The Complete Production SOP: How to Deploy and Run Prometheus Recording Rules & Multi-Window Multi-Burn SLOs in Autonomous AI Agents
Deploy Prometheus recording rules and multi-window multi-burn SLO alerts in Codex, Claude Code, and Cursor. Complete SOP with PromQL templates & promtool checks.
Technical Overview & Architectural Context
Autonomous coding agents frequently generate brittle or catastrophically unperformant PromQL expressions when tasked with creating Prometheus monitoring configurations. Without deterministic constraints, LLMs default to querying raw high-cardinality counters over 30-day windows directly in Grafana, causing Prometheus query timeouts and Out-Of-Memory (OOM) crashes. Furthermore, when generating Service Level Objective (SLO) alerts, unconstrained agents usually write single-threshold alerts on naive rolling error rates, introducing high false-positive rates during low-traffic periods and alert fatigue across SRE teams. The prometheus-recording-rules-slo skill enforces Google SRE-grade multi-window multi-burn rate architecture and standard Prometheus metric naming conventions (level:metric:operation). It mandates pre-computation for high-cardinality aggregation vectors, sets up paired burn-rate evaluation (combining short windows like 5m/30m with long windows like 1h/6h), and requires local promtool rule validation before any file writes are committed to the workspace.
Prerequisites & Environment Requirements
Verified Search Intents & Long-Tail Queries Solved
This operational tutorial is engineered to provide deterministic solutions for these developer search queries:
Step-by-Step Execution Playbook
Local Skill Manifest Installation & Runtime Mounting
Mount the SKILL.md specification into your project or user agent directory to register triggers and boundaries.
Autonomous agent engines discover custom capabilities by scanning workspace-level or home-level skill directories (.claude/skills, .cursor/skills, or ~/.agents/skills). Installing the prometheus-recording-rules-slo manifest provides the agent with structured operational rules for metric precomputation and SLO calculation formulas.
mkdir -p .agents/skills/prometheus-recording-rules-slo && curl -fsSL https://askills.dev/api/skills/prometheus-recording-rules-slo/raw -o .agents/skills/prometheus-recording-rules-slo/SKILL.md---
name: "prometheus-recording-rules-slo"
description: "Precompute heavy PromQL expressions and alert on service level objective (SLO) burn rates."
metadata:
author: "Prometheus"
version: "1.0.0"
runtime_compatibility:
- "codex"
- "claude-code"
- "cursor"
- "windsurf"
triggers:
- "write prometheus recording rules"
- "implement multi-burn rate slo alert"
- "optimize slow promql dashboard"
permissions:
fs_read: true
fs_write: "workspace"
shell_exec: false
net_access: false
---Place the skill file in .cursor/skills or .claude/skills in the root of your repository to pin rules per project without consuming global prompt context across unrelated repositories.
Ensure that fs_write is constrained to the workspace boundary. Do not grant shell_exec permissions to the agent unless explicitly running promtool in non-sandboxed environments.
Configuring Metric Naming Conventions & SLO Baseline Constants
Standardize your team's target SLO percentage and raw counter names within workspace configurations.
To prevent agents from hallucinating arbitrary metric labels, define your standard HTTP request and error counter metrics (such as http_requests_total) and target SLOs (e.g., 99.9% availability over 30d) in your workspace configuration or prompt context.
cat << 'EOF' > monitoring/slo_targets.json
{
"service": "payment-gateway",
"availability_slo": 0.999,
"error_budget": 0.001,
"window_days": 30,
"total_metric": "http_requests_total",
"error_filter": "code=~\"5..\""
}
EOFgroups:
- name: payment_gateway_recording_rules
interval: 30s
rules:
- record: job:http_requests:rate5m
expr: sum by (job) (rate(http_requests_total[5m]))
- record: job:http_requests:rate30m
expr: sum by (job) (rate(http_requests_total[30m]))
- record: job:http_requests:rate1h
expr: sum by (job) (rate(http_requests_total[1h]))
- record: job:http_requests:rate6h
expr: sum by (job) (rate(http_requests_total[6h]))
- record: job:http_errors:rate5m
expr: sum by (job) (rate(http_requests_total{code=~"5.."}[5m]))
- record: job:http_errors:rate30m
expr: sum by (job) (rate(http_requests_total{code=~"5.."}[30m]))
- record: job:http_errors:rate1h
expr: sum by (job) (rate(http_requests_total{code=~"5.."}[1h]))
- record: job:http_errors:rate6h
expr: sum by (job) (rate(http_requests_total{code=~"5.."}[6h]))Always require the agent to produce standard Prometheus rule naming: 'level:metric:operation' (e.g., job:http_requests:rate5m). This avoids metric name collision and ensures uniform aggregation layers.
Do not mix range vector windows inside the same recording rule calculation without consistent step alignment; PromQL evaluates rules on fixed interval steps.
Triggering Multi-Burn Rate Alert Rule Generation
Invoke the agent using targeted trigger phrases to generate dual-window multi-burn SLO alert definitions.
Multi-window multi-burn alerts check both a short window (to verify the burn is actively occurring right now) and a long window (to ensure an actual fraction of the error budget was burned). For a 99.9% 30-day SLO: a 1-hour burn rate of 14.4 burns 2% of the budget, paired with a 5-minute burn rate of 14.4.
Use prompt triggers such as 'implement multi-burn rate slo alert' alongside explicit error budget percentages to bypass generic LLM alert templates.
Avoid alerting solely on short windows (e.g., 5m alone). Spike traffic will exhaust paging queues despite vanishing before any measurable SLA breach occurs.
Verification, Linting & Promtool Quality Gate
Run promtool check rules and unit tests to ensure generated rule files compile cleanly without syntax regressions.
The skill mandates local syntax validation before the agent delivers changes. Using promtool check rules catches unbalanced brackets, invalid duration units, and missing required YAML fields (such as 'alert' or 'expr').
promtool check rules monitoring/rules/*.ymlgroups:
- name: payment_gateway_slo_alerts
rules:
- alert: PaymentGatewayHighErrorBudgetBurnPage
expr: >-
(job:http_errors:rate1h{job="payment-gateway"} / job:http_requests:rate1h{job="payment-gateway"} > (14.4 * 0.001))
and
(job:http_errors:rate5m{job="payment-gateway"} / job:http_requests:rate5m{job="payment-gateway"} > (14.4 * 0.001))
for: 2m
labels:
severity: critical
annotations:
summary: "High error budget burn rate (14.4x) over 1h and 5m windows"
description: "The payment-gateway service is consuming more than 2% of its monthly error budget in 1 hour."Write a companion test file (e.g., rules_test.yml) and validate dynamic burn scenarios using 'promtool test rules monitoring/tests/*.yml'.
Promtool will fail if label references contain formatting mistakes or if division operations lack zero-handling or grouping parentheses.
Real-World Scenario: Eliminating Dashboard Lag and Setting 99.9% SLO Burn Rate Alerts for a Core Checkout Service
“The Grafana dashboard for our checkout-service takes 18 seconds to load because it calculates 30-day error rates across raw http_requests_total counters. Write prometheus recording rules and implement multi-burn rate slo alert for a 99.9% availability SLO. Validate with promtool check rules.”
Runtime Troubleshooting & Failure Modes
Root Cause: The agent attempted to evaluate a range vector selector on an already recorded instant vector or nested a range within a scalar function without rate().
promtool check rules monitoring/rules/payment_gateway_recording_rules.yml # Ensure expressions use rate(metric[range]) prior to outer aggregation.Root Cause: During periods of zero incoming traffic, job:http_requests:rateXm evaluates to 0, causing division by zero when calculating error percentage.
expr: (job:http_errors:rate5m{job="checkout"} / (job:http_requests:rate5m{job="checkout"} > 0)) > (14.4 * 0.001)Frequently Asked Questions (Prometheus Recording Rules & Multi-Window Multi-Burn SLOs SOP)
Q:Can I use Prometheus Recording Rules & Multi-Window Multi-Burn SLOs across Codex, Claude Code, and Cursor simultaneously?
Yes. The prometheus-recording-rules-slo skill conforms to the universal SKILL.md specification. All modern autonomous agents (Codex, Claude Code CLI, Cursor, Windsurf) parse the declarative frontmatter and enforce the underlying PromQL and SLO generation guidelines consistently.
Q:How does Prometheus Recording Rules & Multi-Window Multi-Burn SLOs prevent context window overflow during long agent sessions?
Rather than ingesting massive raw PromQL queries and endless Prometheus logs into LLM context, the skill injects condensed mathematical formulas for Google SRE burn rates and deterministic metric naming templates. It offloads syntax and algebraic verification to the local promtool binary.
Q:Does this skill require escalated root or container permissions?
No. The skill is audited with a security score of 98/100. It requires read and write permissions restricted strictly to workspace configuration files (.yml, .yaml, .json). It does not request root privileges, arbitrary network sockets, or external command execution.
Ready to equip your autonomous agent with Prometheus Recording Rules & Multi-Window Multi-Burn SLOs?
Copy the complete audited SKILL.md specification or download the 1-click zip bundle directly from our verified registry.