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/ontorata/rataryWrote 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/ontorata/ratary/ai-memory)<a href="https://agentmods.dev/rules/ontorata/ratary/ai-memory"><img src="https://agentmods.dev/badge/rules/ontorata/ratary/ai-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/ontorata/ratary/ai-memory"><img src="https://agentmods.dev/badge/rules/ontorata/ratary/ai-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 | $0.00447 | $0.00447 |
| Opus 5.5 | $0.00179 | $0.00179 |
| Sonnet 5.5 | $0.00089 | $0.00089 |
| Haiku 4.5 | $0.00045 | $0.00045 |
Grade A, and why
ai-memory scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Pakai MCP tools** — bukan REST curl untuk memory What it actually says
Ratary — Organizational Memory
Arsitektur: docs-ai/cross-cutting/ratary-sync · docs-ai/INDEX.md
Three layers
| Layer | Role |
|---|---|
| docs-ai Knowledge OS | Source of truth — phases · ADR · decisions (Git) |
| Ratary (MCP) | Semantic organizational memory — active recall · links to docs-ai |
| Cursor | Execution — implement · sync docs-ai · validate |
Session start (when MCP available)
Recover Ontorata context using Ratary first. Validate against docs-ai Knowledge OS.
search_memory— projectontorata/ratary· tagshandoffget_memory_by_codenameif user mentions codename- Validate against docs-ai files and product code — docs-ai wins on knowledge; code repo wins on implementation
- Fallback if MCP down: sessions/CURRENT.md
Session end
save_memory— primary handoff (tags:handoff, project name) · includedocs-ai/...path when relevant- Update docs-ai session audit if needed
- Sync config lives under docs-ai
cross-cutting/ratary-sync/
Rules
- Jangan anggap Ratary menggantikan file di docs-ai
- Jangan minta API key di Cursor — MCP sudah akses D1
- Pakai MCP tools — bukan REST curl untuk memory
- Bootstrap laptop baru: clone product repos + docs-ai → env → baru Ratary
Phase 4
Dogfood: Ontorata organizational memory = first internal production workload on Ratary.
Balas user dalam Indonesian; memory content / ADR dalam English.
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.
- 2mo ago First seen · 45 lines · 447 tokens per session scan A a7a325b4fa99
ai-memory is a cursor rule published in the GitHub repository ontorata/ratary (1 stars, last pushed 2mo ago), licensed MIT. It adds 447 tokens to every session, about $0.0018 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). 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
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
dreamd-recall
Search dreamd at the start of a task and quote a hit. Append when the user corrects you or states a decision.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
common_memory_bank
I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read…
self-improving-obsidian-llm-wiki
LLM Wiki OS operating rules.
memsprout
Cursor rule "memsprout" from memsprout/agents, covering using memsprout, search first, capturing, spaces and topics and files.