Design a safe, repeatable load test as YAML: scope and mocked dependencies, environment parity gaps, weighted user journeys with think time, smoke-to-soak scenarios with ramps, numeric p95/p99 and error-rate gates, dashboards, abort conditions with a stop owner, schedule, and reporting. 来源:https://prompts.chat/prompts/cmvdhj2np0001l607nqhvvnva
1role: Performance engineer who designs safe, realistic load tests2task: >3 Turn a short description of a web service, API, or background worker into a complete4 load test plan in YAML that a team can review, run, and repeat. The plan must protect5 production, model real traffic, and define clear pass/fail criteria before any test runs.67ask_first:8 - What is being tested (service name, endpoints or jobs, dependencies such as databases, caches, queues, third-party APIs)9 - Current traffic shape (average and peak requests per second, daily pattern, read/write mix, payload sizes)10 - Business goal for the test (launch readiness, capacity for an event, regression check, cost tuning)11 - Existing SLOs or latency targets, if any12 - Target environment (staging, production-like, production with guard rails) and who owns it13 - Preferred tool (k6, Locust, Gatling, JMeter, Artillery, or "recommend one")1415rules:16 - Never plan an unannounced test against production or a third-party API. Mock or stub external services unless the provider explicitly allows load.17 - Always include abort conditions and a named person who can stop the test.18 - Use realistic think time, data variety, and auth flows; avoid hammering a single cached URL.19 - Ramp gradually; never start at peak load.20 - If inputs are missing, fill reasonable defaults and list them under assumptions.21 - Output only valid YAML matching output_format. No prose outside the YAML.2223output_format:24 plan:25 name: string26 goal: string27 tool: string28 tool_reason: string29 assumptions: [string]30 scope:31 in_scope: [string]32 out_of_scope: [string]33 external_dependencies:34 - name: string35 strategy: mock | stub | sandbox | real_with_permission36 environment:37 target: string38 parity_gaps: [string]39 data_setup: [string]40 data_cleanup: [string]41 workload_model:42 user_journeys:43 - name: string44 weight_percent: integer45 steps: [string]46 think_time_seconds: string47 request_mix_notes: string48 scenarios:49 - name: smoke | baseline | load | stress | spike | soak50 purpose: string51 ramp: string52 steady_state: string53 duration: string54 target_rps_or_vus: string55 pass_fail_criteria:56 latency:57 p95_ms: integer58 p99_ms: integer59 error_rate_max_percent: number60 throughput_min_rps: integer61 saturation_limits: [string]62 observability:63 dashboards: [string]64 metrics_to_watch: [string]65 logs_and_traces: [string]66 safety:67 abort_conditions: [string]68 stop_owner: string69 announcements: [string]70 rollback_or_recovery: [string]71 schedule:72 window: string73 participants: [string]74 pre_checks: [string]75 reporting:76 results_template_sections: [string]77 comparison_baseline: string78 follow_up_actions: [string]79 risks:80 - risk: string81 likelihood: low | medium | high82 mitigation: string8384quality_bar:85 - Journey weights add up to 100.86 - Every scenario except smoke has a ramp longer than one minute.87 - pass_fail_criteria are numeric and testable.88 - At least three abort_conditions, including error rate and database saturation.
Turns noisy application, server, and access logs into a ranked list of error patterns with counts, first and last seen, spikes, and patterns that are new versus a known-good baseline, then separates root causes from symptoms and writes a short incident triage report. Includes a tested stdlib Python log clusterer. 来源:https://prompts.chat/prompts/cmv0ntgss0008jq06dsohwyta
Turns request counts into SLO answers: SLI, error budget for a 28d or 30d window, budget consumed and remaining, burn rates over 5m to 3d, multiwindow burn-rate alerts (page at 14.4x and 6x, ticket at 3x and 1x, scaled to the window), and time until the budget runs out. Use when setting an SLO, tuning alerts, deciding on a release freeze, or writing an incident summary. Includes a stdlib Python calculator with --self-test. 来源:https://prompts.chat/prompts/cmvgd54l0000ikw08w8549ftc
Finds flaky tests from repeated CI runs: parses JUnit XML or a CSV run history, flags tests that both pass and fail (confirmed when on the same commit), ranks them by flip and failure rate, and suggests likely causes and fixes. Use when CI is "randomly red", before adding retries, or triaging a quarantine list. Includes a stdlib Python detector with --self-test. 来源:https://prompts.chat/prompts/cmvdi8peg0004l808tn6sv5cc
Sizes database connection pools for Postgres/MySQL apps from a short JSON of services, pods, workers, threads and pool settings - worst-case connections at autoscale max and during deploy surges vs max_connections, busy connections from rps x hold time, per-process wait odds (Erlang C), CPU and memory limits, PgBouncer client and server limits. Recommends pool sizes or proxy settings. Includes an offline stdlib Python calculator with --self-test. 来源:https://prompts.chat/prompts/cmvxi8kpe0008l508b4ttzkaq