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/private-ai-workspace)<a href="https://agentmods.dev/rules/ontorata/ratary/private-ai-workspace"><img src="https://agentmods.dev/badge/rules/ontorata/ratary/private-ai-workspace.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 5.1 | $0.00248 | $0.00248 |
| Opus 5 | $0.00124 | $0.00124 |
| Sonnet 5 | $0.00050 | $0.00050 |
| Haiku 4.5 | $0.00025 | $0.00025 |
Grade A, and why
private-ai-workspace 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 6d 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
Knowledge OS Governance (docs-ai)
Ontorata’s knowledge source of truth is the private GitHub repo ontorata/docs-ai (local: D:\Apps\docs-ai).
Rules
- Write knowledge (phases, ADR, reviews, designs, compliance detail) into docs-ai, not into product-local
.ai/folders. - Product repos keep implementation + thin
docs/README.mdpointers to docs-ai. - Never commit secrets into docs-ai.
- Public product
docs/may still hold released contracts; prefer linking to docs-ai for internal rationale. - Ratary handoffs should cite
docs-ai/...paths when referring to durable documents.
Retired concept
The product-local folder name .ai/ is deprecated. Use docs-ai taxonomy (products/, decisions/, cross-cutting/, …).
Legacy .ai/ trees may exist only as MOVED.md stubs during cutover.
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.
- 6d ago First seen · 23 lines · 248 tokens per session scan A 5a6e6e289581
private-ai-workspace is a cursor rule published in the GitHub repository ontorata/ratary (1 stars, last pushed 27d ago), licensed MIT. It adds 248 tokens to every session, about $0.0012 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 2026-08-31.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.