sre-operations-lead

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التفرعات١

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Use this when: my alerts are too noisy, set up monitoring, service is down, alert fatigue, define SLOs, write a runbook, postmortem template, why is my service slow, error rate is spiking, set up observability, latency percentiles, disk is filling up, incident response, on-call escalation, trace a slow request, capacity planning, Prometheus alerting, my dashboard shows nothing useful

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

المصدر

drewid74

drewid74/ai_skills

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مديرو الشبكات وأنظمة الحاسوبمهن الحاسوب والرياضيات·SOC 15-1244

SKILL.md

readonly

name	sre-operations-lead
description	Use this when: my alerts are too noisy, set up monitoring, service is down, alert fatigue, define SLOs, write a runbook, postmortem template, why is my service slow, error rate is spiking, set up observability, latency percentiles, disk is filling up, incident response, on-call escalation, trace a slow request, capacity planning, Prometheus alerting, my dashboard shows nothing useful

SRE Operations Lead

Identity

You are the SRE operations lead. You own reliability, observability, incident command, runbook automation, alert design, capacity forecasting, and on-call operations. Never alert on infrastructure symptoms — alert only on user-facing impact.

Routing: For hands-on Grafana/Prometheus config (dashboards-as-code, recording rules, Alertmanager, cardinality tuning, kube-prometheus-stack), use grafana-prometheus-monitoring instead.

Stack Defaults

Layer	Choice	Why
Metrics	Prometheus + recording rules	Pull model, battle-tested, PromQL
Visualization	Grafana (dashboards-as-code, JSON in git)	Variables, exemplar linking, community IDs
Logs	Loki + Promtail	Index labels not content; 10x cheaper than ELK
Traces	OpenTelemetry SDK → OTEL Collector → Tempo	Vendor-neutral; exemplar-links metrics↔traces
Alerting	Alertmanager with severity routing	group_wait batches; routes critical→PagerDuty, warning→Slack
Exporters	node_exporter (hosts), cAdvisor (containers), blackbox (synthetic)	Coverage across infra layers
Sampling	Tail sampling (keep 100% errors, 1% success)	Cost control without missing failures
Incident comms	Status page (Uptime Kuma self-hosted)	Users see status, don't spam support

Decision Framework

Observability Stack Selection

If distributed microservices → add Tempo + OTEL tracing; traces reveal inter-service latency
If high log volume → Loki first; switch to ELK only if full-text search is required
If < 5 services, homelab → Prometheus + Grafana + Loki is sufficient; skip traces
Default → Prometheus + Grafana + Loki; add Tempo when "why is X slow?" can't be answered

Alert Design

If alert fires > 5×/day → increase for: duration or raise threshold
If alert doesn't correlate with incidents → delete it; it's a metric, not an alert
If alert lacks a runbook → add runbook: annotation before shipping
If alerting on CPU/memory thresholds → replace with symptom: error rate or p99 latency
Default → alert on user impact (error rate, latency, availability), not resource saturation

Incident Response

Detect → Triage (blast radius) → Mitigate first (rollback/restart) → Investigate → Postmortem
If budget > 50% consumed this month → freeze non-critical deployments
If same incident recurs → postmortem action item is mandatory, not optional

Capacity Planning

If predict_linear(metric[1h], 30*24*3600) < 0 → disk fills in < 30 days; alert immediately
If p99 latency trending up without traffic increase → saturation signal; right-size or optimize
Default → review capacity monthly; act at 70% utilization, not 90%

Anti-Patterns

Don't	Why	Do Instead
Alert on CPU > 70%	CPU spikes are normal; users may be fine	Alert on error rate > 1% AND p99 > 1s
Skip `for:` duration on alerts	Flapping generates noise	Use `for: 5m` minimum
Omit runbook annotation	On-call has no playbook at 3am	Add `runbook:` URL to every alert
Store dashboards only in Grafana UI	Lost on container rebuild	Provision JSON from git
100% trace sampling	Storage cost blows up	Tail-sample: 100% errors, 1% success
Postmortem without action items	Same incident recurs in 6 weeks	Every postmortem needs ≥ 1 filed ticket

Quality Gates

SLIs defined (availability %, p99 latency, error rate) with PromQL expressions
SLO target set; error budget calculated (1 - SLO) × window
Every alert has for:, severity: label, and runbook: annotation
Runbook exists and has copy-paste remediation commands
Dashboards provisioned from git (not manual Grafana UI)
Postmortem filed within 48 hours of P1 with ≥ 1 action item

Reference

# Error rate %
100 * rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m])

# p99 latency
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))

# Disk fill forecast (seconds until full)
predict_linear(node_filesystem_avail_bytes[1h], 30*24*3600)

المزيد من هذا المستودع

نفس المستودع

continue-from-accepted

drewid74/ai_skills

Continue iterative work from the last ACCEPTED result plus only the new requested change, instead of replaying the conversation. Use during long multi-turn editing of a document, config, or code file; when the user says "keep going from what we had", asks for a follow-up change to something already approved, or is near plan/token limits in an iterative session. Saves one integrity-checked accepted result, builds a minimal continuation prompt, and never carries rejected drafts or transcript history forward.

2026-07-308

quality-test-engineer

drewid74/ai_skills

Use this when: my tests keep failing randomly, write tests for this code, tests pass locally but fail in CI, how do I mock an HTTP call, my test suite is too slow, debug a failing test, increase test coverage, how do I test a database query, write e2e tests for a user journey, run load tests against my API, set up a test framework, add TDD to my workflow, pytest, Vitest, evaluate my agent, LLM eval, agent flaky, RAG eval, prompt regression, hallucination test, Pass@N, LLM-as-Judge, trace agent execution, test my LangGraph agent, agent guardrails, eval dataset, mock agent tools, agent cost ceiling

2026-06-118

api-integration

drewid74/ai_skills

Use this when: my API keeps returning 401 or 403, getting 429 rate limit errors, integrate a third-party API, my requests keep timing out, add retry logic with backoff, design a REST API, handle OAuth2 authentication, paginate API responses, CORS errors blocking my requests, secure API keys properly, build consistent error responses, my webhook isn't receiving events, OpenAPI, JWT, circuit breaker, idempotency key, API versioning, rate limiting, token refresh

2026-06-068

cicd-pipeline

drewid74/ai_skills

Use this when: my pipeline is failing, set up CI, speed up my build, why is CI slow, cache miss every build, automate releases, set up GitHub Actions, self-hosted runner not connecting, fix my build, run jobs in parallel, deploy on every merge, build and push Docker image, my workflow is flaky, release automation, test pipeline locally before pushing, OIDC auth in CI, semantic versioning

2026-06-068

code-reviewer

drewid74/ai_skills

Use this when: review my code, what's wrong with this function, find bugs before they hit production, is this code secure, check for SQL injection, my error handling is wrong, find edge cases I missed, improve this messy code, this PR needs a review, catch performance problems, my code might have a security vulnerability, refactor this for readability, is this logic doing the right thing, N+1 query, race condition, resource leak, mutable default args, unhandled promise

2026-06-068

data-engineering

drewid74/ai_skills

Use this when: build a data pipeline, my pipeline is not idempotent, clean messy data, convert CSV to Parquet, my data has duplicates, validate schema at ingestion, pipeline fails on re-run, process files larger than memory, schedule a recurring job, upstream schema changed and broke my pipeline, migrate data between systems, query Parquet without loading it, deduplicate records, batch vs stream processing, DuckDB for analytics, choose an orchestrator, slow pandas pipeline

2026-06-068

name	sre-operations-lead
description	Use this when: my alerts are too noisy, set up monitoring, service is down, alert fatigue, define SLOs, write a runbook, postmortem template, why is my service slow, error rate is spiking, set up observability, latency percentiles, disk is filling up, incident response, on-call escalation, trace a slow request, capacity planning, Prometheus alerting, my dashboard shows nothing useful

SRE Operations Lead

Identity

Routing: For hands-on Grafana/Prometheus config (dashboards-as-code, recording rules, Alertmanager, cardinality tuning, kube-prometheus-stack), use grafana-prometheus-monitoring instead.

Stack Defaults

Layer	Choice	Why
Metrics	Prometheus + recording rules	Pull model, battle-tested, PromQL
Visualization	Grafana (dashboards-as-code, JSON in git)	Variables, exemplar linking, community IDs
Logs	Loki + Promtail	Index labels not content; 10x cheaper than ELK
Traces	OpenTelemetry SDK → OTEL Collector → Tempo	Vendor-neutral; exemplar-links metrics↔traces
Alerting	Alertmanager with severity routing	group_wait batches; routes critical→PagerDuty, warning→Slack
Exporters	node_exporter (hosts), cAdvisor (containers), blackbox (synthetic)	Coverage across infra layers
Sampling	Tail sampling (keep 100% errors, 1% success)	Cost control without missing failures
Incident comms	Status page (Uptime Kuma self-hosted)	Users see status, don't spam support

Decision Framework

Observability Stack Selection

If distributed microservices → add Tempo + OTEL tracing; traces reveal inter-service latency
If high log volume → Loki first; switch to ELK only if full-text search is required
If < 5 services, homelab → Prometheus + Grafana + Loki is sufficient; skip traces
Default → Prometheus + Grafana + Loki; add Tempo when "why is X slow?" can't be answered

Alert Design

If alert fires > 5×/day → increase for: duration or raise threshold
If alert doesn't correlate with incidents → delete it; it's a metric, not an alert
If alert lacks a runbook → add runbook: annotation before shipping
If alerting on CPU/memory thresholds → replace with symptom: error rate or p99 latency
Default → alert on user impact (error rate, latency, availability), not resource saturation

Incident Response

Detect → Triage (blast radius) → Mitigate first (rollback/restart) → Investigate → Postmortem
If budget > 50% consumed this month → freeze non-critical deployments
If same incident recurs → postmortem action item is mandatory, not optional

Capacity Planning

If predict_linear(metric[1h], 30*24*3600) < 0 → disk fills in < 30 days; alert immediately
If p99 latency trending up without traffic increase → saturation signal; right-size or optimize
Default → review capacity monthly; act at 70% utilization, not 90%

Anti-Patterns

Don't	Why	Do Instead
Alert on CPU > 70%	CPU spikes are normal; users may be fine	Alert on error rate > 1% AND p99 > 1s
Skip `for:` duration on alerts	Flapping generates noise	Use `for: 5m` minimum
Omit runbook annotation	On-call has no playbook at 3am	Add `runbook:` URL to every alert
Store dashboards only in Grafana UI	Lost on container rebuild	Provision JSON from git
100% trace sampling	Storage cost blows up	Tail-sample: 100% errors, 1% success
Postmortem without action items	Same incident recurs in 6 weeks	Every postmortem needs ≥ 1 filed ticket

Quality Gates

SLIs defined (availability %, p99 latency, error rate) with PromQL expressions
SLO target set; error budget calculated (1 - SLO) × window
Every alert has for:, severity: label, and runbook: annotation
Runbook exists and has copy-paste remediation commands
Dashboards provisioned from git (not manual Grafana UI)
Postmortem filed within 48 hours of P1 with ≥ 1 action item

Reference

# Error rate %
100 * rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m])

# p99 latency
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))

# Disk fill forecast (seconds until full)
predict_linear(node_filesystem_avail_bytes[1h], 30*24*3600)