instrument-llm-observability SKILL.md skillA
instrument-llm-observability from contextjet-ai/awesome-llm-observability is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add contextjet-ai/awesome-llm-observability/instrument-llm-observability@git:20260705.23078bePins git:20260705.23078be in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- contextjet-ai/awesome-llm-observability
- Path
skills/instrument-llm-observability/SKILL.md- Versions recorded
- 2
- Latest version
- git:20260705.23078be, committed Jul 5, 2026
- First version recorded
- Jul 5, 2026
- Size
- 3,582 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this when adding tracing/observability to an LLM or AI-agent application - capturing prompts, tool calls, token usage, latency, and cost per step. Trigger whenever someone wants to "add tracing", "instrument", "monitor", "see what my agent is doing", or debug an LLM app in production. Prefer vendor-neutral OpenTelemetry unless a specific platform is already in use.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Instrument an LLM app for observability Add production-grade tracing to an LLM/agent app so every run is inspectable: prompts, tool calls, retrievals, token usage, latency, and cost per span. ## Decide the approach first (2 questions) 1. **Is a backend already chosen?** (Langfuse, Arize Phoenix, Comet Opik, Datadog, Grafana…). If yes, use its SDK/auto-instrumentation. If no, default to **OpenTelemetry GenAI semantic conventions** so traces export to *any* OTel backend and you avoid lock-in. 2. **Lowest-friction path available?** If the app calls LLMs through a **gateway/proxy** (LiteLLM, Helicone, Portkey), you may get tracing + cost with *zero code* by changing the base URL. Check that before adding an SDK. ## Vendor-neutral path (recommended default): OpenTelemetry Emit `gen_ai.*` spans via an OTel instrumentation library, then point at any collector. - Use **OpenLLMetry** (`traceloop-sdk`) or **OpenInference** (Arize) - both emit OTel-compatible LLM spans. - Minimal shape: 1. Install the instrumentation lib + an OTLP exporter. 2. Initialize once at startup (set service name + OTLP endpoint via `OTEL_EXPORTER_OTLP_ENDPOINT`). …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/instrument-llm-observability@sha256:44496e083bae81caThe line at the top of this page pins the label git:20260705.23078be, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_zfjztwujoud6dtug)
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Versions
The registry has recorded 2 versions of this file, all committed on Jul 5, 2026.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260705.23078be latest | 2026-07-05 | 23078be | 3,582 B | A | view · diff |
| git:20260705.23977fd | 2026-07-05 | 23977fd | 3,592 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3582 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
contextjet-ai/awesome-llm-observability · 45 stars · license a license file GitHub does not identify · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_zfjztwujoud6dtug GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/instrument-llm-observability GET https://markdownregistry.com/api/v1/blob/44496e083bae81ca27e4e5b6b2e3f383d22088c1f50bb2cbbbc871f42ba7b5a5
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.