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/proyecto26/system-design-skills/distributed-loggingnpx skills add proyecto26/system-design-skills --skill distributed-logginggit clone --depth 1 https://github.com/proyecto26/system-design-skillsWrote 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/proyecto26/system-design-skills/distributed-logging)<a href="https://agentmods.dev/skills/proyecto26/system-design-skills/distributed-logging"><img src="https://agentmods.dev/badge/skills/proyecto26/system-design-skills/distributed-logging.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 5.1 | $0.00124 | $0.02845 |
| Opus 5 | $0.00062 | $0.01422 |
| Sonnet 5 | $0.00025 | $0.00569 |
| Haiku 4.5 | $0.00012 | $0.00284 |
Grade A, and why
distributed-logging 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 6d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Distributed logging
Move logs from thousands of processes into one searchable place, fast enough to debug a live incident and cheap enough to keep for months. Getting it wrong is a classic "ignore failure" miss: the logging pipeline is itself a distributed system that buckles under the exact traffic spike you most need it during, and a naive design either drops the evidence or takes down the app it instruments.
When to reach for this
More than one process emits logs and someone needs to search them together; an
incident requires correlating a request across services; log volume has outgrown
grep on a box; or compliance demands retention. The pipeline buys central search,
cross-service correlation, and a durable record decoupled from any single host.
When NOT to
A single service on one host where journald + log rotation is enough — a full
pipeline is pure operational overhead (YAGNI). Numeric time-series questions ("what
is p99 latency", "is error rate up") belong to metrics, not log scans — that is
observability's job; logs answer "what exactly happened to this request". Don't
ship every debug line at full volume before a number shows the volume justifies the
cost; sample first.
Clarify first
- Volume and peak — lines/sec and bytes/sec, average and peak (→
back-of-the-envelope). This sizes every stage. - Structured or free-text — can producers emit JSON now, or is there legacy text to parse?
- Query latency need — interactive search in seconds (hot index) vs. occasional forensic/audit reads (cold archive)?
- Retention + compliance — how long hot, how long cold, any legal hold or PII redaction requirement?
- Loss tolerance — may logs be dropped/sampled under overload, or is every line evidence (audit/financial)?
The options
Collection (agent on the host)
- Sidecar/node agent (Fluentd, Fluent Bit, Vector, Filebeat): tails files or reads stdout, adds metadata, ships out. Use when apps log to files/stdout and you want app code untouched — the default.
- Direct-to-bus SDK: the app writes structured events straight to a transport. Use when you control the code and want exact structure, accepting tighter coupling.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 177 lines · 124 tokens per session scan A 3b0da1597223
distributed-logging is a skill published in the GitHub repository proyecto26/system-design-skills (69 stars, last pushed 3mo ago), licensed MIT. It adds 124 tokens to every session and 2,845 once invoked, about $0.0006 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.
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