agent-observability

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

An observability setup for AI agents: recording traces, logs, response times, token use, and costs. Observability means collecting information that shows what a system did and where it spent time or money.

In plain words
What is it for?
Use it to measure full agent requests, individual model calls, and tool calls. It can support Prometheus metrics, Grafana dashboards, OpenTelemetry traces, structured logs, cost tracking, and privacy-aware telemetry.
Why use it?
It makes slow, expensive, or failing agent behavior easier to find instead of relying on guesswork. It also supports reliability targets and removal of personal data from recorded information.

Skill for Claude Code

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

Part of the agentic-awesome-skills plugin — 174 skills shipped together

Good fit Use it to measure full agent requests, individual model calls, and tool calls. It can support Prometheus metrics, Grafana dashboards, OpenTelemetry traces, structured logs, cost tracking, and privacy-aware telemetry.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lingxling/awesome-skills-cn/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 lingxling/awesome-skills-cn --skill agent-observability
Clone the repo
git clone --depth 1 https://github.com/lingxling/awesome-skills-cn

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 174 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/lingxling/awesome-skills-cn/agent-observability/github.svg)](https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-observability)
Your own site
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agent-observability"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/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/lingxling/awesome-skills-cn/agent-observability"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/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 9d 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 9d 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.

antigravity-awesome-skills/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. 9d 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 lingxling/awesome-skills-cn (310 stars, last pushed 9d ago), 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

mm-doctor

A diagnostic workflow for checking the health of the mm system, including its configuration, project links, note vault, passports, session archive, and planning consistency.

mworldorg/markdown-memory · 126 tokens

inversion

"Invert, always invert." Apply Carl Jacobi's mathematical principle and Charlie Munger's investing wisdom to solve problems by thinking backward from failure. Use when: Goal setting - Define what would guarantee failure, then avoid it; Risk analysis - Identify what could destroy your project before starting; Decision…

guia-matthieu/clawfu-skills · 96 tokens

deliverability-checker

Check email deliverability and DNS configuration. Use when: diagnosing email delivery issues; setting up new email domains; auditing email authentication; checking SPF/DKIM/DMARC; troubleshooting spam folder issues.

guia-matthieu/clawfu-skills · 44 tokens

rlm

Process large codebases (>100 files) using the Recursive Language Model pattern. Orchestrates parallel sub-agents to map-reduce across files without context rot. Use when: analyzing large repositories; auditing security or auth across many files; finding patterns across 50+ files; processing large log files or data…

guia-matthieu/clawfu-skills · 65 tokens

link-checker

Find broken links on websites. Use when: auditing website for broken links; checking internal link structure; finding 404 errors; validating external links; pre-launch QA.

guia-matthieu/clawfu-skills · 36 tokens

doubt-driven-development

Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…

addyosmani/agent-skills · 96 tokens