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 agentmods add rules/csoai-org/deepfake-detector-mcp/cursorrulesgit clone --depth 1 https://github.com/CSOAI-ORG/deepfake-detector-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/rules/csoai-org/deepfake-detector-mcp/cursorrules)<a href="https://agentmods.dev/rules/csoai-org/deepfake-detector-mcp/cursorrules"><img src="https://agentmods.dev/badge/rules/csoai-org/deepfake-detector-mcp/cursorrules.svg" alt="Measured on agentmods" 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 | $0.00086 | $0.00086 |
| Opus 5 | $0.00043 | $0.00043 |
| Sonnet 5 | $0.00017 | $0.00017 |
| Haiku 4.5 | $0.00009 | $0.00009 |
Grade A, and why
cursorrules 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 3d 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.
What it actually says
deepfake-detector-mcp - Auto-trigger Rules
When the user asks about deepfake detector, use deepfake-detector-mcp tools: detect_deepfake, analyze_audio_authenticity, check_image_provenance, generate_authenticity_report
MCP server for deepfake detector mcp operations
Install: pip install deepfake-detector-mcp
By MEOK AI Labs — MIT licensed.
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.
- 3d ago First seen · 10 lines · 86 tokens per session scan A 4396873ccf9b
cursorrules is a cursor rule published in the GitHub repository CSOAI-ORG/deepfake-detector-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 86 tokens to every session, about $0.0004 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 2026-08-31.
Other cursor rules, from other repositories
cursorrules
When the user asks about iso 27001, use iso-27001-ai-mcp tools: auditisms, riskassessment, gapanalysis, crosswalktoai, generatesoa.
cursorrules
When the user asks about csv tools, use csv-tools-ai-mcp tools: parsecsv, validateheaders, detectdelimiter, converttojson.
cursorrules
When the user asks about database universal, use database-universal-mcp tools: querysql, listtables, describetable, insertrow, exporttocsv.
cursorrules
When the user asks about dependency updater, use dependency-updater-ai-mcp tools: checkoutdated, suggestupdates, checkvulnerabilities, generatelockfile.
cursorrules
When the user asks about fitness, use fitness-ai-mcp tools: generateworkout, trackcalories, calculatebodycomposition, buildtrainingplan, checkexerciseform.
cursorrules
When the user asks about habit tracker, use habit-tracker-ai-mcp tools: createhabit, logcompletion, gethabitstreak, gethabithistory, getallhabits.