Agent skill

Logging Architect

by FerroxLabs in FerroxLabs/wayland

Logging strategy designer covering structured logging, log levels, correlation IDs, distributed tracing, log aggregation, PII handling, retention policies, alerting, and observability stack setup.

Apache-2.0Auto-check passedDevOps & Cloud

Install Logging Architect

skills CLI
$ npx skills add FerroxLabs/wayland --skill logging-architect -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install FerroxLabs/wayland logging-architect --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/FerroxLabs/wayland.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/software-engineering/logging-architect .claude/skills/logging-architect && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
logging-architect
GitHub stars
608
Token cost
~3.8k tokens
SKILL.md length
1,254 words
Files
1
Skills in repo
1,194
Repo updated
First seen
Licence
Apache-2.0

At a glance

Logging strategy designer covering structured logging, log levels, correlation IDs, distributed tracing, log aggregation, PII handling, retention policies, alerting, and observability stack setup.

  • Works in 5 steps: Generate a correlation ID at the system… → Propagate it in HTTP headers:… → Include it in every log line within the… → …
  • The user asks about logging architect
  • SKILL.md covers Structured Logging, Log Levels, Correlation IDs and Distributed Tracing, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Logging Architect is an agent skill from FerroxLabs/wayland. Logging strategy designer covering structured logging, log levels, correlation IDs, distributed tracing, log aggregation, PII handling, retention policies, alerting, and observability stack setup. Use when the user asks about logging architect, logging architect best practices, or needs guidance on logging architect implementation. Do NOT use when the user needs a different specialized skill or is asking about an unrelated technology domain.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Observability. It works with Node.js. The repository describes itself as: Wayland - The AI Agent That Perceives. Reasons. Acts. Evolves. The licence is Apache-2.0.

When your agent uses it

  • The user asks about logging architect
  • Logging architect best practices
  • Needs guidance on logging architect implementation
  • The user needs a different specialized skill

Example prompts

  • “/logging-architect”

Requirements

  • Python 3
  • Node.js

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Generate a correlation ID at the system boundary (API gateway, load balancer, or first service).
  2. Propagate it in HTTP headers: X-Correlation-Id or X-Request-Id.
  3. Include it in every log line within the request lifecycle.
  4. Propagate it through async boundaries (message queues, event buses).
  5. Return it in error responses so users can reference it in support requests.

What it can do on your machine

Read from SKILL.md and the folder at commit 4c030c7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript, json, yaml and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Logging Architect loads about 3.8k tokens when it runs. Until then it costs about 116 tokens; SKILL.md has 1,254 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~116
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from FerroxLabs/wayland at commit 4c030c7, republished under its Apache-2.0 licence (© FerroxLabs). 1,254 words, ~3,846 tokens.

Download SKILL.mdSave it as .claude/skills/logging-architect/SKILL.md (or your agent's skills folder).
name
logging-architect
description
Logging strategy designer covering structured logging, log levels, correlation IDs, distributed tracing, log aggregation, PII handling, retention policies, alerting, and observability stack setup. Use when the user asks about logging architect, logging architect best practices, or needs guidance on logging architect implementation. Do NOT use when the user needs a different specialized skill or is asking about an unrelated technology domain.
license
Apache-2.0
metadata.author
foundry-skills
metadata.version
1.0.0
metadata.tags
best-practices devops guide
metadata.category
software-engineering
metadata.subcategory
developer-tools
metadata.disclaimer
none
metadata.difficulty
advanced

Logging Architect

You are an expert logging architect. Design logging systems that make production systems observable, debuggable, and auditable. Logs are the narrative of your system. Make them tell a coherent story.

Structured Logging

Why Structured Logging

Unstructured logs are for humans reading a terminal. Structured logs are for machines querying petabytes.

Unstructured (bad for production):

2024-01-15 14:23:45 ERROR Failed to process order #12345 for user john@example.com

Structured (good for production):

json
{
  "timestamp": "2024-01-15T14:23:45.123Z",
  "level": "error",
  "message": "Failed to process order",
  "service": "order-service",
  "orderId": "12345",
  "userId": "usr_abc123",
  "error": {
    "type": "PaymentDeclined",
    "code": "CARD_DECLINED",
    "message": "Insufficient funds"
  },
  "requestId": "req_xyz789",
  "traceId": "trace_def456",
  "duration_ms": 342
}
Structured Logging Libraries
LanguageLibraryFormat
Node.jspino, winstonJSON
Pythonstructlog, python-json-loggerJSON
Javalogback + Logstash encoder, log4j2 JSON layoutJSON
Gozerolog, zapJSON
Rusttracing + tracing-subscriberJSON
.NETSerilogJSON
Implementation Example (Node.js with Pino)
typescript
import pino from "pino";

const logger = pino({
  level: ENV_CONFIG_VALUE || "info",
  formatters: {
    level: (label) => ({ level: label }),
  },
  timestamp: pino.stdTimeFunctions.isoTime,
  redact: ["req.headers.authorization", "user.email", "user.phone"],
});

// Create child logger with request context
function createRequestLogger(req) {
  return logger.child({
    requestId: req.id,
    method: req.method,
    path: req.url,
    userId: req.user?.id,
  });
}

// Usage
const log = createRequestLogger(req);
log.info({ orderId: order.id }, "Order created successfully");
log.error({ err, orderId: order.id }, "Failed to process payment");

Log Levels

When to Use Each Level
LevelWhen to useExample
FATALSystem cannot continue. Requires immediate human attention.Database connection pool exhausted. TLS certificate expired.
ERROROperation failed. Requires investigation but system continues.Payment processing failed. External API returned 500.
WARNSomething unexpected happened but was handled. Potential issue.Retry succeeded after 2 attempts. Cache miss rate above threshold. Deprecated API called.
INFOSignificant business events. Normal operations.User registered. Order placed. Deployment started. Config loaded.
DEBUGDetailed technical information for troubleshooting.SQL query executed. HTTP request sent. Cache hit/miss. Function entry/exit.
TRACEVery detailed. Function parameters, loop iterations.Rarely used in production. Enabled temporarily for deep debugging.
Production Log Level Strategy
  • Default: INFO in production, DEBUG in staging.
  • Dynamic: Support changing log level at runtime via API or config without restart.
  • Per-component: Allow different levels per module (e.g., DEBUG for payment module, INFO for everything else).
  • Per-request: Allow DEBUG logging for specific request IDs or user IDs in production.
Log Level Decision Tree
Is the system about to crash or become unavailable?
  Yes -> FATAL

Did an operation fail with no automatic recovery?
  Yes -> ERROR

Did something unexpected happen but the system recovered?
  Yes -> WARN

Is this a normal business event that operators care about?
  Yes -> INFO

Is this technical detail needed only for troubleshooting?
  Yes -> DEBUG

Is this extremely granular, variable-level tracing?
  Yes -> TRACE

Correlation IDs

What They Are

A correlation ID (request ID) is a unique identifier that follows a request through all services, enabling end-to-end tracing.

Implementation
typescript
// Middleware to generate/propagate correlation ID
function correlationMiddleware(req, res, next) {
  const correlationId = req.headers["x-correlation-id"] || uuid();
  req.correlationId = correlationId;
  res.setHeader("x-correlation-id", correlationId);

  // Attach to all outgoing HTTP requests
  req.httpClient = HTTP client.create({
    headers: { "x-correlation-id": correlationId },
  });

  next();
}
Rules
  1. Generate a correlation ID at the system boundary (API gateway, load balancer, or first service).
  2. Propagate it in HTTP headers: X-Correlation-Id or X-Request-Id.
  3. Include it in every log line within the request lifecycle.
  4. Propagate it through async boundaries (message queues, event buses).
  5. Return it in error responses so users can reference it in support requests.

Distributed Tracing

OpenTelemetry Integration

Distributed tracing goes beyond correlation IDs by tracking parent-child relationships between operations.

Trace: req_abc123
  |
  +-- Span: API Gateway (12ms)
       |
       +-- Span: Auth Service - validateToken (3ms)
       |
       +-- Span: Order Service - createOrder (45ms)
            |
            +-- Span: Database - INSERT order (8ms)
            |
            +-- Span: Payment Service - charge (120ms)
            |    |
            |    +-- Span: Stripe API - POST /charges (95ms)
            |
            +-- Span: Email Service - sendConfirmation (15ms)
Key Concepts
ConceptDefinition
TraceEnd-to-end journey of a request
SpanA single operation within a trace
Trace IDUnique ID for the entire trace
Span IDUnique ID for a single span
Parent Span IDLinks child spans to parents
BaggageKey-value pairs propagated across service boundaries
Setup (Node.js with OpenTelemetry)
typescript
import { NodeSDK } from "@opentelemetry/sdk-node";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { getNodeAutoInstrumentations } from "@opentelemetry/auto-instrumentations-node";

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: "[reference URL]" }),
  instrumentations: [getNodeAutoInstrumentations()],
  serviceName: "order-service",
});
sdk.start();

Log Aggregation

Architecture
[Service A] --logs--> [Log Shipper] --> [Message Queue] --> [Log Processor] --> [Storage] --> [Query/UI]

Example stack:
[App] --> [Fluentd/Filebeat] --> [Kafka] --> [Logstash] --> [Elasticsearch] --> [Kibana]

Or:
[App] --> [Promtail] --> [Loki] --> [Grafana]

Or:
[App] --> [OTLP] --> [OpenTelemetry Collector] --> [Backend of choice]
Stack Comparison
StackProsConsBest for
ELK (Elasticsearch + Logstash + Kibana)Powerful full-text search, matureResource-heavy, expensive at scaleLarge organizations, complex queries
Loki + GrafanaLightweight, label-based, integrates with metricsLess powerful querying than ELKKubernetes-native, cost-conscious teams
Datadog / Splunk / New RelicManaged, integrated with metrics/tracesExpensive, vendor lock-inTeams that prefer SaaS
CloudWatch / StackdriverNative cloud integrationLimited cross-cloudSingle-cloud deployments
Log Shipping Best Practices
  1. Ship logs asynchronously. Never block the application on log delivery.
  2. Buffer locally. If the aggregator is down, queue logs on disk.
  3. Use a message queue (Kafka, Kinesis) between shippers and processors for decoupling.
  4. Parse and enrich at the processor, not the application (keep app-side logging simple).
  5. Index selectively. Not every field needs to be searchable. High-cardinality fields (user IDs) are expensive to index.

PII Handling in Logs

What is PII

Personally Identifiable Information: names, email addresses, phone numbers, IP addresses, SSNs, credit card numbers, addresses, dates of birth, health information, biometric data.

Strategies
StrategyHowWhen
RedactionReplace with [REDACTED]Sensitive fields known at log time
MaskingShow partial: john****@example.comNeed partial info for debugging
HashingSHA-256 of valueNeed to correlate across logs without exposing data
TokenizationReplace with opaque tokenNeed to look up original value in secure vault
OmissionDo not log the field at allTruly unnecessary data
Implementation (Pino Redaction)
typescript
const logger = pino({
  redact: {
    paths: [
      "user.email",
      "user.phone",
      "user.ssn",
      "req.headers.authorization",
      "req.headers.cookie",
      "payment.cardNumber",
    ],
    censor: "[REDACTED]",
  },
});
Compliance Requirements
RegulationLogging Implication
GDPRPII must be deletable; right to be skipped applies to logs
HIPAAHealth info must not appear in logs accessible to non-authorized personnel
PCI DSSCard numbers must never be logged, even partially (except last 4 digits)
SOC 2Audit logs must be tamper-evident and retained per policy

Log Retention Policies

Tiered Retention
TierRetentionStorageUse Case
Hot7-14 daysSSD, indexedActive debugging, real-time queries
Warm30-90 daysHDD, indexedRecent incident investigation
Cold1-7 yearsObject storage (S3), compressedCompliance, audit, legal
Archive7+ yearsGlacier/deep archiveRegulatory requirement
Show full SKILL.md (509 more words)Show less
Retention Decision Factors
  1. Regulatory requirements: Some industries mandate 7+ year retention.
  2. Incident response: Need enough history to investigate incidents (minimum 30 days).
  3. Cost: Storage and indexing costs grow linearly. Archive aggressively.
  4. Volume: High-volume services may need shorter hot retention.

Alerting from Logs

Alert Design Principles
  1. Every alert must be actionable. If there is nothing to do, it should not alert.
  2. Alert on symptoms, not causes. Alert on "error rate > 5%" not "database connection failed" (the latter is a cause that may or may not affect users).
  3. Use appropriate channels: Page for user-affecting issues. Slack/email for everything else.
  4. Tune thresholds: Start conservative, tighten over time. False alarms cause alert fatigue.
What to Alert On
ConditionSeverityChannel
Error rate > 5% for > 2 minutesCriticalPagerDuty
Error rate > 1% for > 10 minutesWarningSlack
Zero traffic for > 5 minutesCriticalPagerDuty
Log volume spike > 10x normalWarningSlack
FATAL log level emittedCriticalPagerDuty
Specific error code appearsVariesConfigurable
Alert Template
yaml
name: high-error-rate
description: Error rate exceeds 5% for order-service
query: |
  rate(log_entries{service="order-service", level="error"}[5m])
  / rate(log_entries{service="order-service"}[5m]) > 0.05
for: 2m
labels:
  severity: critical
  team: platform
annotations:
  summary: "High error rate in order-service ({{ $value | humanizePercentage }})"
  runbook: "[reference URL]"
  dashboard: "[reference URL]"

Standard Log Fields

Every log entry should include these fields for consistency across services:

FieldTypeRequiredExample
timestampISO 8601Yes2024-01-15T14:23:45.123Z
levelstringYesinfo, error, warn
messagestringYesHuman-readable description
servicestringYesorder-service
environmentstringYesproduction, staging
versionstringYes1.2.3 or git SHA
requestIdstringWhen applicablereq_abc123
traceIdstringWhen applicabletrace_def456
userIdstringWhen applicableusr_ghi789 (hashed if PII concern)
duration_msnumberFor operations342
error.*objectFor errors{ type, code, message, stack }

What NOT to Log

  1. Passwords, secrets, API keys, tokens.
  2. Full credit card numbers (log last 4 at most).
  3. Health records, SSNs, government IDs.
  4. Full request/response bodies (may contain PII; log selectively).
  5. High-frequency loop iterations (creates log storms).
  6. Successful health check responses (noise).
  7. Information already captured by tracing (do not duplicate span data in logs).

When to Use

Use this skill when:

  • Designing or implementing logging architect solutions
  • Reviewing or improving existing logging architect approaches
  • Making architectural or implementation decisions about logging architect
  • Learning logging architect patterns and best practices
  • Troubleshooting logging architect-related issues

Do NOT use this skill when:

  • The question is about a fundamentally different technology domain
  • A more specific sibling skill covers the exact topic needed
  • The user needs a complete hands-on tutorial rather than expert guidance

Output Format

markdown
# Logging Architect Analysis

## Context Assessment
[Situation summary and constraints]

## Recommended Approach
[Primary recommendation with rationale]

## Implementation Steps
1. [Step with specific details]
2. [Step with specific details]
3. [Step with specific details]

## Trade-offs and Considerations
- [Key trade-off 1]
- [Key trade-off 2]

## Next Steps
- [Immediate action item]
- [Follow-up action item]

Example

Input: "Help me implement logging architect for a medium-scale production application"

Output: A structured analysis covering current state assessment, recommended logging architect approach with specific patterns, implementation roadmap with milestones, and risk mitigation strategies tailored to the application scale and constraints.

Edge Cases

  • Legacy system integration: When logging architect must coexist with legacy approaches, provide a gradual migration path rather than a complete rewrite
  • Scale mismatch: When the solution complexity exceeds the project scale, recommend a simpler approach and note when to revisit
  • Team skill gaps: When the team lacks experience with the recommended approach, include learning resources and simpler alternatives
  • Conflicting requirements: When constraints conflict (e.g., performance vs. maintainability), explicitly state the trade-off and recommend based on stated priorities

© FerroxLabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in src/process/resources/skills-library/bodies/skills/software-engineering/logging-architect of FerroxLabs/wayland.

Open the folder on GitHubat commit 4c030c7

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Logging Architect next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Categories

Questions about Logging Architect

What does Logging Architect do?

Logging strategy designer covering structured logging, log levels, correlation IDs, distributed tracing, log aggregation, PII handling, retention policies, alerting, and observability stack setup. Logging Architect is an agent skill from FerroxLabs/wayland. Logging strategy designer covering structured logging, log levels, correlation IDs, distributed tracing, log aggregation, PII handling, retention policies, alerting, and observability stack setup.

When should I use Logging Architect?

Logging Architect fits situations like: the user asks about logging architect; logging architect best practices; needs guidance on logging architect implementation; the user needs a different specialized skill.

How do I install Logging Architect in Claude Code?

Run `npx skills add FerroxLabs/wayland --skill logging-architect -a claude-code`. Or copy the skill folder (src/process/resources/skills-library/bodies/skills/software-engineering/logging-architect in FerroxLabs/wayland) into .claude/skills/logging-architect in your project. Claude Code loads it when a task matches its description.

How do I install Logging Architect in Codex?

Run `npx skills add FerroxLabs/wayland --skill logging-architect -a codex`. Or copy the skill folder (src/process/resources/skills-library/bodies/skills/software-engineering/logging-architect in FerroxLabs/wayland) into .agents/skills/logging-architect in your project. Codex loads it when a task matches its description.

Can I use Logging Architect in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add FerroxLabs/wayland --skill logging-architect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/logging-architect, .gemini/skills/logging-architect, .github/skills/logging-architect and .opencode/skills/logging-architect in your project.

What does Logging Architect need to run?

SKILL.md names no scripts, command-line tools or credentials: Logging Architect is instructions for the agent only. Our summary lists: Python 3; Node.js.

Does Logging Architect access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Logging Architect safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Logging Architect use?

Logging Architect is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Logging Architect use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Logging Architect?

Skills that share tags, products or a category with Logging Architect: Workers Best Practices (hodgef/apiker, 127 stars), Nemo Relay Migrate From Flow (NVIDIA/NeMo-Relay, 190 stars), Monitoring Expert (Jeffallan/claude-skills, 12k stars) and Azure Monitor Opentelemetry TS (microsoft/skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Logging Architect?

FerroxLabs (a GitHub user) maintains it in FerroxLabs/wayland, which has 608 GitHub stars. The repository holds 1,194 skills in this directory. The repository was last updated on October 6, 2026.

Source: FerroxLabs/wayland on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.