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Found 273 Skills
Early rug-risk triage for token launches and small DeFi deployments from public data—liquidity lock and pool events, dev and sniper wallet clustering, contract authority and transfer-risk checks, coordinated exits, and evidence-backed risk scores. Use when the user asks for rug pull detection, pump-and-dump signals, launch red flags, LP removal forensics, or cross-chain profit exit tracing—not for front-running trades, harassing teams, or certifying scams without on-chain proof.
Builds, configures, debugs, and optimizes AWS observability using CloudWatch (Logs Insights, Metrics, Alarms, Dashboards, EMF), X-Ray, CloudTrail, and ADOT. Covers Log Insights query syntax (fields, filter, stats, parse, pattern, join, subqueries), alarm configuration (metric, composite, anomaly detection, missing data treatment), dashboard design, custom metrics (PutMetricData, EMF, metric filters), X-Ray tracing (ADOT, sampling rules, annotations vs metadata), ADOT collector config, and CloudTrail auditing. Use when the user mentions CloudWatch, Log Insights, alarms, INSUFFICIENT_DATA, dashboards, custom metrics, EMF, X-Ray, traces, sampling, CloudTrail, who deleted, ADOT, OpenTelemetry, observability, monitoring, synthetics, canaries, or troubleshooting alarm behavior. Do NOT use for application logging setup, container log drivers, or security threat detection.
Salesforce Data Cloud Segment phase. Use this skill when the user creates or publishes segments, manages calculated insights, or troubleshoots audience SQL in Data Cloud. TRIGGER when: user creates or publishes segments, manages calculated insights, inspects segment counts or membership, or troubleshoots audience SQL in Data Cloud. DO NOT TRIGGER when: the task is DMO/mapping/identity-resolution work (use harmonizing-datacloud), activation work (use activating-datacloud), query/search-index work (use retrieving-datacloud), or Standard Data Model (STDM)/session tracing (use observing-agentforce).
MCP server for JavaScript reverse engineering in real browser environments with hooks, breakpoints, network tracing, deobfuscation, and environment reconstruction.
Route durable graph-building requests into one honest mode: assistant-native install, local Python build, incremental refresh, graph query follow-up, or a graphify-style structural fallback for markdown-heavy corpora. Use when the user wants `GRAPH_REPORT.md`, `graph.json`, `graph.html`, repo/corpus relationship tracing, mixed code+docs+asset graphing, or graph-backed architecture understanding that should persist across sessions. Route simple locate/reference work to `codebase-search`, narrative knowledge-base work to `llm-wiki`, and project-memory handoff to `opencontext`.
This skill should be used when the user asks to draft or structure STR reports, suspicious transaction reports, SAR, suspicious activity reports, draft STR, STR narrative, file suspicious activity, AML STR, goAML, FinCEN SAR, suspicion narrative, or MLRO report. Guides jurisdiction-agnostic STR/SAR drafting—narrative structure (who, what, when, where, why suspicious), red flags and typologies, transaction aggregation and chronology, subject identification fields, supporting documentation checklists, quality review before filing, and escalation to MLRO/compliance—not TM rule building (aml-compliance), full LE case management, legal filing duty determination (commercial-counsel), or deep blockchain tracing (blockint skills). Complements aml-compliance, aml-cft, auditor, compliance-engineer, and commercial-counsel.
Use when one Python service must send each agent's, tenant's, team's, or request's spans to its correct Arize space and project using application metadata. Covers dynamic OpenTelemetry routing for custom agent builders and multi-tenant applications, including register_with_routing, set_routing_context, multi-space tracing, and custom span routing.
Automatically discover observability and monitoring skills when working with Prometheus, Grafana, distributed tracing, structured logging, metrics, alerting, dashboards, or monitoring. Activates for observability development tasks.
Generate Go use cases following GO modular architechture conventions (Fx DI, ports/usecase architecture). Use for any business logic operation in internal/modules/<module>/usecase/ - entity operations (create, update, list, delete), infrastructure operations (upload file, send notification), or any domain action requiring metrics, tracing, and validation.
This skill should be used when fixing bugs, implementing features, debugging issues, or making code changes. Ensures understanding of code flow before implementation by: (1) Tracing execution path with specific file:line references, (2) Creating lightweight text diagrams showing class.method() flows, (3) Verifying understanding with user. Prevents wasted effort from assumptions or guessing. Triggers when users request: bug fixes, feature implementations, refactoring, TDD cycles, debugging, code analysis.
Provides NodeReal MegaNode blockchain infrastructure APIs for 25+ chains including BSC, Ethereum, opBNB, Optimism, Polygon, Arbitrum, and Klaytn. Covers standard JSON-RPC endpoints, Enhanced APIs (nr_ methods for ERC-20 token balances, NFT holdings, asset transfers), MegaFuel gasless transactions via BEP-322 paymaster, Direct Route MEV protection, Debug/Trace APIs, WebSocket subscriptions, ETH Beacon Chain consensus layer, Portal API usage monitoring, API Marketplace (NFTScan, Contracts API, SPACE ID, Greenfield, BNB Staking, PancakeSwap, zkSync), non-EVM chains (Aptos, NEAR, Avalanche), and JWT authentication. Use when building blockchain dApps with NodeReal, querying token or NFT data, setting up RPC infrastructure, configuring gasless transactions, protecting against MEV, tracing transactions, verifying smart contracts, resolving .bnb domains, or monitoring validators and API usage.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.