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
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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.
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About
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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.
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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
- Repository
- github.com/giggsoinc/raven
- Website
- github.com/giggsoinc/raven
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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log-management-specialist skill. Agent Mart. https://agentmart.dev/skills/giggsoinc-log-management-specialist (last updated 2026-10-24)