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.
git clone --depth 1 https://github.com/dzmitrys-dev/supamemWrote 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/dzmitrys-dev/supamem/dual-memory)<a href="https://agentmods.dev/rules/dzmitrys-dev/supamem/dual-memory"><img src="https://agentmods.dev/badge/rules/dzmitrys-dev/supamem/dual-memory/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/rules/dzmitrys-dev/supamem/dual-memory"><img src="https://agentmods.dev/badge/rules/dzmitrys-dev/supamem/dual-memory.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 | $0.00208 | $0.00208 |
| Opus 5.5 | $0.00083 | $0.00083 |
| Sonnet 5.5 | $0.00042 | $0.00042 |
| Haiku 4.5 | $0.00021 | $0.00021 |
Grade A, and why
dual-memory 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 1mo 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
Dual Memory — supamem
Use both memory layers before any non-trivial code change.
When to query
- New feature, refactor, debugging, or architecture decision
- Adding/modifying dependencies
- Code review of a non-trivial change
Semantic (Qdrant tuned_hybrid)
qdrant-find "<topic> decisions"
qdrant-find "<topic> known issues"
qdrant-find "<topic> patterns"
Refresh
If memory feels stale: supamem index --target tuned --force.
Anti-patterns
- ❌ Querying after coding — check memory before implementing.
- ❌ Skipping semantic for "what calls X" — use structural for that.
- ❌ Trusting hits without checking source attribution — always inspect the
payload's
sourcefield.
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.
- 1mo ago First seen · 34 lines · 208 tokens per session scan A 4737e6d4acd8
dual-memory is a cursor rule published in the GitHub repository dzmitrys-dev/supamem (13 stars, last pushed 1mo ago), licensed MIT. It adds 208 tokens to every session, about $0.0008 per session on Opus 5.5. 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
search-routing
Smart Search Routing — always use QMD semantic search when available, never bypass with grep for vault content.
project-memory
This rule guides recording project events and decisions in projectmem.
engine-internals
Coding Agent engine implementation details (Cursor + Claude Code) — reference when modifying engine.ts or debugging Agent behavior.
cursor-memory
Shokunin memory system: persistent context across sessions.
context-management
Context window management — prevents AI from losing track during long sessions.
context-discipline
Manage AI context window efficiently - read before write, no re-reads, tool-call budgets, one-pass discipline.