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 Owl-Listener/ai-design-skills --skill observability-designgit clone --depth 1 https://github.com/Owl-Listener/ai-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/owl-listener/ai-design-skills/observability-design)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/observability-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/observability-design/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/owl-listener/ai-design-skills/observability-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/observability-design.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 | $0.00018 | $0.00557 |
| Opus 5.5 | $0.00007 | $0.00223 |
| Sonnet 5.5 | $0.00004 | $0.00111 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
observability-design 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Design
You can't improve what you can't see. Observability design makes the internal workings of multi-agent systems visible — so designers can understand user experience problems, developers can debug failures, and teams can improve the system over time.
What to Make Observable
- Workflow execution: Which agents were involved, in what order, with what results
- Decision points: What decisions were made, what alternatives were considered, why one was chosen
- Handoff details: What context transferred between agents, was anything lost
- Timing: How long each agent took, where bottlenecks occur
- Failures: What failed, how it was recovered, what the user experienced
- Quality signals: Output quality scores, user satisfaction signals, task success markers
Observability for Different Audiences
For designers:
- User journey view: What did the user experience across the whole workflow?
- Pain point identification: Where did users struggle, abandon, or express frustration?
- Quality patterns: Which outputs are high and low quality, and why? For developers:
- Execution traces: Step-by-step log of agent actions
- Error logs: What failed and where
- Performance metrics: Latency, throughput, resource usage For product managers:
- Usage patterns: Which workflows are used most, which are abandoned
- Success metrics: Task completion rates, user satisfaction trends
- Cost analysis: Resource consumption per workflow For users (optional):
- Progress indicators: Where is the system in the workflow?
- Agent transparency: Which agent is handling their request?
- Audit trails: What the system did on their behalf
Designing Observability Interfaces
- Dashboards: Real-time and historical views of system health and performance
- Trace viewers: Detailed step-by-step views of individual workflow executions
- Alert systems: Notifications when metrics exceed thresholds
- Search and filter: Ability to find specific executions by criteria
- Comparison tools: Compare performance across time periods, versions, or cohorts
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 · 49 lines · 18 tokens per session scan A f3b48a79500a
observability-design is a skill published in the GitHub repository Owl-Listener/ai-design-skills (189 stars, last pushed 4mo ago), licensed MIT. It adds 18 tokens to every session and 557 once invoked, about $0.0001 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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