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 datadog-labs/agent-skills --skill agent-observability-session-classifygit clone --depth 1 https://github.com/datadog-labs/agent-skillsWrote 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/datadog-labs/agent-skills/agent-observability-session-classify)<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>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
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.
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 | $0.00136 | $0.16126 |
| Opus 5 | $0.00068 | $0.08063 |
| Sonnet 5 | $0.00027 | $0.03225 |
| Haiku 4.5 | $0.00014 | $0.01613 |
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([ Copies of this mod
1 near-identical copy found in the catalogue:
- agent-observability-session-classify — 100% identical, 0 lines differ
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:
- If the user passed
--backend pupanywhere in their invocation → use pup mode immediately, regardless of whether MCP tools are present. Skip steps 2–4. - Check whether MCP tools are present in your active tool list. The canonical signal is whether
mcp__datadog-llmo-mcp__search_llmobs_spansappears in your available tools. - If MCP tools are present → use MCP mode throughout. Call MCP tools exactly as named in this skill's workflow sections.
- If MCP tools are absent → check whether
pupis executable: runpup --versionvia Bash. A JSON response containing"version"confirms pup is available. - 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.
- 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 loginand 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 usenow--prefixed strings — strip the prefix when converting from a skill--timeframeargument:now-7d→7d,now-24h→24h,now-30d→30d. --summaryonpup llm-obs spans searchstrips payload fields to essential metadata only. Use it in bulk/search phases where content is not needed.
pup mode notes by entry mode:
session_idmode: Steps 1–3 and Step 5 work fully. Step 4 (RUM) usespup rum aggregate --user-email EMAILinstead ofanalyze_rum_events— see Tool Reference. Step 4b (audit trail) is pup-native and queries the active user's own org via OAuth.trace_idmode: Full parity with MCP mode.ml_appmode: 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
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.
- 8d ago First seen · 1,154 lines · 136 tokens per session scan A 375fc3c0a1d2
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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