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/monkey2jack/aidumei/cursor-aidumemgit clone --depth 1 https://github.com/monkey2jack/aiduMEIWhat 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 | $0.00000 | $0.00247 |
| Opus 5 | $0.00000 | $0.00123 |
| Sonnet 5 | $0.00000 | $0.00049 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
cursor-aidumem 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
aiduMEI Code Memory Hook
When you make significant code changes (new functions, refactors, bug fixes), automatically store them to aiduMEI's Raw Drawer for long-term recall.
Trigger
After completing a meaningful code change (not trivial one-liners), call:
POST http://127.0.0.1:8767/add/raw
Content-Type: application/json
{
"content": "FILE: <relative_path>\n\n<code_snippet_or_summary>",
"source": "cursor_hook",
"user_id": "default"
}
What to Store
✅ Store:
- New functions/classes with their docstrings
- Bug fix descriptions with before/after
- Architectural decisions ("moved X to Y because...")
- API endpoint implementations
- Configuration changes with rationale
❌ Skip:
- Trivial whitespace/formatting changes
- Single-line variable renames
- Auto-generated code
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 · 39 lines · 0 tokens per session scan A 875fa73cc0b1
cursor-aidumem is a cursor rule published in the GitHub repository monkey2jack/aiduMEI (18 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 247 tokens. 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
rolemule-core
Core RoleMule conventions — app name, error system, JSON serialization, background tasks.
analytics-consent-onboarding
PostHog analytics, GDPR cookie consent, and onboarding tour usage.
verify-ai-readiness
Holistic assessment of the AI knowledge layer on a 5-level maturity scale; flags agent-blocking gaps.
01-qa-agent
QA AGENT PERSONA: Principles, Anti-patterns, workflows.
03-skill-testcases
SKILL: Generate Test Scenarios Matrix from API spec (use for /api-isolated-tests).
cursorrules
Agent2 is a framework for building production AI agents with typed HTTP APIs. PydanticAI handles the agent loop. Agent2 handles everything else: API, auth, pause/resume, approvals, provider routing, knowledge search.