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/aspectrr/deer/observability-llm-obsnpx skills add aspectrr/deer --skill observability-llm-obsgit clone --depth 1 https://github.com/aspectrr/deerWrote 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/aspectrr/deer/observability-llm-obs)<a href="https://agentmods.dev/skills/aspectrr/deer/observability-llm-obs"><img src="https://agentmods.dev/badge/skills/aspectrr/deer/observability-llm-obs.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 5.1 | $0.00052 | $0.00887 |
| Opus 5 | $0.00026 | $0.00443 |
| Sonnet 5 | $0.00010 | $0.00177 |
| Haiku 4.5 | $0.00005 | $0.00089 |
Grade A, and why
observability-llm-obs 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 5d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM and Agentic Observability
Monitor LLMs and agentic components using data ingested into Elastic. Focus on performance, cost/token utilization, response quality, and call chaining.
Where to look
- Trace data (APM / OTel):
traces*for LLM spans from OTel/EDOT instrumentations - Integration metrics/logs:
metrics*andlogs*from Elastic LLM integrations (OpenAI, Azure, Bedrock, Vertex AI) - Discover first: Use
GET _data_streamorGET traces*/_mappingto find available data
Data available
From traces (traces*)
| Purpose | Example attribute names (OTel GenAI) |
|---|---|
| Operation / provider | gen_ai.operation.name, gen_ai.provider.name |
| Model | gen_ai.request.model, gen_ai.response.model |
| Token usage | gen_ai.usage.input_tokens, gen_ai.usage.output_tokens |
| Errors | error.type |
Use duration and event.outcome for latency and success/failure. Use trace.id and parent/child relationships for call chaining analysis.
Use cases and query patterns
LLM performance
FROM traces*
| WHERE @timestamp >= "2025-03-01T00:00:00Z" AND @timestamp <= "2025-03-01T23:59:59Z"
AND span.attributes.gen_ai.provider.name IS NOT NULL
| STATS request_count = COUNT(*), failures = COUNT(*) WHERE event.outcome == "failure",
avg_duration_us = AVG(span.duration.us)
BY span.attributes.gen_ai.request.model
| EVAL error_rate = failures / request_count
| LIMIT 100
Token usage over time
FROM traces*
| WHERE @timestamp >= "2025-03-01T00:00:00Z" AND @timestamp <= "2025-03-01T23:59:59Z"
AND span.attributes.gen_ai.provider.name IS NOT NULL
| STATS input_tokens = SUM(span.attributes.gen_ai.usage.input_tokens),
output_tokens = SUM(span.attributes.gen_ai.usage.output_tokens)
BY BUCKET(@timestamp, 1 hour), span.attributes.gen_ai.request.model
| SORT @timestamp
| LIMIT 500
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.
- 5d ago First seen · 94 lines · 52 tokens per session scan A 5a35fa284182
observability-llm-obs is a skill published in the GitHub repository aspectrr/deer (404 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 887 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 2026-08-30.
Other skills, from other repositories
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
pdf-processing
Process and extract information from PDF documents. Use this skill when the user asks to read, analyze, or extract data from PDF files.
architecture-diagram
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
makefile-generation
Generates Makefiles with testing, linting, formatting, and automation targets. Use when starting a project or standardizing build automation.
port-mass-scan
Port scan /8-/24 with Masscan+RustScan and nmap banners.
create-agent-issue
Create a single high-quality GitHub issue in logancyang/obsidian-copilot-preview from a TODO item, design-doc punch list entry, or ad-hoc feature/bug request. Each invocation is exactly one issue end-to-end: investigate the surface in src/, draft against the standard, ensure labels exist, push via gh, and (if a source…