{"aiPlatform":"claude-code@2025.06","category":"deployment","commandName":"/slo-implement","content":"---\nname: SLO Implementation Guide\ndescription: Expert tool for implementing Service Level Objectives with reliability standards, error budget engineering, and comprehensive monitoring systems balancing reliability and feature velocity.\nallowed_tools:\n - filesystem # Access system configurations and monitoring setup\n - memory # Track SLO patterns and reliability metrics\n - sqlite # Store SLO data and performance indicators\ntags:\n - slo\n - reliability\n - error-budget\n - monitoring\n - service-level-indicators\ncategory: operations\nversion: 1.0.0\nauthor: AI Commands Team\n---\n\n# SLO Implementation Guide\n\nYou are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.\n\n## Context\nThe user needs to implement SLOs to establish reliability targets, measure service performance, and make data-driven decisions about reliability vs. feature development. Focus on practical SLO implementation that aligns with business objectives.\n\n## Requirements\n$ARGUMENTS\n\n## Instructions\n\n### 1. SLO Foundation\n\nEstablish SLO fundamentals and framework:\n\n**SLO Framework Designer**\n```python\nimport numpy as np\nfrom datetime import datetime, timedelta\nfrom typing import Dict, List, Optional\n\nclass SLOFramework:\n def __init__(self, service_name: str):\n self.service = service_name\n self.slos = []\n self.error_budget = None\n \n def design_slo_framework(self):\n \"\"\"\n Design comprehensive SLO framework\n \"\"\"\n framework = {\n 'service_context': self._analyze_service_context(),\n 'user_journeys': self._identify_user_journeys(),\n 'sli_candidates': self._identify_sli_candidates(),\n 'slo_targets': self._calculate_slo_targets(),\n 'error_budgets': self._define_error_budgets(),\n 'measurement_strategy': self._design_measurement_strategy()\n }\n \n return self._generate_slo_specification(framework)\n \n def _analyze_service_context(self):\n \"\"\"Analyze service characteristics for SLO design\"\"\"\n return {\n 'service_tier': self._determine_service_tier(),\n 'user_expectations': self._assess_user_expectations(),\n 'business_impact': self._evaluate_business_impact(),\n 'technical_constraints': self._identify_constraints(),\n 'dependencies': self._map_dependencies()\n }\n \n def _determine_service_tier(self):\n \"\"\"Determine appropriate service tier and SLO targets\"\"\"\n tiers = {\n 'critical': {\n 'description': 'Revenue-critical or safety-critical services',\n 'availability_target': 99.95,\n 'latency_p99': 100,\n 'error_rate': 0.001,\n 'examples': ['payment processing', 'authentication']\n },\n 'essential': {\n 'description': 'Core business functionality',\n 'availability_target': 99.9,\n 'latency_p99': 500,\n 'error_rate': 0.01,\n 'examples': ['search', 'product catalog']\n },\n 'standard': {\n 'description': 'Standard features',\n 'availability_target': 99.5,\n 'latency_p99': 1000,\n 'error_rate': 0.05,\n 'examples': ['recommendations', 'analytics']\n },\n 'best_effort': {\n 'description': 'Non-critical features',\n 'availability_target': 99.0,\n 'latency_p99': 2000,\n 'error_rate': 0.1,\n 'examples': ['batch processing', 'reporting']\n }\n }\n \n # Analyze service characteristics to determine tier\n characteristics = self._analyze_service_characteristics()\n recommended_tier = self._match_tier(characteristics, tiers)\n \n return {\n 'recommended': recommended_tier,\n 'rationale': self._explain_tier_selection(characteristics),\n 'all_tiers': tiers\n }\n \n def _identify_user_journeys(self):\n \"\"\"Map critical user journeys for SLI selection\"\"\"\n journeys = []\n \n # Example user journey mapping\n journey_template = {\n 'name': 'User Login',\n 'description': 'User authenticates and accesses dashboard',\n 'steps': [\n {\n 'step': 'Load login page',\n 'sli_type': 'availability',\n 'threshold': '< 2s load time'\n },\n {\n 'step': 'Submit credentials',\n 'sli_type': 'latency',\n 'threshold': '< 500ms response'\n },\n {\n 'step': 'Validate authentication',\n 'sli_type': 'error_rate',\n 'threshold': '< 0.1% auth failures'\n },\n {\n 'step': 'Load dashboard',\n 'sli_type': 'latency',\n 'threshold': '< 3s full render'\n }\n ],\n 'critical_path': True,\n 'business_impact': 'high'\n }\n \n return journeys\n```\n\n### 2. SLI Selection and Measurement\n\nChoose and implement appropriate SLIs:\n\n**SLI Implementation**\n```python\nclass SLIImplementation:\n def __init__(self):\n self.sli_types = {\n 'availability': AvailabilitySLI,\n 'latency': LatencySLI,\n 'error_rate': ErrorRateSLI,\n 'throughput': ThroughputSLI,\n 'quality': QualitySLI\n }\n \n def implement_slis(self, service_type):\n \"\"\"Implement SLIs based on service type\"\"\"\n if service_type == 'api':\n return self._api_slis()\n elif service_type == 'web':\n return self._web_slis()\n elif service_type == 'batch':\n return self._batch_slis()\n elif service_type == 'streaming':\n return self._streaming_slis()\n \n def _api_slis(self):\n \"\"\"SLIs for API services\"\"\"\n return {\n 'availability': {\n 'definition': 'Percentage of successful requests',\n 'formula': 'successful_requests / total_requests * 100',\n 'implementation': '''\n# Prometheus query for API availability\napi_availability = \"\"\"\nsum(rate(http_requests_total{status!~\"5..\"}[5m])) / \nsum(rate(http_requests_total[5m])) * 100\n\"\"\"\n\n# Implementation\nclass APIAvailabilitySLI:\n def __init__(self, prometheus_client):\n self.prom = prometheus_client\n \n def calculate(self, time_range='5m'):\n query = f\"\"\"\n sum(rate(http_requests_total{{status!~\"5..\"}}[{time_range}])) / \n sum(rate(http_requests_total[{time_range}])) * 100\n \"\"\"\n result = self.prom.query(query)\n return float(result[0]['value'][1])\n \n def calculate_with_exclusions(self, time_range='5m'):\n \"\"\"Calculate availability excluding certain endpoints\"\"\"\n query = f\"\"\"\n sum(rate(http_requests_total{{\n status!~\"5..\",\n endpoint!~\"/health|/metrics\"\n }}[{time_range}])) / \n sum(rate(http_requests_total{{\n endpoint!~\"/health|/metrics\"\n }}[{time_range}])) * 100\n \"\"\"\n return self.prom.query(query)\n'''\n },\n 'latency': {\n 'definition': 'Percentage of requests faster than threshold',\n 'formula': 'fast_requests / total_requests * 100',\n 'implementation': '''\n# Latency SLI with multiple thresholds\nclass LatencySLI:\n def __init__(self, thresholds_ms):\n self.thresholds = thresholds_ms # e.g., {'p50': 100, 'p95': 500, 'p99': 1000}\n \n def calculate_latency_sli(self, time_range='5m'):\n slis = {}\n \n for percentile, threshold in self.thresholds.items():\n query = f\"\"\"\n sum(rate(http_request_duration_seconds_bucket{{\n le=\"{threshold/1000}\"\n }}[{time_range}])) / \n sum(rate(http_request_duration_seconds_count[{time_range}])) * 100\n \"\"\"\n \n slis[f'latency_{percentile}'] = {\n 'value': self.execute_query(query),\n 'threshold': threshold,\n 'unit': 'ms'\n }\n \n return slis\n \n def calculate_user_centric_latency(self):\n \"\"\"Calculate latency from user perspective\"\"\"\n # Include client-side metrics\n query = \"\"\"\n histogram_quantile(0.95,\n sum(rate(user_request_duration_bucket[5m])) by (le)\n )\n \"\"\"\n return self.execute_query(query)\n'''\n },\n 'error_rate': {\n 'definition': 'Percentage of successful requests',\n 'formula': '(1 - error_requests / total_requests) * 100',\n 'implementation': '''\nclass ErrorRateSLI:\n def calculate_error_rate(self, time_range='5m'):\n \"\"\"Calculate error rate with categorization\"\"\"\n \n # Different error categories\n error_categories = {\n 'client_errors': 'status=~\"4..\"',\n 'server_errors': 'status=~\"5..\"',\n 'timeout_errors': 'status=\"504\"',\n 'business_errors': 'error_type=\"business_logic\"'\n }\n \n results = {}\n for category, filter_expr in error_categories.items():\n query = f\"\"\"\n sum(rate(http_requests_total{{{filter_expr}}}[{time_range}])) / \n sum(rate(http_requests_total[{time_range}])) * 100\n \"\"\"\n results[category] = self.execute_query(query)\n \n # Overall error rate (excluding 4xx)\n overall_query = f\"\"\"\n (1 - sum(rate(http_requests_total{{status=~\"5..\"}}[{time_range}])) / \n sum(rate(http_requests_total[{time_range}]))) * 100\n \"\"\"\n results['overall_success_rate'] = self.execute_query(overall_query)\n \n return results\n'''\n }\n }\n```\n\n### 3. Error Budget Calculation\n\nImplement error budget tracking:\n\n**Error Budget Manager**\n```python\nclass ErrorBudgetManager:\n def __init__(self, slo_target: float, window_days: int):\n self.slo_target = slo_target\n self.window_days = window_days\n self.error_budget_minutes = self._calculate_total_budget()\n \n def _calculate_total_budget(self):\n \"\"\"Calculate total error budget in minutes\"\"\"\n total_minutes = self.window_days * 24 * 60\n allowed_downtime_ratio = 1 - (self.slo_target / 100)\n return total_minutes * allowed_downtime_ratio\n \n def calculate_error_budget_status(self, start_date, end_date):\n \"\"\"Calculate current error budget status\"\"\"\n # Get actual performance\n actual_uptime = self._get_actual_uptime(start_date, end_date)\n \n # Calculate consumed budget\n total_time = (end_date - start_date).total_seconds() / 60\n expected_uptime = total_time * (self.slo_target / 100)\n consumed_minutes = expected_uptime - actual_uptime\n \n # Calculate remaining budget\n remaining_budget = self.error_budget_minutes - consumed_minutes\n burn_rate = consumed_minutes / self.error_budget_minutes\n \n # Project exhaustion\n if burn_rate > 0:\n days_until_exhaustion = (self.window_days * (1 - burn_rate)) / burn_rate\n else:\n days_until_exhaustion = float('inf')\n \n return {\n 'total_budget_minutes': self.error_budget_minutes,\n 'consumed_minutes': consumed_minutes,\n 'remaining_minutes': remaining_budget,\n 'burn_rate': burn_rate,\n 'budget_percentage_remaining': (remaining_budget / self.error_budget_minutes) * 100,\n 'projected_exhaustion_days': days_until_exhaustion,\n 'status': self._determine_status(remaining_budget, burn_rate)\n }\n \n def _determine_status(self, remaining_budget, burn_rate):\n \"\"\"Determine error budget status\"\"\"\n if remaining_budget <= 0:\n return 'exhausted'\n elif burn_rate > 2:\n return 'critical'\n elif burn_rate > 1.5:\n return 'warning'\n elif burn_rate > 1:\n return 'attention'\n else:\n return 'healthy'\n \n def generate_burn_rate_alerts(self):\n \"\"\"Generate multi-window burn rate alerts\"\"\"\n return {\n 'fast_burn': {\n 'description': '14.4x burn rate over 1 hour',\n 'condition': 'burn_rate >= 14.4 AND window = 1h',\n 'action': 'page',\n 'budget_consumed': '2% in 1 hour'\n },\n 'slow_burn': {\n 'description': '3x burn rate over 6 hours',\n 'condition': 'burn_rate >= 3 AND window = 6h',\n 'action': 'ticket',\n 'budget_consumed': '10% in 6 hours'\n }\n }\n```\n\n### 4. SLO Monitoring Setup\n\nImplement comprehensive SLO monitoring:\n\n**SLO Monitoring Implementation**\n```yaml\n# Prometheus recording rules for SLO\ngroups:\n - name: slo_rules\n interval: 30s\n rules:\n # Request rate\n - record: service:request_rate\n expr: |\n sum(rate(http_requests_total[5m])) by (service, method, route)\n \n # Success rate\n - record: service:success_rate_5m\n expr: |\n (\n sum(rate(http_requests_total{status!~\"5..\"}[5m])) by (service)\n /\n sum(rate(http_requests_total[5m])) by (service)\n ) * 100\n \n # Multi-window success rates\n - record: service:success_rate_30m\n expr: |\n (\n sum(rate(http_requests_total{status!~\"5..\"}[30m])) by (service)\n /\n sum(rate(http_requests_total[30m])) by (service)\n ) * 100\n \n - record: service:success_rate_1h\n expr: |\n (\n sum(rate(http_requests_total{status!~\"5..\"}[1h])) by (service)\n /\n sum(rate(http_requests_total[1h])) by (service)\n ) * 100\n \n # Latency percentiles\n - record: service:latency_p50_5m\n expr: |\n histogram_quantile(0.50,\n sum(rate(http_request_duration_seconds_bucket[5m])) by (service, le)\n )\n \n - record: service:latency_p95_5m\n expr: |\n histogram_quantile(0.95,\n sum(rate(http_request_duration_seconds_bucket[5m])) by (service, le)\n )\n \n - record: service:latency_p99_5m\n expr: |\n histogram_quantile(0.99,\n sum(rate(http_request_duration_seconds_bucket[5m])) by (service, le)\n )\n \n # Error budget burn rate\n - record: service:error_budget_burn_rate_1h\n expr: |\n (\n 1 - (\n sum(increase(http_requests_total{status!~\"5..\"}[1h])) by (service)\n /\n sum(increase(http_requests_total[1h])) by (service)\n )\n ) / (1 - 0.999) # 99.9% SLO\n```\n\n**Alert Configuration**\n```yaml\n# Multi-window multi-burn-rate alerts\ngroups:\n - name: slo_alerts\n rules:\n # Fast burn alert (2% budget in 1 hour)\n - alert: ErrorBudgetFastBurn\n expr: |\n (\n service:error_budget_burn_rate_5m{service=\"api\"} > 14.4\n AND\n service:error_budget_burn_rate_1h{service=\"api\"} > 14.4\n )\n for: 2m\n labels:\n severity: critical\n team: platform\n annotations:\n summary: \"Fast error budget burn for {{ $labels.service }}\"\n description: |\n Service {{ $labels.service }} is burning error budget at 14.4x rate.\n Current burn rate: {{ $value }}x\n This will exhaust 2% of monthly budget in 1 hour.\n \n # Slow burn alert (10% budget in 6 hours)\n - alert: ErrorBudgetSlowBurn\n expr: |\n (\n service:error_budget_burn_rate_30m{service=\"api\"} > 3\n AND\n service:error_budget_burn_rate_6h{service=\"api\"} > 3\n )\n for: 15m\n labels:\n severity: warning\n team: platform\n annotations:\n summary: \"Slow error budget burn for {{ $labels.service }}\"\n description: |\n Service {{ $labels.service }} is burning error budget at 3x rate.\n Current burn rate: {{ $value }}x\n This will exhaust 10% of monthly budget in 6 hours.\n```\n\n### 5. SLO Dashboard\n\nCreate comprehensive SLO dashboards:\n\n**Grafana Dashboard Configuration**\n```python\ndef create_slo_dashboard():\n \"\"\"Generate Grafana dashboard for SLO monitoring\"\"\"\n return {\n \"dashboard\": {\n \"title\": \"Service SLO Dashboard\",\n \"panels\": [\n {\n \"title\": \"SLO Summary\",\n \"type\": \"stat\",\n \"gridPos\": {\"h\": 4, \"w\": 6, \"x\": 0, \"y\": 0},\n \"targets\": [{\n \"expr\": \"service:success_rate_30d{service=\\\"$service\\\"}\",\n \"legendFormat\": \"30-day SLO\"\n }],\n \"fieldConfig\": {\n \"defaults\": {\n \"thresholds\": {\n \"mode\": \"absolute\",\n \"steps\": [\n {\"color\": \"red\", \"value\": None},\n {\"color\": \"yellow\", \"value\": 99.5},\n {\"color\": \"green\", \"value\": 99.9}\n ]\n },\n \"unit\": \"percent\"\n }\n }\n },\n {\n \"title\": \"Error Budget Status\",\n \"type\": \"gauge\",\n \"gridPos\": {\"h\": 4, \"w\": 6, \"x\": 6, \"y\": 0},\n \"targets\": [{\n \"expr\": '''\n 100 * (\n 1 - (\n (1 - service:success_rate_30d{service=\"$service\"}/100) /\n (1 - $slo_target/100)\n )\n )\n ''',\n \"legendFormat\": \"Remaining Budget\"\n }],\n \"fieldConfig\": {\n \"defaults\": {\n \"min\": 0,\n \"max\": 100,\n \"thresholds\": {\n \"mode\": \"absolute\",\n \"steps\": [\n {\"color\": \"red\", \"value\": None},\n {\"color\": \"yellow\", \"value\": 20},\n {\"color\": \"green\", \"value\": 50}\n ]\n },\n \"unit\": \"percent\"\n }\n }\n },\n {\n \"title\": \"Burn Rate Trend\",\n \"type\": \"graph\",\n \"gridPos\": {\"h\": 8, \"w\": 12, \"x\": 12, \"y\": 0},\n \"targets\": [\n {\n \"expr\": \"service:error_budget_burn_rate_1h{service=\\\"$service\\\"}\",\n \"legendFormat\": \"1h burn rate\"\n },\n {\n \"expr\": \"service:error_budget_burn_rate_6h{service=\\\"$service\\\"}\",\n \"legendFormat\": \"6h burn rate\"\n },\n {\n \"expr\": \"service:error_budget_burn_rate_24h{service=\\\"$service\\\"}\",\n \"legendFormat\": \"24h burn rate\"\n }\n ],\n \"yaxes\": [{\n \"format\": \"short\",\n \"label\": \"Burn Rate (x)\",\n \"min\": 0\n }],\n \"alert\": {\n \"conditions\": [{\n \"evaluator\": {\"params\": [14.4], \"type\": \"gt\"},\n \"operator\": {\"type\": \"and\"},\n \"query\": {\"params\": [\"A\", \"5m\", \"now\"]},\n \"type\": \"query\"\n }],\n \"name\": \"High burn rate detected\"\n }\n }\n ]\n }\n }\n```\n\n### 6. SLO Reporting\n\nGenerate SLO reports and reviews:\n\n**SLO Report Generator**\n```python\nclass SLOReporter:\n def __init__(self, metrics_client):\n self.metrics = metrics_client\n \n def generate_monthly_report(self, service, month):\n \"\"\"Generate comprehensive monthly SLO report\"\"\"\n report_data = {\n 'service': service,\n 'period': month,\n 'slo_performance': self._calculate_slo_performance(service, month),\n 'incidents': self._analyze_incidents(service, month),\n 'error_budget': self._analyze_error_budget(service, month),\n 'trends': self._analyze_trends(service, month),\n 'recommendations': self._generate_recommendations(service, month)\n }\n \n return self._format_report(report_data)\n \n def _calculate_slo_performance(self, service, month):\n \"\"\"Calculate SLO performance metrics\"\"\"\n slos = {}\n \n # Availability SLO\n availability_query = f\"\"\"\n avg_over_time(\n service:success_rate_5m{{service=\"{service}\"}}[{month}]\n )\n \"\"\"\n slos['availability'] = {\n 'target': 99.9,\n 'actual': self.metrics.query(availability_query),\n 'met': self.metrics.query(availability_query) >= 99.9\n }\n \n # Latency SLO\n latency_query = f\"\"\"\n quantile_over_time(0.95,\n service:latency_p95_5m{{service=\"{service}\"}}[{month}]\n )\n \"\"\"\n slos['latency_p95'] = {\n 'target': 500, # ms\n 'actual': self.metrics.query(latency_query) * 1000,\n 'met': self.metrics.query(latency_query) * 1000 <= 500\n }\n \n return slos\n \n def _format_report(self, data):\n \"\"\"Format report as HTML\"\"\"\n return f\"\"\"\n\n\n\n SLO Report - {data['service']} - {data['period']}\n \n\n\n

SLO Report: {data['service']}

\n

Period: {data['period']}

\n \n
\n

Executive Summary

\n

Service reliability: {data['slo_performance']['availability']['actual']:.2f}%

\n

Error budget remaining: {data['error_budget']['remaining_percentage']:.1f}%

\n

Number of incidents: {len(data['incidents'])}

\n
\n \n
\n

SLO Performance

\n \n \n \n \n \n \n \n {self._format_slo_table_rows(data['slo_performance'])}\n
SLOTargetActualStatus
\n
\n \n
\n

Incident Analysis

\n {self._format_incident_analysis(data['incidents'])}\n
\n \n
\n

Recommendations

\n {self._format_recommendations(data['recommendations'])}\n
\n\n\n\"\"\"\n```\n\n### 7. SLO-Based Decision Making\n\nImplement SLO-driven engineering decisions:\n\n**SLO Decision Framework**\n```python\nclass SLODecisionFramework:\n def __init__(self, error_budget_policy):\n self.policy = error_budget_policy\n \n def make_release_decision(self, service, release_risk):\n \"\"\"Make release decisions based on error budget\"\"\"\n budget_status = self.get_error_budget_status(service)\n \n decision_matrix = {\n 'healthy': {\n 'low_risk': 'approve',\n 'medium_risk': 'approve',\n 'high_risk': 'review'\n },\n 'attention': {\n 'low_risk': 'approve',\n 'medium_risk': 'review',\n 'high_risk': 'defer'\n },\n 'warning': {\n 'low_risk': 'review',\n 'medium_risk': 'defer',\n 'high_risk': 'block'\n },\n 'critical': {\n 'low_risk': 'defer',\n 'medium_risk': 'block',\n 'high_risk': 'block'\n },\n 'exhausted': {\n 'low_risk': 'block',\n 'medium_risk': 'block',\n 'high_risk': 'block'\n }\n }\n \n decision = decision_matrix[budget_status['status']][release_risk]\n \n return {\n 'decision': decision,\n 'rationale': self._explain_decision(budget_status, release_risk),\n 'conditions': self._get_approval_conditions(decision, budget_status),\n 'alternative_actions': self._suggest_alternatives(decision, budget_status)\n }\n \n def prioritize_reliability_work(self, service):\n \"\"\"Prioritize reliability improvements based on SLO gaps\"\"\"\n slo_gaps = self.analyze_slo_gaps(service)\n \n priorities = []\n for gap in slo_gaps:\n priority_score = self.calculate_priority_score(gap)\n \n priorities.append({\n 'issue': gap['issue'],\n 'impact': gap['impact'],\n 'effort': gap['estimated_effort'],\n 'priority_score': priority_score,\n 'recommended_actions': self.recommend_actions(gap)\n })\n \n return sorted(priorities, key=lambda x: x['priority_score'], reverse=True)\n \n def calculate_toil_budget(self, team_size, slo_performance):\n \"\"\"Calculate how much toil is acceptable based on SLOs\"\"\"\n # If meeting SLOs, can afford more toil\n # If not meeting SLOs, need to reduce toil\n \n base_toil_percentage = 50 # Google SRE recommendation\n \n if slo_performance >= 100:\n # Exceeding SLO, can take on more toil\n toil_budget = base_toil_percentage + 10\n elif slo_performance >= 99:\n # Meeting SLO\n toil_budget = base_toil_percentage\n else:\n # Not meeting SLO, reduce toil\n toil_budget = base_toil_percentage - (100 - slo_performance) * 5\n \n return {\n 'toil_percentage': max(toil_budget, 20), # Minimum 20%\n 'toil_hours_per_week': (toil_budget / 100) * 40 * team_size,\n 'automation_hours_per_week': ((100 - toil_budget) / 100) * 40 * team_size\n }\n```\n\n### 8. SLO Templates\n\nProvide SLO templates for common services:\n\n**SLO Template Library**\n```python\nclass SLOTemplates:\n @staticmethod\n def get_api_service_template():\n \"\"\"SLO template for API services\"\"\"\n return {\n 'name': 'API Service SLO Template',\n 'slos': [\n {\n 'name': 'availability',\n 'description': 'The proportion of successful requests',\n 'sli': {\n 'type': 'ratio',\n 'good_events': 'requests with status != 5xx',\n 'total_events': 'all requests'\n },\n 'objectives': [\n {'window': '30d', 'target': 99.9}\n ]\n },\n {\n 'name': 'latency',\n 'description': 'The proportion of fast requests',\n 'sli': {\n 'type': 'ratio',\n 'good_events': 'requests faster than 500ms',\n 'total_events': 'all requests'\n },\n 'objectives': [\n {'window': '30d', 'target': 95.0}\n ]\n }\n ]\n }\n \n @staticmethod\n def get_data_pipeline_template():\n \"\"\"SLO template for data pipelines\"\"\"\n return {\n 'name': 'Data Pipeline SLO Template',\n 'slos': [\n {\n 'name': 'freshness',\n 'description': 'Data is processed within SLA',\n 'sli': {\n 'type': 'ratio',\n 'good_events': 'batches processed within 30 minutes',\n 'total_events': 'all batches'\n },\n 'objectives': [\n {'window': '7d', 'target': 99.0}\n ]\n },\n {\n 'name': 'completeness',\n 'description': 'All expected data is processed',\n 'sli': {\n 'type': 'ratio',\n 'good_events': 'records successfully processed',\n 'total_events': 'all records'\n },\n 'objectives': [\n {'window': '7d', 'target': 99.95}\n ]\n }\n ]\n }\n```\n\n### 9. SLO Automation\n\nAutomate SLO management:\n\n**SLO Automation Tools**\n```python\nclass SLOAutomation:\n def __init__(self):\n self.config = self.load_slo_config()\n \n def auto_generate_slos(self, service_discovery):\n \"\"\"Automatically generate SLOs for discovered services\"\"\"\n services = service_discovery.get_all_services()\n generated_slos = []\n \n for service in services:\n # Analyze service characteristics\n characteristics = self.analyze_service(service)\n \n # Select appropriate template\n template = self.select_template(characteristics)\n \n # Customize based on observed behavior\n customized_slo = self.customize_slo(template, service)\n \n generated_slos.append(customized_slo)\n \n return generated_slos\n \n def implement_progressive_slos(self, service):\n \"\"\"Implement progressively stricter SLOs\"\"\"\n return {\n 'phase1': {\n 'duration': '1 month',\n 'target': 99.0,\n 'description': 'Baseline establishment'\n },\n 'phase2': {\n 'duration': '2 months',\n 'target': 99.5,\n 'description': 'Initial improvement'\n },\n 'phase3': {\n 'duration': '3 months',\n 'target': 99.9,\n 'description': 'Production readiness'\n },\n 'phase4': {\n 'duration': 'ongoing',\n 'target': 99.95,\n 'description': 'Excellence'\n }\n }\n \n def create_slo_as_code(self):\n \"\"\"Define SLOs as code\"\"\"\n return '''\n# slo_definitions.yaml\napiVersion: slo.dev/v1\nkind: ServiceLevelObjective\nmetadata:\n name: api-availability\n namespace: production\nspec:\n service: api-service\n description: API service availability SLO\n \n indicator:\n type: ratio\n counter:\n metric: http_requests_total\n filters:\n - status_code != 5xx\n total:\n metric: http_requests_total\n \n objectives:\n - displayName: 30-day rolling window\n window: 30d\n target: 0.999\n \n alerting:\n burnRates:\n - severity: critical\n shortWindow: 1h\n longWindow: 5m\n burnRate: 14.4\n - severity: warning\n shortWindow: 6h\n longWindow: 30m\n burnRate: 3\n \n annotations:\n runbook: https://runbooks.example.com/api-availability\n dashboard: https://grafana.example.com/d/api-slo\n'''\n```\n\n### 10. SLO Culture and Governance\n\nEstablish SLO culture:\n\n**SLO Governance Framework**\n```python\nclass SLOGovernance:\n def establish_slo_culture(self):\n \"\"\"Establish SLO-driven culture\"\"\"\n return {\n 'principles': [\n 'SLOs are a shared responsibility',\n 'Error budgets drive prioritization',\n 'Reliability is a feature',\n 'Measure what matters to users'\n ],\n 'practices': {\n 'weekly_reviews': self.weekly_slo_review_template(),\n 'incident_retrospectives': self.slo_incident_template(),\n 'quarterly_planning': self.quarterly_slo_planning(),\n 'stakeholder_communication': self.stakeholder_report_template()\n },\n 'roles': {\n 'slo_owner': {\n 'responsibilities': [\n 'Define and maintain SLO definitions',\n 'Monitor SLO performance',\n 'Lead SLO reviews',\n 'Communicate with stakeholders'\n ]\n },\n 'engineering_team': {\n 'responsibilities': [\n 'Implement SLI measurements',\n 'Respond to SLO breaches',\n 'Improve reliability',\n 'Participate in reviews'\n ]\n },\n 'product_owner': {\n 'responsibilities': [\n 'Balance features vs reliability',\n 'Approve error budget usage',\n 'Set business priorities',\n 'Communicate with customers'\n ]\n }\n }\n }\n \n def create_slo_review_process(self):\n \"\"\"Create structured SLO review process\"\"\"\n return '''\n# Weekly SLO Review Template\n\n## Agenda (30 minutes)\n\n### 1. SLO Performance Review (10 min)\n- Current SLO status for all services\n- Error budget consumption rate\n- Trend analysis\n\n### 2. Incident Review (10 min)\n- Incidents impacting SLOs\n- Root cause analysis\n- Action items\n\n### 3. Decision Making (10 min)\n- Release approvals/deferrals\n- Resource allocation\n- Priority adjustments\n\n## Review Checklist\n\n- [ ] All SLOs reviewed\n- [ ] Burn rates analyzed\n- [ ] Incidents discussed\n- [ ] Action items assigned\n- [ ] Decisions documented\n\n## Output Template\n\n### Service: [Service Name]\n- **SLO Status**: [Green/Yellow/Red]\n- **Error Budget**: [XX%] remaining\n- **Key Issues**: [List]\n- **Actions**: [List with owners]\n- **Decisions**: [List]\n'''\n```\n\n## Output Format\n\n1. **SLO Framework**: Comprehensive SLO design and objectives\n2. **SLI Implementation**: Code and queries for measuring SLIs\n3. **Error Budget Tracking**: Calculations and burn rate monitoring\n4. **Monitoring Setup**: Prometheus rules and Grafana dashboards\n5. **Alert Configuration**: Multi-window multi-burn-rate alerts\n6. **Reporting Templates**: Monthly reports and reviews\n7. **Decision Framework**: SLO-based engineering decisions\n8. **Automation Tools**: SLO-as-code and auto-generation\n9. **Governance Process**: Culture and review processes\n\nFocus on creating meaningful SLOs that balance reliability with feature velocity, providing clear signals for engineering decisions and fostering a culture of reliability.","contentHash":"546e6e7554ae7e88cabe6b27838d40ca5a3f88c483453f3a62e8461f40e278bf","copies":0,"createdAt":"2025-08-12T16:09:38.969Z","description":"Implement Service Level Objectives (SLOs)","github":{"repoUrl":"https://github.com/Commands-com/commands","lastSyncDirection":"from-github","metadata":{"importedFrom":"github_repository","repoPrivate":false,"repoDefaultBranch":"main","connectedAt":"2025-08-12T16:09:38.969Z"},"importedAt":"2025-08-12T16:09:38.969Z","lastSyncAt":"2025-08-17T17:57:48.747Z","fileMapping":{"license":null,"readme":null,"assets":[],"mainFile":"tools/slo-implement.md"},"selectedCommand":"slo-implement","fileShas":{"mainFile":"589870f6ddfd7e5b9f22e0360556a10fd743602d","yamlPath":"313647b1fb381389da33b7913e95baf617c4b392"},"branch":"main","connectionType":"commands_yaml","connected":true,"lastSyncCommit":"01591bc061d236bde47bf23b0f47e8afcf1a5144","importSource":"repository_import","installationId":"69232615","syncStatus":"synced"},"githubRepoUrl":"https://github.com/Commands-com/commands","id":"e8f4522e-5b90-4593-a21e-85fb7646318e","inputParameters":[{"defaultValue":"99.9","name":"slo_target","options":["99","99.5","99.9","99.95","99.99"],"description":"Target availability percentage","label":"SLO Target","type":"select","required":false},{"defaultValue":"alert","name":"error_budget_policy","options":["alert","throttle-deploys","freeze-features","custom"],"description":"How to handle error budget","label":"Error Budget Policy","type":"select","required":false}],"instructions":"Implement Service Level Objectives (SLOs)","likes":0,"mcp_search_content":"","organizationUsername":"commands-com","price":"free","search_content":"slo implement implement service level objectives (slos) /slo-implement deployment claude-code@2025.06","title":"SLO Implement","type":"command","updatedAt":"2025-08-17T17:57:48.747Z","userId":"W0V8NAw5AhWRwcuwSoFLOi1Yem83","visibility":"public","name":"slo-implement","userInteraction":{"userHasStarred":false}}