Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sawrus/agent-guides/log-aggregationnpx skills add sawrus/agent-guides --skill log-aggregationgit clone --depth 1 https://github.com/sawrus/agent-guidesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sawrus/agent-guides/log-aggregation)<a href="https://agentmods.dev/skills/sawrus/agent-guides/log-aggregation"><img src="https://agentmods.dev/badge/skills/sawrus/agent-guides/log-aggregation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00030 | $0.01058 |
| Opus 5 | $0.00015 | $0.00529 |
| Sonnet 5 | $0.00006 | $0.00212 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
log-aggregation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Log Aggregation
Expertise: Loki (Grafana stack), Promtail/Fluent Bit, structured JSON logging, log-based alerting, ELK basics.
When to load
When setting up log collection, writing log queries, debugging missing logs, or adding log-based alerts.
Loki Stack (K8s — recommended)
# Promtail DaemonSet auto-discovers K8s pod logs
# Install via helm:
helm upgrade --install loki grafana/loki-stack \
-n monitoring \
-f loki-values.yaml
# loki-values.yaml
loki:
auth_enabled: false
limits_config:
retention_period: 720h # 30 days
ingestion_rate_mb: 16
max_streams_per_user: 10000
storage_config:
boltdb_shipper:
active_index_directory: /data/loki/boltdb-index
filesystem:
directory: /data/loki/chunks
promtail:
config:
clients:
- url: http://loki:3100/loki/api/v1/push
scrape_configs:
- job_name: kubernetes-pods
kubernetes_sd_configs:
- role: pod
pipeline_stages:
- docker: {} # parse Docker JSON log format
- json: # extract fields from app JSON logs
expressions:
level: level
trace_id: trace_id
service: service
- labels:
level:
service:
LogQL Queries
# All error logs from a service in last 5 min
{namespace="production", app="order-service"} |= "ERROR"
# Parse JSON and filter by field
{namespace="production"} | json | level="error" | trace_id != ""
# Count errors per service (for alerting)
sum by (service) (
count_over_time({namespace="production"} | json | level="error" [5m])
)
# Log rate (to detect log explosion)
sum(rate({namespace="production"}[5m])) by (app)
# Find slow requests from logs
{app="api-gateway"} | json | response_time_ms > 500
Structured Logging Standards
# Python — structlog
import structlog
log = structlog.get_logger()
# Always include: service, version, trace_id, span_id, level
log.info("order.created",
order_id="ord-123",
user_id="usr-456", # OK in log; NOT in metrics labels
amount_cents=4999,
# trace_id injected automatically via TraceContextFilter
)
# Output (JSON):
# {"event": "order.created", "level": "info", "order_id": "ord-123",
# "trace_id": "abc123def456", "span_id": "789xyz", "timestamp": "..."}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 160 lines · 30 tokens per session scan A 472fd057770c
log-aggregation is a skill published in the GitHub repository sawrus/agent-guides (17 stars, last pushed 4d ago), licensed MIT. It adds 30 tokens to every session and 1,058 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
documentation-and-adrs
Records decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
agentmail
Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.
luna
Reviews code for objective correctness, security, and reliability.
agent-self-scheduling
Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
max
Cleans up and improves existing code without changing behavior.