agent-observability

agent-observability is a skill for Claude Code from iradoweck/antigravity-awesome-skills. It costs 26 tokens per session (2,735 once invoked), scanned A, a copy of agent-observability, MIT.

A monitoring setup for AI agents that records traces, timing, token usage, and cost. Traces show the steps an agent took while handling a request.

In plain words
What is it for?
Use it to track agent and model-call latency, tool-call timing, token and cost usage, logs, and reliability targets.
Why use it?
It helps explain slow, expensive, or unreliable agent runs and gives teams data for debugging and service monitoring.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agentic-awesome-skills plugin — 243 skills shipped together

Good fit Use it to track agent and model-call latency, tool-call timing, token and cost usage, logs, and reliability targets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/iradoweck/antigravity-awesome-skills/agent-observability
Install

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.

Any agent
npx skills add iradoweck/antigravity-awesome-skills --skill agent-observability
Clone the repo
git clone --depth 1 https://github.com/iradoweck/antigravity-awesome-skills

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 243 skills.

Wrote 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.

agentmods badge for agent-observability

README.md
[![agentmods](https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/agent-observability/github.svg)](https://agentmods.dev/skills/iradoweck/antigravity-awesome-skills/agent-observability)
Your own site
<a href="https://agentmods.dev/skills/iradoweck/antigravity-awesome-skills/agent-observability"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/agent-observability/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.

agentmods 80×15 button for agent-observability

Your own site · 80×15
<a href="https://agentmods.dev/skills/iradoweck/antigravity-awesome-skills/agent-observability"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/agent-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,735 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.02735
Opus 5.5 $0.00010 $0.01094
Sonnet 5.5 $0.00005 $0.00547
Haiku 4.5 $0.00003 $0.00274

Measured 24d ago against content hash 08958fb3a7be, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 24d 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.

Origin

This is a copy

100% identical to agent-observability — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agentic-awesome-skills-claude/skills/agent-observability/SKILL.md · 347 lines

How it starts

The opening of the file, as written. The whole thing — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Observability

Monitor AI agent behavior with logs, traces, metrics, and cost telemetry. This skill covers the full observability stack for LLM-powered applications: from raw Prometheus counters to Grafana dashboards, OpenTelemetry tracing, structured logging, cost tracking, SLO definition, and PII redaction.


Core Metrics

Define these metrics at the application layer. All examples use the Prometheus client library naming conventions.

Latency

from prometheus_client import Histogram

# Total end-to-end latency for a full agent turn (user prompt -> final response)
AGENT_LATENCY = Histogram(
    "agent_request_duration_seconds",
    "End-to-end latency of an agent request",
    labelnames=["agent_name", "model", "status"],
    buckets=(0.25, 0.5, 1, 2, 5, 10, 30, 60, 120),
)

# Latency of a single LLM API call (one completion request)
LLM_CALL_LATENCY = Histogram(
    "llm_call_duration_seconds",
    "Latency of an individual LLM API call",
    labelnames=["model", "provider", "stream"],
    buckets=(0.1, 0.25, 0.5, 1, 2, 5, 10, 30),
)

# Latency of tool/function calls executed by the agent
TOOL_CALL_LATENCY = Histogram(
    "agent_tool_call_duration_seconds",
    "Latency of a tool call executed by the agent",
    labelnames=["tool_name", "agent_name", "status"],
    buckets=(0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10),
)

Token Usage

from prometheus_client import Counter, Histogram

PROMPT_TOKENS = Counter(
    "llm_prompt_tokens_total",
    "Total prompt tokens sent to the model",
    labelnames=["model", "agent_name"],
)

COMPLETION_TOKENS = Counter(
    "llm_completion_tokens_total",
    "Total completion tokens received from the model",
    labelnames=["model", "agent_name"],
)

CACHED_TOKENS = Counter(
    "llm_cached_tokens_total",
    "Prompt tokens served from KV-cache (provider-reported)",
    labelnames=["model", "agent_name"],
)

TOKENS_PER_REQUEST = Histogram(
    "llm_tokens_per_request",
    "Total tokens (prompt + completion) per request",
    labelnames=["model", "agent_name"],
    buckets=(100, 500, 1000, 2000, 4000, 8000, 16000, 32000, 64000, 128000),
)

Read the full file on GitHub · 347 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 24d ago First seen · 347 lines · 26 tokens per session scan A 08958fb3a7be

Subscribe to this mod's changes

agent-observability is a skill published in the GitHub repository iradoweck/antigravity-awesome-skills (30 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 2,735 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-observability, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

debug-optimize-lcp

Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…

ChromeDevTools/chrome-devtools-mcp · 99 tokens

systematic-debugging

Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.

open-metadata/OpenMetadata · 37 tokens

diagnose

Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.

emdash-cms/emdash · 43 tokens

repro-admin

Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.

emdash-cms/emdash · 48 tokens

log-error-digest

Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…

zebbern/claude-code-guide · 71 tokens

deck-retro

Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…

asheshgoplani/agent-deck · 86 tokens