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Found 361 Skills
Use this skill when diagnosing, configuring, or monitoring NICs for AF_XDP / XDP workloads. Covers driver detection, hardware queue configuration, ring buffer sizing, RSS indirection table management, interrupt coalesce tuning, offload control (GSO/GRO/TSO/LRO), VLAN offloads, Flow Director (FDIR) rules with loc pinning and ixgbe wipe bug workaround, RPS/XPS queue CPU mapping, sysctl network tuning, CPU core pinning and NUMA awareness, hardware queue and drop monitoring, softirq and rx_missed_errors analysis, BPF program inspection with bpftool (prog dump xlated, net show), kernel tracing via ftrace and dmesg, perf profiling and flamegraphs, IRQ-to-queue-to-core mapping, bonding interface diagnostics, socket inspection, and a quick diagnostic checklist.
Discovers business domains in a Swift codebase by tracing what users can DO — not by reading folder names or architecture docs. Maps each domain's vertical slice (Types → Config → Repo → Service → Runtime → UI), identifies providers (external SDK bridges), and separates cross-cutting concerns. Produces a domain map that drives all downstream decisions: folder structure, SPM targets, enforcement specs, migration plans. Use this skill whenever the user wants to understand their codebase domains, find what's cross-cutting vs domain-specific, restructure a Swift project, figure out where code belongs, or map a product's capabilities to architectural boundaries. Triggers on "what are my domains", "where does this belong", "map this codebase", "what's cross-cutting", "organize this project", "is this a domain or infra", "restructure this", "architecture review", or any request to understand the business domain structure of a Swift codebase.
Operates as an on-chain forensics investigator using only public chain data and OSINT—tracing flows across chains, clustering addresses, reviewing contracts for risk patterns, detecting scam vectors, and producing evidence-backed reports. Use when the user asks for blockchain investigation, forensic tracing, scam or rug analysis from public data, transaction trail documentation, or structured intelligence reports without private keys or insider access.
Use the unified Opper SDKs (`opperai` package for both Python and TypeScript, with built-in agent support) for AI task completion, structured output with Pydantic / Zod / JSON Schema, knowledge base semantic search, streaming, tracing, tool use, and multi-agent composition. Use this skill whenever the user is writing Python or TypeScript code that imports `opperai`, builds an Opper agent, or asks how to do anything Opper-related in code — even if they don't explicitly name the SDK. Both languages live in one repo with parallel numbered examples; agents are part of the SDK, not a separate package.
TypeScript-native multi-agent orchestration framework that decomposes goals into task DAGs automatically with MCP and live tracing
Assess chemical and drug toxicity via adverse outcome pathways, real-world adverse event signals, and toxicogenomic evidence. Integrates AOPWiki (AOPWiki_list_aops, AOPWiki_get_aop) for mechanism- level pathway tracing, FAERS for post-market adverse event quantification, OpenFDA for label mining, and CTD for chemical-gene-disease evidence. Produces structured toxicity reports with evidence grading (T1-T4). Use when asked about toxicity mechanisms, adverse outcome pathways, AOP mapping, FAERS signal detection, or chemical-disease relationships for drugs or environmental chemicals.
Check whether a tenant's DEPLOYED Sumsub config actually satisfies a regulation/policy document — tracing each requirement to where it is collected, scored, and ENFORCED, and flagging "collected-but-not-enforced" gaps. TRIGGER when the user has a regulation/policy/requirements doc (PDF or text) and wants to verify the live config matches it, audit a client's setup against compliance rules, "does my config satisfy this regulation", "check conformance / gap analysis", or close the loop after configuring with the create-* skills. SKIP for building config (sumsub-create-*) or for generating a config plan from a regulation (sumsub-analyze-regulation). For pure hygiene linting with no regulation, run this skill's bundled lint_config.py sub-pass directly.
Analyzes observability signals from customer GenAI applications with DQL. Reads OpenTelemetry GenAI spans and LLM evaluation bizevents. Use for: golden signals (traffic, errors, latency, saturation); LLM signals (model, provider, tokens); cost/token analytics, usage attribution, and prompt caching; agent signals (tool calls, steps, failures, loop detection, Smartscape topology); conversation/session analytics; guardrails (blocked/truncated responses); and evaluation signals (quality, pass/fail). Trigger: "LLM latency", "token usage by model", "cost by model and provider", "cost per conversation", "who is driving token spend", "do I have prompt caching", "failing agent tool calls", "find runaway agents", "responses truncated or blocked", "failed evaluations", "am I hitting rate limits", "token throughput / TPM", "provider throttling or 429s". Do NOT use for: Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI tracing (dt-obs-tracing).
Hunt threat-intelligence indicators of compromise (IoCs) across Dynatrace logs and spans and produce a 0-100 threat-exposure score. Extracts and normalizes IoCs — IPs, Domains (hostnames included), URLs, Emails, CVEs, File hashes (md5/sha1/sha256), MITRE TTPs — from unstructured reports, advisories, advisory URLs, pasted text, or STIX, then hunts them in fetch logs and fetch spans. Trigger: hunt these IoCs, am I exposed to this threat, check these indicators in my logs and traces, threat exposure report, extract IoCs from this advisory URL, search these hashes/domains/IPs in my environment. Routes CVE-to-vulnerability and IP/MITRE-to-detection legs to dt-sec-insights. Do NOT use for: querying security.events directly (vulnerabilities, detections, compliance, THREAT_REPORT — use dt-sec-insights); general log queries not tied to an IoC hunt (use dt-obs-logs); general span/trace analysis (use dt-obs-tracing); explaining DQL syntax (use dt-dql-essentials).
Transform a user-supplied photo into an expressive minimal zine poster made only from original source-derived illustration, an artistic proposition, emotional tension, visual metaphor, spacious negative space, art-directed high-chroma color, and unconstrained authorial typography. Let wording, language, amount, placement, type voices, scale, direction, legibility, and image interaction follow expression and aesthetic judgment rather than presets. Preserve source orientation by default with a 3:5 portrait output or 5:3 landscape output. Add source-derived distributed supporting accents and a natural isolated-contour option alongside adaptive paper-edge transitions. Support an exact `单色块模式` trigger for one contiguous saturated color field with all remaining forms in neutral ink. Use for authored abstract or editorial reinterpretations that communicate an emotion or idea without embedding, cropping, tracing, or preserving the original photographic material in the final image.
OpenTelemetry in Node.js / JavaScript / TypeScript — NodeSDK, declarative YAML configuration, auto-instrumentations, ESM vs CJS import patterns. Use when adding, reviewing, or configuring OpenTelemetry in a Node.js service. Triggers on "setup otel in node", "js telemetry", "node tracing setup", "NodeSDK", "auto instrumentation node", "TracerProvider node", or any Node.js-related OTel question.
OpenTelemetry browser/RUM mechanics for SPAs and MPAs. Use for “browser OTel,” “frontend observability,” “Web Vitals,” `sdk-trace-web`, `WebTracerProvider`, `browser-sdk`, browser instrumentations, page-load or route tracing, sessions, clicks, console capture, JavaScript errors, or frontend-to-backend trace correlation. Browser telemetry is privacy- and volume-sensitive, and experimental packages move quickly. Not for Node.js service instrumentation or Collector-only configuration.