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/proticom/gnosys/cursorgit clone --depth 1 https://github.com/proticom/gnosysWrote 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/proticom/gnosys/cursor)<a href="https://agentmods.dev/rules/proticom/gnosys/cursor"><img src="https://agentmods.dev/badge/rules/proticom/gnosys/cursor.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 | $0.00990 | $0.00990 |
| Opus 5 | $0.00495 | $0.00495 |
| Sonnet 5 | $0.00198 | $0.00198 |
| Haiku 4.5 | $0.00099 | $0.00099 |
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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gnosys Memory System
This project uses Gnosys for persistent memory via MCP. Gnosys uses a centralized brain (~/.gnosys/gnosys.db) shared across all projects with project, user, and global scopes.
Read first
- At task start, call
gnosys_discoverwith relevant keywords - Load results with
gnosys_read - When the user references past decisions, says "recall", "remember when", "what did we decide" — search memory first
- Use
gnosys_federated_searchfor cross-project search with scope boosting - Use
gnosys_working_setto see recently modified memories for context - Gnosys starts on a reduced core toolset — if a gnosys tool you need is missing, call
gnosys_toolset(set: standard or full) to expand it
Write automatically
- Always write with
gnosys_add_structured— you are an LLM, so structure the memory yourself (title, category, tags, content, relevance) and pass the explicit fields. NEVER call the freeformgnosys_add: the server rejects it with an error for MCP callers (it would run a second, redundant LLM call and depends on a provider key that can fail silently).gnosys_add_structuredmakes no server-side model call. (Freeformgnosys_addexists only for non-agent scripts/cron, which must set GNOSYS_ALLOW_FREEFORM_ADD=1.) - When user says "remember", "memorize", "save this", "note this down", "don't forget" — call
gnosys_add_structured - When user states a decision or preference (even casually) — commit to
decisionscategory - When user provides a spec or plan — commit BEFORE starting work
- After significant implementation — commit findings and gotchas
- User preferences (coding style, conventions) — use
gnosys_preference_set
Key tools
| Action | Tool |
|---|---|
| Find memories | gnosys_discover (metadata) → gnosys_read (content) |
| Search | gnosys_hybrid_search (best), gnosys_federated_search (cross-project), gnosys_search (keyword), gnosys_ask (Q&A) |
| Write | gnosys_add_structured (explicit fields — always use this as an agent; no server-side LLM call). gnosys_add (freeform) is rejected for MCP callers — non-agent scripts only. |
| Update | gnosys_update, gnosys_reinforce (useful/not_relevant/outdated) |
| Browse | gnosys_list, gnosys_lens (filtered), gnosys_tags, gnosys_graph |
| Maintain | gnosys_maintain, gnosys_stale, gnosys_history, gnosys_dashboard |
| Preferences | gnosys_preference_set, gnosys_preference_get, gnosys_preference_delete |
| Projects | gnosys_init (register), gnosys_briefing (status), gnosys_stores (debug) |
| Context | gnosys_federated_search, gnosys_working_set, gnosys_detect_ambiguity |
| Recall | gnosys_recall (fast context injection, sub-50ms) |
| Export | gnosys_export (Obsidian vault), gnosys_audit (operation trail) |
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.
- yesterday First seen · 52 lines · 990 tokens per session scan A a20e3c85b0f8
cursor is a cursor rule published in the GitHub repository proticom/gnosys (4 stars, last pushed yesterday), licensed MIT. It adds 990 tokens to every session, about $0.0049 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 2026-09-03.
Other cursor rules, from other repositories
amfs-memory
Rules for using AMFS (Agent Memory File System) as a persistent agent brain across coding sessions.
cursorrules
Zettelkasten memory for AI agents. Markdown cards in /.memex/cards/ with [[wikilinks]]. npm package @touchskyer/memex. Distributed as CLI + MCP server + Claude Code plugin + VS Code extension + Pi extension.
cursor-brain-os
Brain OS operating instructions — use MCP tools for project state, not source-code grep.
wrapup
End-of-session wrap-up: summarizes the session and pushes a log to the project's linked NotebookLM notebook. Activate with @wrapup in chat, or "wrap up", "save this session", or "end of session".
avos-agent-workflow
Avos memory workflow for AI coding agents. Guides codebase search and history lookup.
140-unified-command-interface
WHEN processing commands APPLY unified interface patterns for consistent memory bank operations.