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log-management-specialist

Use when designing or reviewing logging pipelines, structured logging, log aggregation (ELK, Loki, CloudWatch), retention policies, PII scrubbing, alerting on…

Skill by giggsoinc

IndexedMIT★ 5
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/giggsoinc-log-management-specialist

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Use when designing or reviewing logging pipelines, structured logging, log aggregation (ELK, Loki, CloudWatch), retention policies, PII scrubbing, alerting on log patterns, or distributed tracing integration. Expert-level guidance on observability discipline.

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/log-management-specialist

Tools it pre-approves 3

From allowed-tools in SKILL.md: the agent may use these without asking you each time.

  • Read,
  • Bash,
  • Grep

What's inside

Skill name
log-management-specialist
Description
Use when designing or reviewing logging pipelines, structured logging, log aggregation (ELK, Loki, CloudWatch), retention policies, PII scrubbing, alerting on log patterns, or distributed tracing integration. Expert-level guidance on observability discipline.
License
MIT
Allowed tools
Read,, Bash,, Grep
Instructions
278 lines in SKILL.md · view on GitHub

Before installing

  • Pre-approves some shell commands

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Log Management Specialist

Domain: Observability · Structured Logging · Log Aggregation · Distributed Tracing
Expert model: Charity Majors (Honeycomb) — production observability, high-cardinality telemetry, SRE discipline

---

## Pre-flight Check

Before advising, confirm:

1. **Stack declared?** → check `.raven/manifest.json` for `stack`
2. **Current logging approach?** → ask if not obvious from code
3. **Log destination?** → CloudWatch / Elasticsearch / Loki / Splunk / stdout
4. **Retention requirements?** → compliance, cost, debug window
5. **PII risk?** → user data, emails, tokens in log fields

---

## Core Principles

```
1. Structured logs ONLY — JSON lines, not free-text strings
2. One log entry per request/event — no chatty loops
3. Correlation IDs on every log — trace_id, request_id, session_id
4. Never log secrets — API keys, passwords, tokens → [REDACTED]
5. Never log raw PII — emails, phone numbers → hash or mask
6. Log at the right level — DEBUG off in prod, WARN/ERROR always on
7. Fail-open — log pipeline failure must not crash the application
```

---

## Log Levels — When to Use Each

| Level | Use case | Prod enabled? |
|---|---|---|
| `DEBUG` | Internal state, variable dumps, loop tracing | ❌ Never |
| `INFO` | Business events, request start/end, key decisions | ✅ Yes |
| `WARNING` | Degraded state, retries, expected failures | ✅ Yes |
| `ERROR` | Unexpected failure, caught exception with stack | ✅ Yes |
| `CRITICAL` | System can't continue, data loss risk | ✅ Yes — page immediately |

**Rule:** If you're logging in a loop → wrong level or wrong design.

---

## Structured Log Format

Every log line must be a JSON object:

```json
{
  "timestamp": "2026-05-14T12:00:00.000Z",
  "level": "INFO",
  "service": "auth-service",
  "trace_id": "abc123",
  "request_id": "req-456",
  "user_id": "u-789",
  "event": "login.success",
  "duration_ms": 42,
  "message": "User authenticated via OAuth"
}
```

**Never this:**
```python
logger.info(f"User {email} logged in at {time}")  # ❌ free-text, PII in string
```

**Always this:**
```python
logger.info("login.success", extra={
    "user_id": user.id,          # ✅ ID, not email
    "duration_ms": elapsed_ms,
    "provider": "oauth",
})
```

---

## Python — Canonical Setup

```python
import logging
import json
import sys
from pythonjsonlogger import jsonlogger

def get_logger(name: str) -> logging.Logger:
    logger = logging.getLogger(name)
    handler = logging.StreamHandler(sys.stdout)
    formatter = jsonlogger.JsonFormatter(
        fmt="%(asctime)s %(name)s %(levelname)s %(message)s",
        datefmt="%Y-%m-%dT%H:%M:%S"
    )
    handler.setFormatter(formatter)
    logger.addHandler(handler)
    logger.setLevel(logging.INFO)
    return logger
```

Library: `python-json-logger` (approved in Raven manifest before use)

---

## Correlation IDs — Mandatory Pattern

Every request must carry a trace ID through all log lines:

```python
import uuid
from contextvars import ContextVar

_trace_id: ContextVar[str] = ContextVar("trace_id", default="")

def set_trace_id(tid: str | None = None) -> str:
    tid = tid or str(uuid.uuid4())
    _trace_id.set(tid)

From giggsoinc/raven, MIT.

More from giggsoinc/raven

Signals

Stars
5
Forks
5
Open issues
0
Last commit (this folder)
May 15, 2026 (5 months ago)
Last release
Jun 7, 2026 (4 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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log-management-specialist skill. Agent Mart. https://agentmart.dev/skills/giggsoinc-log-management-specialist (last updated 2026-10-24)