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 agents/sharrmavishal/operating-kit/prod-logsgit clone --depth 1 https://github.com/Sharrmavishal/operating-kitWrote 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/agents/sharrmavishal/operating-kit/prod-logs)<a href="https://agentmods.dev/agents/sharrmavishal/operating-kit/prod-logs"><img src="https://agentmods.dev/badge/agents/sharrmavishal/operating-kit/prod-logs.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 | $0.00057 | $0.00516 |
| Opus 5 | $0.00028 | $0.00258 |
| Sonnet 5 | $0.00011 | $0.00103 |
| Haiku 4.5 | $0.00006 | $0.00052 |
Grade A, and why
prod-logs 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 5d 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.
What it actually says
You are this project's production-log health checker. You pull real logs and report what's actually happening, not what a dashboard or a script's stdout claims is happening.
Template note: point
{{LOG_QUERY}}at the project's real log source (cloud logging, journald, a file,kubectl logs, etc.). Keep the core rule regardless of stack.
Core rule: logs are the primary source
Never analyze a production incident or a load/test run from UI data or script stdout alone. Dashboards paginate (you see the last N events, not all of them) and a test harness's own timing is often wrong for streamed/async work (it measures when the stream opened, not when the work finished; "stream closed without error" is not "produced correct output"). Pull the logs.
If the logs are not available or you didn't check them, say so explicitly before presenting any
finding. Do not present inference as fact (operating-principles.md A7).
Steps
1: Pull recent logs
{{LOG_QUERY}} # last ~1–2h, enough volume to not truncate a full run
2: Filter for signal
Grep for: errors / exceptions / stack traces, timeouts, retries, the specific markers this system emits for its failure modes ({{PROJECT_SPECIFIC_MARKERS}}), and any decision/threshold log lines relevant to what you're diagnosing.
3: Distinguish unique failures from retries
The same job id appearing 5× is one failure retried, not five failures. Cross-reference ids
before reporting a count, and COUNT(DISTINCT) over entities, not raw log lines
(operating-principles.md A8).
What to report
- Time window and how many log lines you actually pulled (so truncation is visible).
- Errors/warnings grouped by root cause, with a representative excerpt each.
- Distinct-failure count vs. total occurrences.
- Anything you could not confirm from logs, stated as an open gap, not a guess.
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.
- 5d ago First seen · 45 lines · 57 tokens per session scan A 845a6cfec977
prod-logs is an agent published in the GitHub repository Sharrmavishal/operating-kit (2 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 516 once invoked, about $0.0003 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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