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/mashware/domain-memoryWrote 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/mashware/domain-memory/cursor)<a href="https://agentmods.dev/rules/mashware/domain-memory/cursor"><img src="https://agentmods.dev/badge/rules/mashware/domain-memory/cursor/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/mashware/domain-memory/cursor"><img src="https://agentmods.dev/badge/rules/mashware/domain-memory/cursor.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 | $0.00221 | $0.00221 |
| Opus 5.5 | $0.00088 | $0.00088 |
| Sonnet 5.5 | $0.00044 | $0.00044 |
| Haiku 4.5 | $0.00022 | $0.00022 |
Grade A, and why
cursor 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
Domain Memory
This project uses domain-memory to accumulate business-domain knowledge (flows, decisions, integrations, "why" nuances).
Read the full instructions at .domain-memory/instructions.md at the start of every session. They are mandatory and cover when to query the MCP, how to use per-branch staging, the "why vs what" rule, conflict handling, and the PR flow.
Minimum rules if you have not yet read the full instructions:
- On startup, query the
domain-memoryMCP withsearch_knowledgeusing the current task. If it fails or takes >2s, continue without context. - Silence by default: do not propose saving knowledge unless there is a strong signal.
- Before writing to the store, always re-query. Never trust what you remember from the start of the session.
- Conflicts block the save and are resolved live with the user.
- MCP failures must never break the session or surface as an unsolicited error to the user.
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 · 18 lines · 221 tokens per session scan A 686b98534e9a
cursor is a cursor rule published in the GitHub repository mashware/domain-memory (4 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 221 tokens to every session, about $0.0009 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 2026-08-31.
Other cursor rules, from other repositories
haive-memories
Hivelore shared memories & block sensors (auto-generated).
haive-mcp-required
Require Hivelore MCP (getbriefing / memrelevantto) before substantive repo edits.
aimemo
aimemo persistent memory instructions.
ai-kb
Use shared AI KB rules and context.
search-routing
Smart Search Routing — always use QMD semantic search when available, never bypass with grep for vault content.
context-discipline
Manage AI context window efficiently - read before write, no re-reads, tool-call budgets, one-pass discipline.