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/jnpiyush/agentx/agent-observabilitynpx skills add jnPiyush/AgentX --skill agent-observabilitygit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/agent-observability)<a href="https://agentmods.dev/skills/jnpiyush/agentx/agent-observability"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/agent-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 | $0.00087 | $0.01437 |
| Opus 5 | $0.00044 | $0.00718 |
| Sonnet 5 | $0.00017 | $0.00287 |
| Haiku 4.5 | $0.00009 | $0.00144 |
Grade A, and why
agent-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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observability
Purpose: Make every LLM call, tool call, and agent decision observable, debuggable, and cost-attributable.
When to Use This Skill
- Standing up an LLM-powered service in any environment beyond local dev
- Investigating regressions, latency spikes, or cost spikes
- Wiring evaluation scores into traces for replay
- Building a feedback loop from production data
- Debugging multi-agent handoffs (see
multi-agent-orchestration)
Three Pillars
| Pillar | What | Tools |
|---|---|---|
| Traces | Per-request span tree of model + tool calls | OpenTelemetry, Langfuse, LangSmith, Phoenix |
| Metrics | Aggregate latency, cost, error rate, eval scores | Prometheus, Azure Monitor, Datadog |
| Logs | Raw prompts, responses, tool args (PII-scrubbed) | Same backends + log store |
Use all three. Traces alone are not sufficient for SLO monitoring; metrics alone cannot debug a single bad response.
OpenTelemetry GenAI Semantic Conventions
The OTel GenAI semantic conventions are now stable. Use them.
Key span attributes:
| Attribute | Example |
|---|---|
gen_ai.system |
openai, anthropic, azure_ai_inference |
gen_ai.request.model |
gpt-5, claude-opus-4.8 |
gen_ai.response.model |
actual served model |
gen_ai.usage.input_tokens |
1234 |
gen_ai.usage.output_tokens |
456 |
gen_ai.request.temperature |
0.3 |
gen_ai.operation.name |
chat, tool_call, embeddings |
Span events:
gen_ai.system.message,gen_ai.user.message,gen_ai.assistant.message,gen_ai.tool.message- Capture content under a config flag (PII risk)
Trace Structure for an Agent Turn
Span: agent.turn (root)
+- Span: gen_ai.chat (model call)
+- Span: tool.execute (per parallel tool call)
| +- Span: db.query / http.request / fs.read (downstream)
+- Span: gen_ai.chat (post-tool reasoning)
+- Span: agent.handoff (if multi-agent)
Every span MUST carry: task_id, agent_name, turn_index, parent_span_id.
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 · 149 lines · 87 tokens per session scan A 9110b64f92de
agent-observability is a skill published in the GitHub repository jnPiyush/AgentX (15 stars, last pushed today), licensed Apache-2.0. It adds 87 tokens to every session and 1,437 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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