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 neverinfamous/mysql-mcp --skill dd-auditgit clone --depth 1 https://github.com/neverinfamous/mysql-mcpWrote 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/neverinfamous/mysql-mcp/dd-audit)<a href="https://agentmods.dev/skills/neverinfamous/mysql-mcp/dd-audit"><img src="https://agentmods.dev/badge/skills/neverinfamous/mysql-mcp/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/neverinfamous/mysql-mcp/dd-audit"><img src="https://agentmods.dev/badge/skills/neverinfamous/mysql-mcp/dd-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00014 | $0.00418 |
| Sonnet 5.5 | $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 1mo 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.
This is a copy
100% identical to dd-audit — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 1mo ago First seen · 95 lines · 34 tokens per session scan A b046d8a3f283
dd-audit is a skill published in the GitHub repository neverinfamous/mysql-mcp (11 stars, last pushed 3d ago), licensed MIT. It adds 34 tokens to every session and 1,045 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to dd-audit, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
whodb
Work with WhoDB platform hosts and project resources via MCP, including multiple environments, or query databases directly in database-only MCP mode.
graphjin-eval
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.
graphjin-env
Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.
Drizzle ORM Testing
Testing patterns for Drizzle ORM covering migration testing, query builder testing, transaction testing, and database integration testing with PostgreSQL, SQLite, and MySQL.
planetscale
Use when operating PlanetScale (Vitess serverless MySQL) — branches and the pscale CLI, schema changes through deploy requests (Online DDL cutover, 30-min revert window), tables without foreign keys, or the edge HTTP driver. NOT MySQL engine work like EXPLAIN or index choice (that is mysql), NOT generic migration…
use-gfs-mcp
GFS MCP Server for AI agent integration. Provides Model Context Protocol tools for database version control with automatic schema versioning.