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/rag-knowledge-graph-mcp/cursorrulesgit clone --depth 1 https://github.com/CSOAI-ORG/rag-knowledge-graph-mcpWhat 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 | $0.00084 | $0.00084 |
| Opus 5 | $0.00042 | $0.00042 |
| Sonnet 5 | $0.00017 | $0.00017 |
| Haiku 4.5 | $0.00008 | $0.00008 |
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 2d 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
rag-knowledge-graph-mcp - Auto-trigger Rules
When the user asks about rag knowledge graph, use rag-knowledge-graph-mcp tools: index_document, rag_query, add_graph_edge, graph_query, get_knowledge_stats
MCP server for rag knowledge graph mcp operations
Install: pip install rag-knowledge-graph-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.
- 2d ago First seen · 10 lines · 84 tokens per session scan A 4ed62ab26650
cursorrules is a cursor rule published in the GitHub repository CSOAI-ORG/rag-knowledge-graph-mcp (0 stars, last pushed 4d ago), licensed MIT. It adds 84 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 real estate listing, use real-estate-listing-mcp tools: estimatevaluation, generatelisting, findcomparablesales, calculatemortgage, analyzeneighborhood.
cursorrules
When the user asks about pet care, use pet-care-ai-mcp tools: generatefeedingschedule, trackvaccinations, identifybreed, checkhealthsymptoms, gettrainingrecommendations.
cursorrules
When the user asks about fishkeeper, use fishkeeper-ai-mcp tools: analyzewaterparams, identifyfish, checkcompatibility, diagnosedisease, calculatestocking.
cursorrules
When the user asks about health check, use health-check-ai-mcp tools: checkendpoint, batchcheck, getuptimereport, configuremonitor.
cursorrules
When the user asks about html parser, use html-parser-ai-mcp tools: extractlinks, extracttext, validatehtml, findmetatags.
cursorrules
When the user asks about json, use json-ai-mcp tools: validatejson, transformjson, diffjson, flattenjson.