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 skills add xobotyi/cc-foundry --skill observabilitygit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/observability)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/observability"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/observability/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/skills/xobotyi/cc-foundry/observability"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 333 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00056 | $0.05037 |
| Opus 5.5 | $0.00022 | $0.02015 |
| Sonnet 5.5 | $0.00011 | $0.01007 |
| Haiku 4.5 | $0.00006 | $0.00504 |
Grade A, and why
observability 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 1mo 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 — 530 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability
If you cannot ask arbitrary questions about your system's behavior from the outside, your system is not observable — it is merely monitored.
The Three Pillars
Logs — What Happened
Logs are timestamped, discrete event records. They capture what happened at a specific moment: an error thrown, a user action, a configuration loaded, a connection refused.
Use logs when you need:
- Rich diagnostic context for a specific event
- Debugging information with full error details and stack traces
- Audit trails of who did what and when
- Record of discrete state transitions
Logs are poor at:
- Showing aggregate system health (use metrics)
- Tracing request flow across services (use traces)
- High-frequency numeric trends (too expensive at volume)
Metrics — How Is It Doing
Metrics are numeric measurements aggregated over time. They capture how the system is performing as quantitative time series: request rates, error percentages, latencies, queue depths, resource utilization.
Use metrics when you need:
- Real-time health signals and alerting
- Trend analysis over hours, days, weeks
- Capacity planning and saturation monitoring
- Pre-aggregated data that scales cheaply regardless of traffic
Metrics are poor at:
- Explaining why something is broken (use logs)
- Showing the path of a single request (use traces)
- Storing per-event detail (cardinality explosion)
Traces — How Did It Flow
Traces record the causal chain of operations that make up a single request as it propagates through distributed components. A trace is a tree of spans, where each span represents one unit of work (an HTTP call, a database query, a queue publish).
Use traces when you need:
- End-to-end latency breakdown across services
- Dependency mapping and bottleneck identification
- Understanding the path a failing request took
- Correlating work across process and network boundaries
Traces are poor at:
- Aggregate health monitoring (use metrics)
- Detailed per-event diagnostics on a single node (use logs)
- Cheap, long-term trend storage (traces are expensive at 100% sampling)
What ships with it
1 file 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.
- 1mo ago First seen · 530 lines · 56 tokens per session scan A 3d1afe33ecc0
observability is a skill published in the GitHub repository xobotyi/cc-foundry (22 stars, last pushed 7d ago), licensed MIT. It adds 56 tokens to every session and 5,037 once invoked, about $0.0002 per session on Opus 5.5. 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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