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Found 3,436 Skills
Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.
Redis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops/sec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT LIST, FT.PROFILE), and when to use the Redis Insight GUI. Use when setting up monitoring or alerts for a Redis instance, diagnosing a performance regression, profiling a slow FT.SEARCH query, or wiring Redis metrics into Prometheus, Datadog, or similar.
Vendor-neutral skill to extract decisions and action items from a meeting transcript and produce a decision log plus an action register.
Vendor-neutral skill to audit application logs for potential sensitive-data leakage and redaction coverage.
Use when planning A/B tests in LaunchDarkly, Optimizely, or similar platforms. Sizes the experiment (sample size, MDE, runtime), drafts hypothesis + success metrics + guardrails, and produces a launch checklist + rollback plan.
Manage Oodle notifiers, notification policies, and muting rules — routing alerts to the right channels and silencing during maintenance.
Elastic ML anomaly detection skill — investigation/RCA, score explanation, job operations (create, datafeed, start/stop, results), and troubleshooting (missing docs, memory limits, datafeed health, lifecycle). Operates against Kibana Agent Builder MCP tools (`ad_*`) on `.ml-anomalies-*`, `.ml-config`, `.ml-notifications-*`, `.ml-annotations-*`. Use when answering "what broke?"/"which entity?"/RCA, "why is score high/low?"/renormalization, "datafeed stopped"/"memory limit", or any request to set up or configure an ML anomaly detection job.
Audit Trail investigations - who changed what, key compromise, cost spike root cause, compliance evidence (SOC 2/PCI), and AI activity auditing.
A-share multi-agent investment research framework with 7 AI analysts, bull/bear debate, and risk assessment adapted for Chinese stock market
Build structured hierarchical memory systems for LLM agents using GAM (General Agentic Memory) with support for text, video, and agent trajectories
Master the Infinite Agentic Loop pattern with Claude Code for parallel AI agent orchestration and iterative content generation
Deploy and configure Mercury Agent, a soul-driven AI agent with permission-hardened tools, token budgets, and multi-channel access