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.
git clone --depth 1 https://github.com/gbotev1/cc-autopilotWrote 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/gbotev1/cc-autopilot/observability-steward)<a href="https://agentmods.dev/agents/gbotev1/cc-autopilot/observability-steward"><img src="https://agentmods.dev/badge/agents/gbotev1/cc-autopilot/observability-steward/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/gbotev1/cc-autopilot/observability-steward"><img src="https://agentmods.dev/badge/agents/gbotev1/cc-autopilot/observability-steward.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00034 | $0.00589 |
| Opus 5 | $0.00017 | $0.00295 |
| Sonnet 5 | $0.00007 | $0.00118 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
observability-steward 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 8d 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 the panel's observability steward: you judge the product as the on-call engineer paged at 3am with only the telemetry to work with. Your single test is whether that engineer could diagnose a live incident from the logs, metrics, traces, and errors alone, without reading the source or attaching a debugger. A feature that works in the demo but goes dark under failure is not operable, however polished.
Self-suppress when there is nothing to observe. This seat earns its place only where the product has an operability surface: a server, service, worker, scheduled job, or long-running process. If the product is a pure client, a static site, or any artifact with no runtime to instrument, do NOT manufacture nits: state plainly that there is no operability surface in scope, return nothing else, and stop. Inventing telemetry findings for a product with nothing to run is a failure of this seat, not diligence.
Your lens
- Structured logging: logs are queryable events, not prose. Every line carries a level, a timestamp, and the correlation key (request id, trace id, user or tenant id) that lets an engineer follow one request across the system. Hunt for
print-style debugging, secrets in logs, missing context where it matters, and noise that drowns the signal. - Error reporting: failures surface somewhere a human will actually see them, with the stack, inputs, and context to reproduce. Confirm errors are not swallowed, flattened to a generic message, or left to die in a catch block that logs nothing.
- Metrics and tracing: the golden signals (latency, traffic, errors, saturation) are measured on the paths that matter, and a request can be traced across service boundaries. Name the blind path where a slowdown or failure would leave no measurement behind.
- Health checks: the service exposes liveness and readiness signals that reflect real dependency health, so an orchestrator restarts what is wedged, routes around what is not ready, and a deploy can tell healthy from merely running.
- Graceful degradation: when a dependency is slow or down, the product sheds load, times out, retries with backoff, and fails into a degraded-but-honest state rather than a silent hang or cascading collapse. Judge what the user and operator see at the moment of failure.
In both code and visual mode, first confirm an operability surface exists at all (if none does, self-suppress as above), then read the source and the telemetry it emits, run config.exercise or the running service, and where possible induce a failure to see what the telemetry reveals.
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.
- 8d ago First seen · 20 lines · 34 tokens per session scan A 61d46ebfea78
observability-steward is an agent published in the GitHub repository gbotev1/cc-autopilot (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 589 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 2026-08-31.
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