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Found 700 Skills
Generate, write, or run an ad-hoc query against SigNoz observability data — metrics, logs, traces, or exceptions — without wrapping it in a dashboard panel or alert. Make sure to use this skill whenever the user asks "show me error rates", "query logs for timeout errors", "what's the p99 latency for the cart service", "how many requests hit the payment endpoint", "find slow traces", "errors in the last hour", or otherwise asks an exploratory question that needs live observability data — even if they don't say "query" or "search" explicitly.
Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span attributes — not LLM analytics traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.
Use this skill when the user asks about Goldsky Edge — the managed RPC endpoint service for EVM chains. Triggers on: 'Edge RPC', 'Goldsky RPC endpoint', 'edge.goldsky.com', 'eth_getLogs is slow', 'RPC rate limit', 'hedged requests', 'flashblocks', 'HyperEVM system transactions', 'x402 pay-per-request RPC', 'Goldsky Edge pricing', 'Edge dashboard', 'gs_edge_ API key', 'rpc-edge'. Also use this skill when the user wants a resilient, low-latency JSON-RPC endpoint for EVM chains (Ethereum, Base, Arbitrum, Optimism, Polygon zkEVM, BSC, Avalanche, Berachain, HyperEVM, Monad, Sei, Sonic, Unichain, zkSync, etc.), is debugging RPC errors like -32005/-32012/-32014/-32015/-32016, or is comparing providers (Alchemy, Infura, QuickNode, Ankr) against Edge. For questions about self-hosting eRPC or custom eRPC configuration beyond what Edge exposes, point them at https://docs.erpc.cloud/llms.txt. Do NOT trigger on Goldsky Mirror, Turbo, or Subgraph pipeline questions — those belong to their respective skills.
Audit and harden a repository test suite so tests carry their weight. Use when the user asks whether tests are useful, flaky, duplicated, slow, under-covering critical behavior, missing contract/regression coverage, or when a codebase needs a test strategy before major refactors, releases, security work, or production hardening.
SQL query optimization for PostgreSQL/MySQL with indexing, EXPLAIN analysis. Use for slow queries, N+1 problems, missing indexes, or encountering sequential scans, OFFSET pagination, temp table spills, inefficient JOINs.
Optimize Harness CI/CD pipeline performance via MCP. Configure parallel test execution with Test Intelligence, design multi-layer caching strategies, analyze pipeline bottlenecks with stage-level timing breakdowns, optimize cache hit rates, and design monorepo CI pipelines with selective builds. Use when asked to speed up pipelines, improve cache hit rates, set up parallel testing, optimize build times, or configure monorepo builds. Do NOT use for creating new pipelines (use create-pipeline instead) or debugging failures (use debug-pipeline instead). Trigger phrases: pipeline speed, slow pipeline, cache hit rate, parallel tests, test intelligence, build optimization, caching strategy, monorepo pipeline, pipeline bottleneck, build speed.
Diagnose Harness pipeline executions via MCP. Analyzes any execution (failed or successful) to produce structured reports with stage/step breakdown, timing, bottlenecks, failure details, chained pipeline drill-down, and execution logs. Use when asked to debug a pipeline, investigate a failure, find out why a build failed, analyze pipeline errors, check execution logs, review execution performance, or find bottlenecks. Trigger phrases: debug pipeline, pipeline failed, why did my build fail, analyze failure, pipeline error, execution logs, fix pipeline, execution bottleneck, slow pipeline.
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
Invoke the `groundcover` Go CLI to manage Groundcover resources (dashboards, monitors, silences, connected apps, notification routes, API keys, policies, integrations, pipelines, workflows) AND to answer production observability questions by querying logs, traces, metrics, k8s inventory, and k8s events. Use whenever a task needs an authenticated call against the Groundcover API or whenever the user is debugging a prod issue and asks things like "why is X erroring in prod", "show me logs for service Y", "what's the p99 latency on Z", "what pods are crashlooping", "search traces for slow requests", "any k8s events for namespace N", "is service S receiving traffic", "list groundcover monitors", "create a silence", "update notification route", "hit a groundcover endpoint". Covers required env vars, the SDK-backed vs raw command split, and concrete request-body templates for logs/traces/metrics/k8s so the CLI can be driven from anywhere.
Sets up AWS Resilience Hub v2 from scratch: creates resilience policies with SLO targets, registers systems and user journeys, onboards services with input sources, and runs a first failure mode assessment. Applies when the user wants to get started with Resilience Hub v2, create a policy, onboard a service, or run an assessment — including creating one concrete policy with specific availability/RTO/RPO targets and a DR approach for a single service (even a tier-1 one). Does not apply to FIS experiments or ARC routing controls.
Quickly deslop UI code by fixing spacing, hierarchy, typography, and small layout issues. Use when the interface needs a fast cleanup or polish pass.
Identify which field values correlate with bad behavior (slowness, errors, anomalies, unusual values) using phi-coefficient correlation analysis over OPAL. Works on any time-series data — metrics, structured logs, span/trace data, or any dataset where rows can be split into a 'bad' and 'good' cohort by a threshold. Use when: (1) User asks for root-cause analysis on a dataset or metric (2) User wants to know what attributes / dimensions / values are most associated with a failure mode, anomaly, or unusual cohort (3) Investigating which services, hosts, regions, namespaces, or attributes drive outliers (4) User mentions phi coefficient, correlation, or outlier detection (5) User asks 'why is X slow/failing', 'what caused the errors on X', or 'what's different about the bad cohort'.