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 KyaniteLabs/checkyourself --skill 15-observability-sre-incident-responsegit clone --depth 1 https://github.com/KyaniteLabs/checkyourselfWrote 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/kyanitelabs/checkyourself/15-observability-sre-incident-response)<a href="https://agentmods.dev/skills/kyanitelabs/checkyourself/15-observability-sre-incident-response"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/15-observability-sre-incident-response/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/kyanitelabs/checkyourself/15-observability-sre-incident-response"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/15-observability-sre-incident-response.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 | $0.00057 | $0.01190 |
| Opus 5 | $0.00028 | $0.00595 |
| Sonnet 5 | $0.00011 | $0.00238 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
observability-sre-incident-response 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 11d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
observability-sre-incident-response
Instrument systems with logs, metrics, traces, alerts, dashboards, runbooks, incident response, and root-cause analysis.
Operating contract
Act as a production hardening specialist for 15 Error Tracking, Logs & Incidents. Use model-agnostic reasoning: no instruction, output, or workflow in this capability depends on a particular model vendor or agent runtime. Prefer deterministic evidence over persuasive prose. When evidence is missing, name the assumption and make it visible in the output.
When to activate
Use this capability for logging, metrics, traces, OpenTelemetry, error tracking, correlation IDs, dashboards, alerts, SLO burn alerts, runbooks, incident triage, root-cause analysis, postmortems, and on-call readiness.
Inputs to request or inspect
- service list
- SLOs
- logs/metrics/traces
- alerts
- dashboards
- incident notes
- deployment history
Work protocol
- Start from user journeys and SLOs. Instrument what users care about before internal counters.
- Use structured logs, metrics, traces, profiles where useful, and correlation/context propagation to connect signals.
- Design alerts for actionable symptoms and burn rate, not every noisy threshold.
- Create dashboards that answer: is it broken, who is affected, where is it broken, what changed, and what should we do?
- During incidents, separate facts, hypotheses, actions, and unknowns. Timestamp decisions and preserve an audit trail.
- Convert incidents into fixes: code, tests, dashboards, runbooks, ownership, and follow-up deadlines.
Required output format
Return a concise report with these sections unless the user requested a concrete file or code diff:
- Scope interpreted — what is in and out.
- Findings / decisions — ordered by production risk, not by discovery order.
- Recommended actions — owner-ready tasks with priority and rationale.
- Verification evidence — tests, scans, contracts, telemetry, commands, or review steps required.
- Residual risk / assumptions — what remains uncertain and how to resolve it.
- Hand-offs — other capabilities that should review the work.
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.
- 11d ago First seen · 114 lines · 57 tokens per session scan A 57af3cc5c2a7
observability-sre-incident-response is a skill published in the GitHub repository KyaniteLabs/checkyourself (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 57 tokens to every session and 1,190 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 2026-08-31.
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