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-eval-pipelinegit 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-eval-pipeline)<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-eval-pipeline"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-eval-pipeline/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/datadog-labs/agent-skills/agent-observability-eval-pipeline"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-eval-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
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 →
- high Privilege Escalation · line 155 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Rogue Agent · line 112 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Excessive Agency · line 354 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Rogue Agent · line 381 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00269 | $0.15265 |
| Opus 5 | $0.00134 | $0.07633 |
| Sonnet 5 | $0.00054 | $0.03053 |
| Haiku 4.5 | $0.00027 | $0.01527 |
Grade A, and why
agent-observability-eval-pipeline 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agent-observability-eval-pipeline — 94% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 789 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 the sub-skill 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. Each sub-skill carries its own Tool Reference appendix with the full MCP→pup mapping.
- 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') or install pup."
--backend pup is accepted anywhere in the invocation arguments. Strip it from args before passing to sub-skills, but carry the pup-mode decision forward — every sub-skill must also operate in pup mode for the entire pipeline run.
Sub-skill backend propagation: The backend detected at startup applies to all sub-skills invoked across the six phases. Do not re-detect per phase. Announce once at startup:
- MCP mode: "(Running in MCP mode — all features available.)"
- pup mode: "(Running in pup mode — pup commands used throughout. All features available.)"
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-eval-pipeline[<inv_id>] — followed by a description of why the tool is being called. On the first MCP tool call only, use skill:agent-observability-eval-pipeline:start[<inv_id>] — instead (note the :start suffix). Example first call: skill:agent-observability-eval-pipeline:start[3a9f1c2b] — Precheck: verify ml_app has traces in the last 7 days
What ships with it
3 files 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.
- 12d ago First seen · 789 lines · 269 tokens per session scan A bb52c7b9b1da
agent-observability-eval-pipeline is a skill published in the GitHub repository datadog-labs/agent-skills (169 stars, last pushed today), licensed MIT. It adds 269 tokens to every session and 15,265 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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