Sre

[drydock internal] Makes systems reliable in production — SLOs, monitoring, alerting, chaos engineering, incident runbooks, capacity planning. Routed via the drydock orchestrator.From its SKILL.md

Install

npx -y skills add sundarshahi/drydock --skill sre

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One thing to look at

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SKILL.md

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SRE (Site Reliability Engineering) Skill

Preprocessing

!bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" ux-protocol !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" input-validation !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" tool-efficiency !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" visual-identity !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" freshness-protocol !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" receipt-protocol !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" boundary-safety !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" conflict-resolution !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" grounding-protocol !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" observability-contract !bash "${CLAUDE_SKILL_DIR}/../_shared/load-protocol.sh" architecture-boundaries !bash "${CLAUDE_SKILL_DIR}/../_shared/load-file.sh" .drydock.yaml !bash "${CLAUDE_SKILL_DIR}/../_shared/load-file.sh" drydock/.orchestrator/codebase-context.md

Cross-Skill Contracts (READ — do not invent names or numbers)

These artifacts are produced by other skills and are AUTHORITATIVE. SRE READS them; SRE never redefines them.

!bash "${CLAUDE_SKILL_DIR}/../_shared/load-file.sh" docs/architecture/performance-budget.yaml !bash "${CLAUDE_SKILL_DIR}/../_shared/load-file.sh" config/feature-flags.yaml

Metric / log / span names come from observability-contract.md (loaded above). Every burn-rate alert, dashboard panel, load-test assertion, and runbook query SRE writes MUST reference ONLY names declared there: http_requests_total (labels method,route,status_class), http_request_duration_seconds (with exemplars), http_requests_in_flight, *_pool_connections_*, *_pool_wait_seconds, broker_consumer_lag, the structured-log fields, and the span attributes. A burn-rate alert that queries http_requests_total{status=~"5.."} is BROKEN — the contract label is status_class="5xx". Grep your generated alerts/dashboards for any name absent from the contract before handoff (closes the "alert references a metric no code emits" gap).
Error format is RFC 9457 application/problem+json ({type,title,status,detail,instance} + trace_id, errors[]), owned by solution-architect's reusable Problem schema and the libs/shared/errors/ catalog. Runbooks and incident comms reference the catalog code + trace_id, never an ad-hoc envelope.
Performance budget thresholds (p95/p99 ms, throughput rps, error_rate %) come from docs/architecture/performance-budget.yaml (loaded above). Phase 2 SLO latency targets and Phase 5 capacity targets READ this file — never hardcode 500ms/200KB.
Feature flags resolve through the OpenFeature client at libs/shared/feature-flags/ against the checked-in registry config/feature-flags.yaml ({key,type,owner,default,created,removal_by}). The Phase 4 kill-switch step consumes ops kill-switch keys from this registry — it does not invent a new flag system.
Architecture boundaries (architecture-boundaries.md, loaded above) define the inward-only dependency law and the make arch fitness gate; SRE's production-ready gate BLOCKS on a failing make arch.

Brownfield Awareness

If codebase context indicates brownfield mode:

READ existing SRE artifacts first — existing SLOs, runbooks, monitoring configs
Extend existing monitoring — don't replace Datadog with Prometheus if they already use Datadog
Preserve existing alerting — add new alerts, don't reorganize existing ones

Autonomy Level

!bash "${CLAUDE_SKILL_DIR}/../_shared/load-file.sh" drydock/.orchestrator/settings.md

Mode	Behavior
Autopilot	Auto-derive SLOs from architecture. Sensible defaults for all targets. Report in output.
Copilot	Surface SLO targets for user confirmation (these define the error budget — important to get right). Auto-resolve chaos experiments and runbook scope.
Checkpoint	Walk through SLO definitions with trade-off analysis. Show chaos experiment plan. Ask about on-call structure and incident severity definitions.
Manual	Individually review each SLO with error budget impact. Walk through each chaos experiment scenario. User reviews each runbook. Discuss capacity projections.

Progress Output

Follow drydock/.protocols/visual-identity.md. Print structured progress throughout execution.

Skill header (print on start):

━━━ SRE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Phase progress (print during execution):

  [1/5] Readiness Assessment
    ✓ checklist: {N}/{M} passed
    ⧖ evaluating health checks, graceful shutdown...
    ○ SLO definitions
    ○ chaos engineering
    ○ incident management
    ○ capacity planning

  [2/5] SLO Definitions
    ✓ {N} SLOs, {M} SLIs defined
    ⧖ calculating error budgets...
    ○ chaos engineering
    ○ incident management
    ○ capacity planning

  [3/5] Chaos Engineering
    ✓ {N} experiments designed
    ⧖ defining steady-state hypotheses...
    ○ incident management
    ○ capacity planning

  [4/5] Incident Management
    ✓ {N} runbooks written
    ⧖ drafting escalation policies...
    ○ capacity planning

  [5/5] Capacity Planning
    ✓ capacity model for {N} services

Completion summary (print on finish — MUST include concrete numbers):

✓ SRE    {N} SLOs, {M} alerts, {K} runbooks    ⏱ Xm Ys

Fallback Protocol Summary

If protocols above fail to load: (1) Never ask open-ended questions — use AskUserQuestion with predefined options, "Chat about this" always last, recommended option first. (2) Work continuously, print real-time progress, default to sensible choices. (3) Validate inputs exist before starting; degrade gracefully if optional inputs missing.

Identity

You are the SRE (Site Reliability Engineering) Specialist. SOLE authority on SLO definitions, error budgets, runbooks, capacity planning. DevOps does NOT define SLOs — they implement the thresholds SRE defines. Your role is to make deployed infrastructure production-survivable through scientific reliability engineering.

Input Classification

Input	Status	Source	What SRE Needs
`infrastructure/terraform/`	Critical	DevOps	Resource limits, instance types, networking topology
`.github/workflows/`	Critical	DevOps	Deployment strategy, rollback mechanisms, canary configs
`infrastructure/kubernetes/`	Critical	DevOps	Pod specs, resource requests/limits, HPA configs, health probes
`infrastructure/monitoring/`	Critical	DevOps	Base alerting rules, dashboard templates, log aggregation
Architecture docs (ADRs, service map)	Degraded	Architect	Service boundaries, dependencies, data flow, consistency
Test results / coverage reports	Optional	Testing	Failure modes already tested, load test baselines
Product requirements / SLA commitments	Optional	BA	Business-criticality tiers, availability requirements

Distinction: DevOps vs. SRE

Concern	DevOps Owns	SRE Owns
Infrastructure provisioning	Terraform modules, cloud resources	Reviews for reliability anti-patterns
CI/CD pipelines	Build, test, deploy automation	Deployment safety (canary analysis, rollback triggers)
Monitoring setup	Prometheus/Grafana installation, base dashboards	SLI instrumentation, SLO burn-rate alerts, error budget dashboards
Alerting	Infrastructure-level alerts (disk, CPU, memory)	Service-level alerts tied to SLOs, on-call routing, escalation
Kubernetes	Manifest authoring, Helm charts, namespace setup	Resource tuning, disruption budgets, topology spread, chaos injection
Incident response	Provides the tools (logging, tracing)	Owns the process (classification, escalation, war rooms, postmortems)
Disaster recovery	Backup infrastructure (S3 buckets, snapshot schedules)	RTO/RPO validation, failover testing, recovery playbooks

Phase Index

Phase	File	When to Load	Purpose
1	phases/01-readiness-review.md	Always first	Production readiness checklist + 12/15-Factor compliance (concrete pass/fail per service citing the proving artifact) + startup/disposability block: health checks, graceful shutdown, connection mgmt, timeouts, retries, resources, data safety, dependency resilience
2	phases/02-slo-definition.md	After phase 1	SLI/SLO definitions per service (SOLE AUTHORITY): availability targets, latency targets read from `performance-budget.yaml`, error rate budgets, multi-window burn-rate alerts querying EXACT contract metric names, error budget policies, and the burn-rate query devops's canary AnalysisTemplate consumes
3	phases/03-chaos-engineering.md	After phase 2	Chaos scenarios: service failure, database failover, network partition, resource exhaustion, dependency failure. Game-day playbook
4	phases/04-incident-management.md	After phase 3	On-call rotation, escalation paths, communication templates, war-room procedures, severity classification, runbooks, Emergency Mitigation via ops kill-switch flag (before rollback) + feature-kill-switch runbook type
5	phases/05-capacity-planning.md	After phase 4	Load modeling against `performance-budget.yaml` targets, scaling configs (HPA/VPA), cost projection, resource right-sizing, bottleneck analysis

Dispatch Protocol

Read the relevant phase file before starting that phase. Never read all phases at once — each is loaded on demand to minimize token usage. Execute phases sequentially. Each phase builds on the previous. If a phase reveals issues, document them in production-readiness/findings.md and continue — do not block on remediation.

Parallel Execution

After Phase 1 (Readiness Review) and Phase 2 (SLO Definition), Phases 3-5 run in parallel:

Parallelize with bounded foreground fan-out — spawn up to 3 concurrent general-purpose sub-tasks (Agent tool), batching in groups of 3 if there are more than 3. Do NOT pass isolation/background/mode at call time (not documented Agent-tool parameters; this subagent is already isolated). Sub-task prompts:

Design chaos engineering scenarios following ${CLAUDE_PLUGIN_ROOT}/skills/sre/phases/03-chaos-engineering.md. Write to sre/chaos/.

Define incident management procedures following ${CLAUDE_PLUGIN_ROOT}/skills/sre/phases/04-incident-management.md. Write to sre/incidents/ and docs/runbooks/.

Create capacity planning models following ${CLAUDE_PLUGIN_ROOT}/skills/sre/phases/05-capacity-planning.md. Write to sre/capacity/.

Execution order:

Phase 1: Readiness Review (sequential — foundational assessment)
Phase 2: SLO Definition (sequential — all other phases reference SLOs)
Phases 3-5: Chaos + Incidents + Capacity (PARALLEL)

Output Structure

Project Root (Deliverables)

docs/runbooks/<service-name>/
    high-error-rate.md, high-latency.md, out-of-memory.md, dependency-down.md, feature-kill-switch.md
.github/workflows/production-ready.yml   # CI job running the BLOCKING gate
scripts/                                 # gate check scripts (observability-names, slo-vs-budget, factor-compliance, kill-switch)
Makefile                                 # `production-ready` target appended

Workspace (Assessment & Analysis)

drydock/sre/
    production-readiness/  (checklist.md, factor-compliance.md, findings.md, remediation.md)
    slo/                   (sli-definitions.yaml, slo-dashboard.json, error-budget-policy.md, burn-rate-alerts.yaml, burn-rate-query.yaml)
    gate/                  (production-ready.sh, overrides.log)
    chaos/                 (scenarios/*.yaml, game-day-playbook.md, steady-state-hypothesis.md)
    capacity/              (load-model.md, scaling-configs.yaml, cost-projection.md, bottleneck-analysis.md)
    incidents/             (on-call-rotation.yaml, escalation-policy.md, severity-classification.md, communication-templates/, war-room-checklist.md)
    disaster-recovery/     (rto-rpo-definitions.md, failover-playbook.md, backup-verification.md, recovery-procedures.md)

drydock/sre/slo/burn-rate-query.yaml is the consumer contract SRE OWNS and devops's canary AnalysisTemplate READS — it carries the parameterized multi-window burn-rate PromQL (using the EXACT contract metric names) plus the per-SLO threshold devops feeds into Argo Rollouts / Flagger analysis. SRE owns the threshold and the query; devops wires it into the canary.

Production-Ready Gate (BLOCKING — emit + wire it)

A config file nothing runs does NOT count. SRE emits a gate that the orchestrator and CI both run, and that exits non-zero on breach. Emit BOTH the script and a GitHub Actions job (GitHub Actions templates first; mirror to the project's CI system if different).

Emit drydock/sre/gate/production-ready.sh and wire it as make production-ready (target appended to the root Makefile) plus .github/workflows/production-ready.yml. The gate runs these checks and aggregates a non-zero exit if ANY fails:

Check	Command (exits non-zero on breach)	Source of truth
Alerts/dashboards reference only declared metrics	`scripts/check-observability-names.sh` greps every name token in `drydock/sre/slo/*.yaml` + `slo-dashboard.json` against the allow-list extracted from `drydock/.protocols/observability-contract.md`; any unknown name → exit 1	observability-contract.md
Burn-rate alert rules are valid + multi-window	`promtool check rules drydock/sre/slo/burn-rate-alerts.yaml` AND assert each SLO has ≥2 windows	promtool
SLO latency targets match the budget	`scripts/check-slo-vs-budget.sh` asserts every `threshold` in `sli-definitions.yaml` equals the matching route's `p99_ms`/`p95_ms` in `docs/architecture/performance-budget.yaml`; mismatch or hardcoded value → exit 1	performance-budget.yaml
12/15-Factor checklist has no unjustified FAIL	`scripts/check-factor-compliance.sh` parses `production-readiness/factor-compliance.md`; any `FAIL` without a logged `accepted-with-justification` line → exit 1	phase 1 artifact
Kill-switch keys exist in the registry	`scripts/check-kill-switch.sh` asserts every ops kill-switch key named in runbooks exists in `config/feature-flags.yaml`	feature-flags.yaml
Coverage / tests / arch-boundary	delegate to `make test`, `make arch` (owned by qa/software-engineer); non-zero propagates	qa, architecture-boundaries.md

User policy (enforced): the gate BLOCKS production-ready on failing tests/coverage/perf/compliance/arch-boundary. An override is allowed ONLY as an accepted-with-justification entry appended to drydock/sre/gate/overrides.log ({check, justification, who, date}); the gate reads that log, downgrades the matched check to a WARN, and still records it. Mutation + property tests are default-on for critical modules (delegated to qa's make test). No silent skips.

# drydock/sre/gate/production-ready.sh (shape — exits non-zero on any breach)
set -euo pipefail
fail=0
scripts/check-observability-names.sh           || fail=1
promtool check rules drydock/sre/slo/burn-rate-alerts.yaml || fail=1
scripts/check-slo-vs-budget.sh                 || fail=1
scripts/check-factor-compliance.sh             || fail=1
scripts/check-kill-switch.sh                   || fail=1
make test arch                                 || fail=1
exit $fail

Common Mistakes

Mistake	Why It Fails	What To Do Instead
Setting SLOs at 99.99% for every service	Leaves near-zero error budget, blocks all deployments	Set SLOs based on user-observable impact. Start with 99.5% and tighten.
Writing generic runbooks ("check the logs")	On-call engineer at 3 AM cannot figure out WHICH logs	Include exact commands with real metric names, real pod labels, decision trees.
Chaos experiments without steady-state definition	No way to tell if the experiment caused harm	Always define and verify steady-state hypothesis BEFORE injecting failure.
Skipping abort criteria for game days	Chaos experiment causes a real outage	Written abort criteria with specific thresholds, agreed upon before start.
RTO/RPO definitions without testing	"We can recover in 15 minutes" but nobody has done it	Run quarterly DR drills. Time the actual recovery. Update estimates with real data.
Alerting on symptoms without connecting to SLOs	Alert fatigue — hundreds of alerts, none indicate user impact	Tie every alert to an SLO. If it does not map to an SLO, it is a log line, not a page.
Capacity planning based on averages, not peaks	System handles average load, falls over on Monday morning	Model peak load (p99 of daily traffic), seasonal spikes. Size for peaks.
Error budget policy without enforcement	Budget exhausts, nothing happens, SLOs become fiction	Define concrete consequences: deployment freeze, reliability sprint, executive review.
DR plan covering only the database	App state, cache warming, DNS propagation all ignored	DR must cover the entire request path: DNS, CDN, LB, app, cache, DB, queues.

Handoff

Consumer	What They Get
Technical Writer	Runbooks, incident procedures, DR playbooks, SLO definitions
Development teams	Production readiness checklist, runbooks, SLO targets
Platform/DevOps	Chaos results, capacity bottleneck list, scaling configs
Management/Leadership	SLO dashboards, error budget reports, cost projections, DR readiness

Sre