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 dd-auditgit 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/dd-audit)<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/dd-audit"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/dd-audit/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/dd-audit"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/dd-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00034 | $0.01045 |
| Opus 5 | $0.00017 | $0.00522 |
| Sonnet 5 | $0.00007 | $0.00209 |
| Haiku 4.5 | $0.00003 | $0.00104 |
Grade A, and why
dd-audit 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- dd-audit — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datadog Audit Trail
Investigate user activity, configuration changes, access patterns, and compliance evidence using pup audit-logs.
Sub-Skills
| Sub-skill | Use when |
|---|---|
| security-investigation | "Who changed X?", "What did this user do?", "Show me deletions in the last 24h" |
| key-compromise | "Was this API key compromised?", "What did key XYZ do?", "Investigate suspicious key activity" |
| cost-spike-investigation | "Why did my bill go up?", "What caused this usage spike?", "Investigate LLM cost increase" |
| compliance-report | "Generate SOC 2 evidence", "PCI audit log", "User provisioning report for auditor" |
| ai-activity-audit | "What did the AI assistant do?", "Audit MCP tool calls", "AI governance report" |
Prerequisites
pup auth login # OAuth2 (recommended)
# or set DD_API_KEY + DD_APP_KEY with audit_logs_read scope
Commands
# List recent events
pup audit-logs list --from 1h --limit 100
# Search with a query
pup audit-logs search --query "@action:deleted" --from 24h
# JSON output for piping to jq
pup audit-logs search --query "@usr.email:[email protected]" --from 7d -o json | jq '.data[].attributes'
Event Schema Quick Reference
| Field | Description | Example values |
|---|---|---|
@usr.email |
Actor email | [email protected] |
@evt.actor.type |
How action was taken | USER, API_KEY, SUPPORT_USER |
@action |
Verb | created, modified, deleted, accessed, login |
@evt.name |
Event category | Dashboard, Monitor, Authentication, Access Management |
@asset.type |
Resource type | dashboard, monitor, api_key, role, user |
@asset.id |
Resource identifier | abc-123 |
@metadata.api_key.id |
API key used (if applicable) | key_abc123 |
@metadata.app_key.id |
App key used (if applicable) | app_abc123 |
@network.client.ip |
Client IP address | 1.2.3.4 |
@network.client.geoip.country.name |
Country | United States |
@network.client.geoip.as.name |
ASN name | Amazon.com |
@http.url_details.path |
API endpoint path | /api/v1/dashboard/xyz |
What ships with it
6 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.
- 9d ago First seen · 95 lines · 34 tokens per session scan A b046d8a3f283
dd-audit is a skill published in the GitHub repository datadog-labs/agent-skills (166 stars, last pushed 13d ago), licensed MIT. It adds 34 tokens to every session and 1,045 once invoked, about $0.0002 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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