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 skills/ancoleman/ai-design-components/implementing-observabilitynpx skills add ancoleman/ai-design-components --skill implementing-observabilitygit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/implementing-observability)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/implementing-observability"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/implementing-observability.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 | $0.00076 | $0.02961 |
| Opus 5 | $0.00038 | $0.01481 |
| Sonnet 5 | $0.00015 | $0.00592 |
| Haiku 4.5 | $0.00008 | $0.00296 |
Grade A, and why
implementing-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 4d 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 — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Observability with OpenTelemetry
Purpose
Implement production-grade observability using OpenTelemetry as the 2025 industry standard. Covers the three pillars (metrics, logs, traces), LGTM stack deployment, and critical log-trace correlation patterns.
When to Use
Use when:
- Building production systems requiring visibility into performance and errors
- Debugging distributed systems with multiple services
- Setting up monitoring, logging, or tracing infrastructure
- Implementing structured logging with trace correlation
- Configuring alerting rules for production systems
Skip if:
- Building proof-of-concept without production deployment
- System has < 100 requests/day (console logging may suffice)
The OpenTelemetry Standard (2025)
OpenTelemetry is the CNCF graduated project unifying observability:
┌────────────────────────────────────────────────────────┐
│ OpenTelemetry: The Unified Standard │
├────────────────────────────────────────────────────────┤
│ │
│ ONE SDK for ALL signals: │
│ ├── Metrics (Prometheus-compatible) │
│ ├── Logs (structured, correlated) │
│ ├── Traces (distributed, standardized) │
│ └── Context (propagates across services) │
│ │
│ Language SDKs: │
│ ├── Python: opentelemetry-api, opentelemetry-sdk │
│ ├── Rust: opentelemetry, tracing-opentelemetry │
│ ├── Go: go.opentelemetry.io/otel │
│ └── TypeScript: @opentelemetry/api │
│ │
│ Export to ANY backend: │
│ ├── LGTM Stack (Loki, Grafana, Tempo, Mimir) │
│ ├── Prometheus + Jaeger │
│ ├── Datadog, New Relic, Honeycomb (SaaS) │
│ └── Custom backends via OTLP protocol │
│ │
└────────────────────────────────────────────────────────┘
What ships with it
16 files 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.
- examples/axum-tracing/README.md 4.0 KB
- examples/fastapi-otel/main.py 7.2 KB runs code
- examples/fastapi-otel/README.md 6.2 KB
- examples/fastapi-otel/requirements.txt 399 B
- examples/grafana-dashboards/api-overview.json 26 KB
- examples/lgtm-docker-compose/docker-compose.yml 11 KB
- examples/lgtm-docker-compose/README.md 5.0 KB
- outputs.yaml 9.9 KB
- references/alerting-rules.md 17 KB
- references/lgtm-stack.md 20 KB
- references/opentelemetry-setup.md 14 KB
- references/structured-logging.md 17 KB
- references/trace-context.md 18 KB
- scripts/generate_dashboards.py 6.5 KB runs code
- scripts/setup_otel.py 13 KB runs code
- scripts/validate_metrics.py 5.2 KB runs code
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.
- 4d ago First seen · 360 lines · 76 tokens per session scan A d0e6ce98e0d0
implementing-observability is a skill published in the GitHub repository ancoleman/ai-design-components (517 stars, last pushed 8mo ago), licensed MIT. It adds 76 tokens to every session and 2,961 once invoked, about $0.0004 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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