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Found 1,628 Skills
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
Social Engagement Benchmark for cross-platform social media engagement benchmark collection, research, monitoring, analysis, and export. Use when the user asks to scrape, extract, collect, export, monitor, research, analyze, or find browser-visible cross-platform social media data for this workflow: Collect comparable engagement metrics for creators, brands, competitors, or campaigns. Covers searches such as Social Engagement Benchmark, cross-platform social media engagement benchmark scraper, cross-platform social media engagement benchmark extractor, cross-platform social media engagement benchmark export, cross-platform social media engagement benchmark research. Supports public or authorized browser-visible data through BrowserAct.
Lightweight scanner tracking Smart Money market concentration across all Hyperliquid assets. Flags assets accelerating up the ranks before they become crowded top-3 plays. IMMEDIATE_MOVER signal fires on 10+ rank jumps with quality filters (erratic history, velocity gate, trader count floor, max leverage check). One API call per scan, runs every 60 seconds. Use when detecting SM rotations, finding emerging opportunities early, or monitoring rank acceleration patterns.
Guide post-trade compliance monitoring and trade surveillance system design. Use when building alert logic to detect churning, front-running, cherry-picking, layering, spoofing, wash trading, or marking the close, implementing post-trade best execution review, evaluating allocation fairness with pro-rata verification or dispersion analysis, designing exception-based monitoring workflows with escalation paths, correlating trading with MNPI events for insider trading detection, building personal trading surveillance for preclearance and blackout enforcement, determining SAR or blue sheet or CAT reporting triggers, or tuning surveillance thresholds to reduce false positives. Also covers turnover ratios, cost-to-equity ratios, and investigation case management.
When the user wants to assess supplier risks, monitor supplier health, or develop risk mitigation strategies. Also use when the user mentions "supplier risk assessment," "supply chain risk," "business continuity," "supplier monitoring," "supply disruption," "risk scoring," "supplier financial health," or "contingency planning." For initial supplier selection, see supplier-selection. For overall supply chain risk, see risk-mitigation.
Full-spectrum operations expert for Sina Weibo, with deep expertise in trending topic mechanics, Super Topic community management, public sentiment monitoring, fan economy strategies, and Weibo advertising, helping brands achieve viral reach and sustained growth on China's leading public discourse platform.
Validate release readiness with evidence-based go/no-go decisions. Covers go/no-go checklists, smoke test suite design, staged rollout validation, rollback criteria and procedures, and post-deployment verification. Ensures release confidence comes from data, not feelings. Use when: "release ready," "go/no-go," "smoke test," "release checklist," "rollback plan," "staged rollout," "canary deploy." Not for: safe-release techniques (flags, canary, dark launch) applied during the rollout itself — use testing-in-production; scheduled probes that run continuously after release — use synthetic-monitoring; designing new tests from prod telemetry — use observability-driven-testing. Related: testing-in-production, qa-metrics, ci-cd-integration, ai-system-testing.
An X API alternative and Twitter API alternative on fetcher.sh — pay-per-call in USDC via x402, or prepaid credits with a Bearer key, no OAuth and no developer application. Use when the user wants to search X posts by keyword, hashtag, or advanced operators (from:, to:, since:, until:, min_faves:, filter:), scrape an X/Twitter profile by handle, pull a user's posts, replies, followers, or followings, fetch a single post with its replies or reposters, read an X List's members or posts, check trending topics by country, or search for X accounts by name. Also covers building an X data pipeline, social listening, competitor monitoring, hashtag tracking, or follower export without the official X API's pricing tiers or app-review process.
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
Use when reporting progress in autonomous loop iterations. Triggers at the end of every autonomous loop iteration, when the autonomous-loop skill completes a BUILD phase, when progress reporting is needed for monitoring or exit evaluation, or when producing machine-parseable RALPH_STATUS blocks with exit signal protocol.
Use this skill when the user wants to turn a recurring browser workflow into a reusable, parameterized agent skill, especially when the task has concrete inputs and a clear output such as scheduled scrapes, form submissions, data extraction, monitoring flows, price probes, or login-gated reports. Do not use for one-off web tasks; use steel-browser.
AWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, OpenAI/Gemini to Bedrock), route to the migration-to-aws skill. Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.