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 ai-activity-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/ai-activity-audit)<a href="https://agentmods.dev/skills/neverinfamous/mysql-mcp/ai-activity-audit"><img src="https://agentmods.dev/badge/skills/neverinfamous/mysql-mcp/ai-activity-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/ai-activity-audit"><img src="https://agentmods.dev/badge/skills/neverinfamous/mysql-mcp/ai-activity-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 | $0.00042 | $0.01248 |
| Opus 5 | $0.00021 | $0.00624 |
| Sonnet 5 | $0.00008 | $0.00250 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
dd-audit-ai-activity 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.
This is a copy
100% identical to dd-audit-ai-activity — 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Trail: AI Activity Audit
Every Datadog MCP tool call is recorded in Audit Trail under the Bits AI SRE category. This skill surfaces what the AI assistant has done in your org — which users invoked it, which tools were called, and which resources were affected.
Prerequisites
pup auth login # OAuth2 (recommended)
# or set DD_API_KEY + DD_APP_KEY with audit_logs_read scope
Queries
All MCP tool activity in a time window
pup audit-logs search --query "@evt.name:\"MCP Server\"" --from 7d --limit 500 -o json \
| jq '[.data[] | {
timestamp: .attributes.timestamp,
user: .attributes.attributes.usr.email,
actor_type: .attributes.attributes.evt.actor.type,
action: .attributes.attributes.action,
resource_type: .attributes.attributes.asset.type,
resource_id: .attributes.attributes.asset.id,
ip: .attributes.attributes.network.client.ip,
country: .attributes.attributes.network.client.geoip.country.name
}]'
Activity by user (who is using the AI assistant most?)
pup audit-logs search --query "@evt.name:\"MCP Server\"" --from 30d --limit 1000 -o json \
| jq '[.data[] | .attributes.attributes.usr.email]
| group_by(.)
| map({user: .[0], tool_calls: length})
| sort_by(-.tool_calls)'
Resources modified by AI tool calls
pup audit-logs search \
--query "@evt.name:\"MCP Server\" @action:(created OR modified OR deleted)" \
--from 7d --limit 500 -o json \
| jq '[.data[] | {
timestamp: .attributes.timestamp,
user: .attributes.attributes.usr.email,
action: .attributes.attributes.action,
resource_type: .attributes.attributes.asset.type,
resource_id: .attributes.attributes.asset.id
}]'
AI activity for a specific user
pup audit-logs search \
--query "@evt.name:\"MCP Server\" @usr.email:[email protected]" \
--from 30d --limit 500 -o json \
| jq '[.data[] | {
timestamp: .attributes.timestamp,
action: .attributes.attributes.action,
resource_type: .attributes.attributes.asset.type,
resource_id: .attributes.attributes.asset.id
}]'
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 · 158 lines · 42 tokens per session scan A fcbe79bfcd6a
dd-audit-ai-activity is a skill published in the GitHub repository neverinfamous/mysql-mcp (10 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 1,248 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to dd-audit-ai-activity, differing in 0 lines, and is treated as a copy.
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