xint
X Intelligence CLI — search, monitor, analyze, and engage on X/Twitter. TypeScript + Bun. AI agent skill.
Complete observability & reliability engineering system. Use when designing monitoring, implementing structured logging, setting up distributed tracing, building alerting systems, creating SLO/SLI frameworks, running incident response, conducting post-mortems, or auditing system reliability. Covers all three pillars (logs/metrics/traces), alert design, dashboard architecture, on-call operations, chaos engineering, and cost optimization.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install 1kalin/afrexai-observability-engineInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Complete system for building observable, reliable services — from structured logging to incident response to SLO-driven development.
Score your current observability posture:
| Signal | Healthy (2) | Weak (1) | Missing (0) |
|---|---|---|---|
| Structured logging | JSON logs with trace_id correlation | Logs exist but unstructured | Console.log / print statements |
| Metrics collection | RED/USE metrics with dashboards | Some metrics, no dashboards | No metrics |
| Distributed tracing | Full request path with sampling | Partial traces, key services only | No tracing |
| Alerting | SLO-based alerts with runbooks | Threshold alerts, some runbooks | No alerts or all-noise |
| Incident response | Defined process with roles + post-mortems | Ad-hoc response, some docs | "Whoever notices fixes it" |
| SLOs defined | SLOs with error budgets tracked weekly | Informal availability targets | No reliability targets |
| On-call rotation | Structured rotation with escalation | Informal "call someone" | No on-call |
| Cost management | Observability budget tracked monthly | Some awareness of costs | No idea what you spend |
12-16: Production-grade. Focus on optimization. 8-11: Foundation exists. Fill the gaps systematically. 4-7: Significant risk. Prioritize alerting + incident response. 0-3: Flying blind. Start with Phase 1 immediately.
Application → Structured JSON → Log Router → Storage → Query Engine
↓
Alert Pipeline
| Field | Type | Purpose | Example |
|---|---|---|---|
timestamp |
ISO-8601 UTC | When | 2026-02-22T18:30:00.123Z |
level |
enum | Severity | info, warn, error, fatal |
service |
string | Which service | payment-api |
| `versio... |
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
Complete observability & reliability engineering system for AI agents. Covers structured logging, metrics, distributed tracing, SLOs, alerting, incident response, post-mortems, on-call, chaos engineering, and cost optimization.
clawhub install afrexai-observability-engine
A 12-phase system covering the full observability lifecycle:
Plus: /16 health check, 100-point quality rubric, 10 commandments, 12 natural language commands.
Tell your agent:
"Audit our observability posture"
Or:
"Design SLOs for our payment API"
For industry-specific observability patterns, check out AfrexAI Context Packs ($47) — SaaS, Fintech, Healthcare, and 7 more verticals.
# Observability & Reliability Engineering
Complete system for building observable, reliable services — from structured logging to incident response to SLO-driven development.
---
## Quick Health Check (/16)
Score your current observability posture:
| Signal | Healthy (2) | Weak (1) | Missing (0) |
|--------|-------------|----------|-------------|
| Structured logging | JSON logs with trace_id correlation | Logs exist but unstructured | Console.log / print statements |
| Metrics collection | RED/USE metrics with dashboards | Some metrics, no dashboards | No metrics |
| Distributed tracing | Full request path with sampling | Partial traces, key services only | No tracing |
| Alerting | SLO-based alerts with runbooks | Threshold alerts, some runbooks | No alerts or all-noise |
| Incident response | Defined process with roles + post-mortems | Ad-hoc response, some docs | "Whoever notices fixes it" |
| SLOs defined | SLOs with error budgets tracked weekly | Informal availability targets | No reliability targets |
| On-call rotation | Structured rotation with escalation | Informal "call someone" | No on-call |
| Cost management | Observability budget tracked monthly | Some awareness of costs | No idea what you spend |
**12-16:** Production-grade. Focus on optimization.
**8-11:** Foundation exists. Fill the gaps systematically.
**4-7:** Significant risk. Prioritize alerting + incident response.
**0-3:** Flying blind. Start with Phase 1 immediately.
---
## Phase 1: Structured Logging
### Log Architecture
```
Application → Structured JSON → Log Router → Storage → Query Engine
↓
Alert Pipeline
```
### Required Fields (Every Log Line)
| Field | Type | Purpose | Example |
|-------|------|---------|---------|
| `timestamp` | ISO-8601 UTC | When | `2026-02-22T18:30:00.123Z` |
| `level` | enum | Severity | `info`, `warn`, `error`, `fatal` |
| `service` | string | Which service | `payment-api` |
| `versio...# AfrexAI Observability Engine 🔭 Complete observability & reliability engineering system for AI agents. Covers structured logging, metrics, distributed tracing, SLOs, alerting, incident response, post-mortems, on-call, chaos engineering, and cost optimization. ## Install ```bash clawhub install afrexai-observability-engine ``` ## What's Inside A 12-phase system covering the full observability lifecycle: 1. **Structured Logging** — JSON schema, PII scrubbing, log level decision tree, setup by language (Node/Python/Go) 2. **Metrics Collection** — RED + USE methods, naming conventions, label design, instrumentation checklist 3. **Distributed Tracing** — OpenTelemetry setup, sampling strategies, context propagation 4. **SLOs & Error Budgets** — SLI selection, target setting, burn-rate alerts, weekly tracking 5. **Alert Design** — Severity levels, anti-patterns, Prometheus templates, runbook templates 6. **Dashboard Architecture** — 4-level hierarchy (executive → infrastructure), panel specs 7. **Incident Response** — Severity classification, roles, step-by-step workflow, channel templates 8. **Post-Mortem Framework** — Blameless template, 5 Whys, meeting agenda 9. **On-Call Operations** — Rotation design, health metrics, weekly review 10. **Chaos Engineering** — Experiment templates, maturity levels, abort conditions 11. **Cost Optimization** — Cost driver ranking, reduction checklist, monthly review 12. **Advanced Patterns** — Correlation, synthetic monitoring, feature flag observability, maturity model Plus: /16 health check, 100-point quality rubric, 10 commandments, 12 natural language commands. ## Quick Start Tell your agent: > "Audit our observability posture" Or: > "Design SLOs for our payment API" ## ⚡ Level Up For industry-specific observability patterns, check out [AfrexAI Context Packs ($47)](https://afrexai-cto.github.io/context-packs/) — SaaS, Fintech, Healthcare, and 7 more verticals. ### More Free Skills by AfrexAI - `afrexai-devops-engine...
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