agent-observability-session-classify

agent-observability-session-classify is a skill for Claude Code, Codex from datadog-labs/agent-skills. It costs 136 tokens per session (16,126 once invoked), scanned A, original, MIT.

A tool for judging whether a user’s request was satisfied in Datadog Agent Observability records. Datadog is a monitoring service, and these records can describe an assistant session, a trace of one operation, or a group of sessions and traces.

In plain words
What is it for?
Use it to assess one assistant session with user-experience data, one trace without that data, or sampled groups of Datadog sessions and traces.
Why use it?
It provides a defined way to classify the result of an assistant interaction instead of relying on an informal reading of the logs. It supports single-session, single-trace, and multi-record analysis modes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 165repo +3 12d ago A scan Socket: passSnyk: passSkillSpector: warn 136 tokens original MIT

Good fit Use it to assess one assistant session with user-experience data, one trace without that data, or sampled groups of Datadog sessions and traces.

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Install with agentmods
npx agentmods add skills/datadog-labs/agent-skills/agent-observability-session-classify
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 datadog-labs/agent-skills --skill agent-observability-session-classify
Clone the repo
git clone --depth 1 https://github.com/datadog-labs/agent-skills

Made for: Claude Code, Codex.

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-session-classify

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-session-classify.svg)](https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-session-classify)
Your own site
<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-session-classify"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-session-classify.svg" alt="Measured on agentmods" height="20"></a>
Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 16,126 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 19 Jun 2026
  • Snyk pass 19 Jun 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Memory Poisoning · line 125
    Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.
    Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
How audits are shown
Origin original No closer match found 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 $0.00136 $0.16126
Opus 5 $0.00068 $0.08063
Sonnet 5 $0.00027 $0.03225
Haiku 4.5 $0.00014 $0.01613

Measured 8d ago against content hash 375fc3c0a1d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

agent-observability-session-classify scanned grade A with 1 finding 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 8d 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

res = subprocess.run([
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

agent-observability/agent-observability-session-classify/SKILL.md · 1,154 lines

How it starts

The opening of the file, as written. The whole thing — 1,154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Backend

Detection — At the start of every invocation, before taking any action, determine which backend to use:

  1. If the user passed --backend pup anywhere in their invocation → use pup mode immediately, regardless of whether MCP tools are present. Skip steps 2–4.
  2. Check whether MCP tools are present in your active tool list. The canonical signal is whether mcp__datadog-llmo-mcp__search_llmobs_spans appears in your available tools.
  3. If MCP tools are present → use MCP mode throughout. Call MCP tools exactly as named in this skill's workflow sections.
  4. If MCP tools are absent → check whether pup is executable: run pup --version via Bash. A JSON response containing "version" confirms pup is available.
  5. If pup responds → use pup mode throughout. Translate every MCP tool call to its pup equivalent using the Tool Reference appendix at the bottom of this file.
  6. If neither is available → stop and tell the user:

    "Neither the Datadog MCP server nor the pup CLI is available. Connect the MCP server (claude mcp add --scope user --transport http datadog-llmo-mcp 'https://mcp.datadoghq.com/api/unstable/mcp-server/mcp?toolsets=llmobs,rum') or install pup."

--backend pup is accepted anywhere in the invocation arguments and is stripped before passing remaining args to the skill logic.

pup invocation rules:

  • Invoke via Bash: pup llm-obs <subcommand> [flags]
  • pup always outputs JSON. Parse directly — no content-block unwrapping (unlike MCP results, which may wrap JSON in [{"type": "text", "text": "<json>"}]).
  • If pup returns an auth error, tell the user to run pup auth login and stop.
  • Parallelization: issue multiple Bash tool calls in a single message (one pup command per call).
  • Time flags: pup accepts bare duration strings (1h, 7d, 30m) and RFC3339 timestamps. Do not use now--prefixed strings — strip the prefix when converting from a skill --timeframe argument: now-7d7d, now-24h24h, now-30d30d.
  • --summary on pup llm-obs spans search strips payload fields to essential metadata only. Use it in bulk/search phases where content is not needed.

pup mode notes by entry mode:

  • session_id mode: Steps 1–3 and Step 5 work fully. Step 4 (RUM) uses pup rum aggregate --user-email EMAIL instead of analyze_rum_events — see Tool Reference. Step 4b (audit trail) is pup-native and queries the active user's own org via OAuth.
  • trace_id mode: Full parity with MCP mode.
  • ml_app mode: Option A (aggregate_spans) is unavailable in pup — skip it and proceed directly to Option B.

Invocation ID: At the very start of each invocation, before any MCP tool call, generate an 8-character hex invocation ID (e.g., 3a9f1c2b). Keep it constant for the entire invocation.

Intent tagging: On every MCP tool call, prefix telemetry.intent with skill:agent-observability-session-classify[<inv_id>] — followed by a description of why the tool is being called. On the first MCP tool call only, use skill:agent-observability-session-classify:start[<inv_id>] — instead (note the :start suffix). Example first call: skill:agent-observability-session-classify:start[3a9f1c2b] — Step 1: enumerate turn root spans for session abc-123

Read the full file on GitHub · 1,154 lines

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. 8d ago First seen · 1,154 lines · 136 tokens per session scan A 375fc3c0a1d2

Subscribe to this mod's changes

agent-observability-session-classify is a skill published in the GitHub repository datadog-labs/agent-skills (165 stars, last pushed 12d ago), licensed MIT. It adds 136 tokens to every session and 16,126 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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