性能
性能分析与优化
1k-perf-optimizer
OneKeyHQ
"Diagnose and optimize OneKey iOS startup and Wallet Home performance using timing metrics, call counts, profiling, and regression baselines."
cpu-profile
ClickHouse
Profile a ClickHouse query using the sampling query profiler and system.trace_log. Use when the user wants to find CPU hotspots, analyze where time is spent in a query, or investigate performance bottlenecks.
maintaining-python-tests
PostHog
Maintains existing pytest and Django test suites without weakening correctness. Use when asked to reduce Python test runtime or CI work, investigate slow pytest families, remove stale migration tests, consolidate repeated setup, improve Python test ownership, or measure whether a test optimization worked after merge. Ranks work by measured cost, applies the writing-tests value gate to existing coverage, preserves distinct behavior cases, validates isolation after shared-fixture changes, and separates testcase work from pytest-suite wall time. For an intermittent failure, use fixing-flaky-tests instead.
optimizing-clickhouse-and-hogql-queries
PostHog
Workflow for optimizing ClickHouse and HogQL queries. Use when a HogQL query, query runner, insight, or report is too slow; when a hand-written ClickHouse query (via sync_execute or in a migration) is too slow; when ClickHouse times out or hits memory limits; when investigating a slow system.query_log row; or when reviewing a proposed HogQL printer change for performance. Covers extracting the ClickHouse SQL, common smells (FROM ... FINAL, JSONExtract over properties, missing skip indexes, self-joins, CTE blow-up), measuring against a real cluster, and applying the fix at the right layer (printer, query runner, or migration). Does NOT cover Postgres / Django ORM / app-database queries; for those use profiling-slow-api-endpoints.
analyzing-experiment-query-performance
PostHog
Pull and interpret production experiment query-performance data from the staff-only /api/debug_ch_queries endpoints backing the /experiments/staff scene: slowest experiment queries, precompute read/build health, and preaggregation cache footprint. Covers prod-US and prod-EU via a query_performance:read personal API key, all query params, and response field semantics (exception codes, exposure paths, precompute skip reasons, job states). Use when investigating slow or failing experiment queries, precompute regressions, 307/159/241 errors, preaggregation table growth, or when asked how experiment query performance or the precompute rollout is doing in production.
django-startup-time
PostHog
Keep heavy imports off the django.setup() path that every process (web, celery, temporal, migrate, shell, CI) pays for. Use when touching AppConfig.ready(), wiring signal receivers, editing the lazy API router (posthog/api/rest_router.py or its init.py shim), deferring a heavy import or profiling import time (importtime, hothog), when the startup-import-budget guard fails, or when merging master into a long-lived branch that made the router lazy.
generating-clickhouse-query-performance-reports
PostHog
Produce and structure slow-query performance reports for PostHog's production ClickHouse (US and EU). Use when asked for a slow query report, query performance analysis over the last N days, per-team query cost, OOM or timeout investigation, cluster cost/memory regressions, or materialization candidates. Covers the modern query_log_archive source (typed lc_* columns, multi-day retention), how to categorize and attribute slow queries, root-cause patterns (unmaterialized JSONExtract, high-cardinality breakdowns, heavy joins), and the report structure. Runs queries via the querying-production-databases-via-metabase skill.
querying-local-postgres
PostHog
Run read-only SQL against the local Postgres app database (SELECT, EXPLAIN, EXPLAIN ANALYZE on SELECT). Default local URL postgres://posthog:posthog@localhost:5432/posthog; else DATABASE_URL. Use when querying the local DB, inspecting tables, debugging data, or analyzing query plans. Mutations are strictly forbidden.
1k-startup-profile
OneKeyHQ
"Deep startup profiling for OneKey mobile — per-module JS factory timings, HBC I/O/parse breakdown, per-segment timings. Gated behind the single env var ONEKEY_STARTUP_PROFILE=1. Zero overhead when disabled (default). Use when diagnosing cold-start regressions, sizing main.bundle parse cost, finding slow modules, measuring segment load, or auditing what a 1.7s require('./App') actually contains. Triggers on: startup profile, 启动性能分析, module timing, HBC parse time, segment timing, require cost, StartupProfile, ONEKEY_STARTUP_PROFILE, __d patcher, inline-requires."
deep-review
coder
Sub-agent powered code reviews spanning correctness, tests, consistency, and fit
openclaw-test-performance
openclaw
Benchmark, diagnose, and optimize OpenClaw test and plugin-suite runtime, import hotspots, CPU/RSS, heap growth, and slow coverage paths.
openclaw-performance
openclaw
"Performance work in OpenClaw: a slow or heavy flow (latency, CPU, memory, SQL, RPC, renders), writing or reviewing a perf PR, or adding a cache. Count the work, delete waste first, cache last."
fastify-best-practices
mcollina
"Guides development of Fastify Node.js backend servers and REST APIs using TypeScript or JavaScript. Use when building, configuring, or debugging a Fastify application — including defining routes, implementing plugins, setting up JSON Schema validation, handling errors, optimising performance, managing authentication, configuring CORS and security headers, integrating databases, working with WebSockets, and deploying to production. Covers the full Fastify request lifecycle (hooks, serialization, logging with Pino) and TypeScript integration via strip types. Trigger terms: Fastify, Node.js server, REST API, API routes, backend framework, fastify.config, server.ts, app.ts."
node
mcollina
Provides domain-specific best practices for Node.js development with TypeScript, covering type stripping, async patterns, error handling, streams, modules, testing, performance, caching, logging, and more. Use when setting up Node.js projects with native TypeScript support, configuring type stripping (--experimental-strip-types), writing Node 22+ TypeScript without a build step, or when the user mentions 'native TypeScript in Node', 'strip types', 'Node 22 TypeScript', '.ts files without compilation', 'ts-node alternative', or needs guidance on error handling, graceful shutdown, flaky tests, profiling, or environment configuration in Node.js. Helps configure tsconfig.json for type stripping, set up package.json scripts, handle module resolution and import extensions, and apply robust patterns across the full Node.js stack.
sandbox-bench
vercel
Benchmark React or Next.js changes on Vercel Sandbox VMs with paired A/B statistics: react PR/commit vs base, or Next.js PR/commit vs base, measured end-to-end through the bench/render-pipeline app (rps, latency, p95; TTFB, RSS and document/Flight bytes when the Next side captures them) and, for React changes, through the react repo's flight-ssr-bench fixture (Node AND Edge web-streams paths, Fizz and Flight+Fizz). Use whenever the user asks to bench, perf test, or A/B a React PR, a react-server-dom / Flight / vendored React change, or a Next.js PR ("is this PR faster", "does this regress RSC?", "measure the perf impact of <commit>"), even if they don't say "benchmark" — any request to quantify a server-side performance difference between two revisions belongs here. Runs remotely (laptop-free), applies correctness gates before measuring, and reports boot-level confidence intervals.
v8-jit
vercel
V8 JIT optimization patterns for writing high-performance JavaScript in Next.js server internals. Use when writing or reviewing hot-path code in app-render, stream-utils, routing, caching, or any per-request code path. Covers hidden classes / shapes, monomorphic call sites, inline caches, megamorphic deopt, closure allocation, array packing, and profiling with --trace-opt / --trace-deopt.
next-cache-components-optimizer
vercel
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then guards against regression. Use when asked to make a route's navigation instant (its static shell commits immediately), fix a route whose static shell isn't prerendered/served/prefetched, grow a route's static shell or fix its slow first paint, diagnose which Suspense boundary keeps a route out of its static shell, or write the instant() e2e guard for one. Requires Next.js 16.3+ with cacheComponents; directs an upgrade if older.
healthcheck
elizaOS
Host security hardening and risk-tolerance configuration for Otto deployments. Use when a user asks for security audits, firewall/SSH/update hardening, risk posture, exposure review, Otto cron scheduling for periodic checks, or version status checks on a machine running Otto (laptop, workstation, Pi, VPS).
optimize-bundle-size
elastic
Reduce plugin page load bundle size and avoid unnecessary increases in packages/kbn-optimizer/limits.yml. Use when proactively optimizing bundles, investigating CI page-load overages, or reviewing PRs that change bundle limits.
depth-estimation
SharpAI
"Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)"
gradle-expert
vitorpamplona
Build optimization, dependency resolution, and multi-module KMP troubleshooting for AmethystMultiplatform. Use when working with: (1) Gradle build files (build.gradle.kts, settings.gradle), (2) Version catalog (libs.versions.toml), (3) Build errors and dependency conflicts, (4) Module dependencies and source sets, (5) Desktop packaging (DMG/MSI/DEB), (6) Build performance optimization, (7) Proguard/R8 configuration, (8) Common KMP + Android Gradle issues (Compose conflicts, secp256k1 JNI variants, source set problems).
py
crazyguitar
Comprehensive Python programming reference covering syntax, concurrency, networking, databases, ML/LLM development, and HPC. Use for: Python questions, Python interview preparation, debugging, performance optimization, async patterns, library examples, code review, best practices, MLOps workflows, distributed computing, security implementations, and any Python development tasks.
react-component-performance
Dimillian
Analyze and optimize React component performance issues (slow renders, re-render thrash, laggy lists, expensive computations). Use when asked to profile or improve a React component, reduce re-renders, or speed up UI updates in React apps.
swiftui-performance-audit
Dimillian
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.