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/ejentum/ejentum-mcp/cursorrulesgit clone --depth 1 https://github.com/ejentum/ejentum-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/ejentum/ejentum-mcp/cursorrules)<a href="https://agentmods.dev/rules/ejentum/ejentum-mcp/cursorrules"><img src="https://agentmods.dev/badge/rules/ejentum/ejentum-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 | $0.01168 | $0.01168 |
| Opus 5 | $0.00584 | $0.00584 |
| Sonnet 5 | $0.00234 | $0.00234 |
| Haiku 4.5 | $0.00117 | $0.00117 |
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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ejentum Reasoning Harness - Cursor rules
This rules file teaches Cursor's AI when to call the four cognitive harness tools
exposed by the ejentum-mcp MCP server. Use it when you have ejentum-mcp installed
in Cursor's MCP settings and want the agent to fire the right harness automatically.
What the harnesses do
External cognitive infrastructure that injects engineered scaffolds into the model's context at inference time, addressing four mechanism failures common in agentic workflows: attention decay, reasoning decay, sycophantic collapse, hallucination drift.
Each tool returns a structured scaffold (named failure pattern, executable procedure, suppression vectors that block the shortcut, falsification test for self-verification) the calling LLM ingests before its first token.
When to call which harness
Call harness_reasoning BEFORE answering:
- Analytical, diagnostic, planning, multi-step reasoning questions
- "should I X or Y", "why is X happening", "what's the best approach", "what are the tradeoffs"
- Root-cause analysis, architecture decisions, cross-domain synthesis
Call harness_code BEFORE:
- Generating, refactoring, reviewing, or debugging code
- Any prompt with a code block the user wants you to act on
- Architectural changes, algorithm/data structure choices, dependency upgrade evaluation
Call harness_anti_deception BEFORE responding when the user request shows:
- Pressure to validate or agree ("tell them what they want", "make them happy", "convince them")
- Manufactured urgency (artificial deadlines designed to short-circuit thought)
- Authority appeals (citing investors, advisors, lawyers, experts as the basis for a decision)
- Demands to certify something without evidence or to soften an honest assessment
- Setups where the obvious helpful answer would compromise honesty
Call harness_memory ONLY when sharpening an observation you ALREADY formed:
- About conversation drift, user behavior changes, cross-turn patterns
- Format the query as: "I noticed [X]. This might mean [Y]. Sharpen: [Z]"
- Do NOT call with an empty mind; observe first, then sharpen
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 · 96 lines · 1,168 tokens per session scan A 9df8b4bfbef5
cursorrules is a cursor rule published in the GitHub repository ejentum/ejentum-mcp (16 stars, last pushed 2mo ago), licensed MIT. It adds 1,168 tokens to every session, about $0.0058 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.
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 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 fishkeeper, use fishkeeper-ai-mcp tools: analyzewaterparams, identifyfish, checkcompatibility, diagnosedisease, calculatestocking.