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 ownership-agentgit 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/ownership-agent)<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/ownership-agent"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/ownership-agent/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/ownership-agent"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/ownership-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 Excessive Agency · line 8 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00067 | $0.01317 |
| Opus 5 | $0.00034 | $0.00659 |
| Sonnet 5 | $0.00013 | $0.00263 |
| Haiku 4.5 | $0.00007 | $0.00132 |
Grade A, and why
k9-ownership-byod-setup 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 11d 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:
- k9-ownership-byod-setup — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BYOD Preferences Reference Table Setup
Help customers create and upload a k9_ownership_preferences reference table to customize how the Ownership Agent determines resource owners.
Read first
references/schema.md— full schema, column details, validation rules, and per-type examplesassets/example.csv— complete working CSV with all three preference types
Overview
The Ownership Agent infers owners for cloud resources with security findings. Ownership preferences let customers customize this by providing rules in a Datadog reference table. The agent reads them automatically.
With preferences you can:
- Map tags to owners: Resources with specific tag values belong to a particular team or person
- Exclude accounts: Prevent bot accounts or shared infrastructure from appearing as owners
- Provide custom guidance: Give the AI engine organization-specific context
Reference Table Details
- Table name:
k9_ownership_preferences(exact name, must match) - Effect delay: Changes take effect within 24 hours of upload
- Schema: 12 columns, all STRING — see
references/schema.mdfor details
Workflow
Step 1: Determine Needs
Ask the customer:
- Tag mappings: "Do you have tags on your cloud resources that indicate ownership? (e.g.,
cost-center,team,project)" - Exclusions: "Are there bot accounts, service accounts, or shared accounts that should never appear as owners?"
- Prompt text: "Any organization-specific context that would help determine ownership? (e.g., naming conventions, team structure)"
Step 2: Generate CSV
Read references/schema.md for the full column spec and assets/example.csv for a working template. Build a CSV with all 12 column headers. Each row gets a unique sequential id and fills columns relevant to its preference_type, leaving the rest empty.
Step 3: Upload Instructions
Option A — CSV Upload (UI):
- Go to Integrations > Reference Tables in Datadog
- Click New Reference Table
- Upload the CSV
- Set table name to
k9_ownership_preferences - Choose primary key:
preference_type, tag_key, tag_value, handle - Save
What ships with it
2 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.
- 11d ago First seen · 108 lines · 67 tokens per session scan A c0761e4e9499
k9-ownership-byod-setup is a skill published in the GitHub repository datadog-labs/agent-skills (169 stars, last pushed 15d ago), licensed MIT. It adds 67 tokens to every session and 1,317 once invoked, about $0.0003 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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