan on a deployed Power Pages site, fetches the latest scan report, and produces a plain-language summary. Scans the live site's public surface for vulnerabilities and surfaces issues by severity. Use when the user wants to scan, check, test, audit, or assess a published site, find vulnerabilities on production, view the latest scan report, see previous scan results, run a security audit, or asks "how safe is my live site?", "is my site vulnerable?", "audit my production site" — even if they say "find issues" or "check for problems" without mentioning "scan" or "security".

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Perform advanced network scanning and service enumeration using Nmap for penetration testing and security assessments. Use this skill when conducting network reconnaissance, port scanning, service version detection, OS fingerprinting, and vulnerability scanning. Covers stealth scanning, firewall evasion, NSE script usage, and network mapping for infrastructure penetration tests.

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Set up CodeQL security scanning in GitHub Actions or with the local CLI. Get guidance on language settings, build steps, analysis rules, result uploads, and troubleshooting so you can configure scans and interpret their findings.

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• Adding security scans to GitHub Actions
• Running local scans with the CodeQL CLI
• Investigating failed builds or result uploads

Deploy and operate Falco to monitor running containers in Kubernetes or Docker. Choose a driver, validate detection rules, confirm alerts for suspicious activity, and forward those alerts to a central security monitoring system.

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• Installing Falco on a Kubernetes cluster
• Choosing a driver for the Linux environment
• Checking alerts for suspicious container activity

Analyze malicious JavaScript from phishing pages, payment data theft scripts, and malware downloaders. Unwrap encoded strings and nested execution layers to reveal hidden behavior, identify stolen data fields, and document suspicious destinations for incident response.

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• Analyzing obfuscated scripts on phishing pages
• Investigating payment data theft on checkout pages
• Examining JavaScript that downloads further malware