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 skills add lingxling/awesome-skills-cn --skill agent-observabilitygit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/agent-observability)<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.
<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>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 | $0.00026 | $0.02735 |
| Opus 5.5 | $0.00010 | $0.01094 |
| Sonnet 5.5 | $0.00005 | $0.00547 |
| Haiku 4.5 | $0.00003 | $0.00274 |
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.
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.
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),
)
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.
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.
- 9d ago First seen · 347 lines · 26 tokens per session scan A 08958fb3a7be
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.
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