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/s60yucca/mnemos/cursorrulesgit clone --depth 1 https://github.com/s60yucca/mnemosWhat 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 | $0.00287 | $0.00287 |
| Opus 5 | $0.00143 | $0.00143 |
| Sonnet 5 | $0.00057 | $0.00057 |
| Haiku 4.5 | $0.00029 | $0.00029 |
Grade A, and why
cursorrules 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 2d 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
Mnemos Memory Integration
Use Mnemos automatically during this workspace session.
- At the start of a new task or session, call
mnemos_contextonce with the current task, bug, feature, or subsystem. - Before coding in a specific area, call
mnemos_searchif prior project memory could change the implementation. - During the session, call
mnemos_storeormnemos_compilewhen you discover durable learnings:
| Situation | Type | Example |
|---|---|---|
| Bug fixed — root cause identified | semantic |
"JWT refresh failed due to clock skew" |
| Task completed — what happened | episodic |
"Migrated auth to RS256 — took 3h, tested locally" |
| Non-obvious command succeeded | skill |
"Build AAR: ./gradlew :sdk:assembleRelease --no-daemon" |
| Architecture/design decision | semantic |
"Using SQLite WAL for concurrency instead of Postgres" |
| Manual compilation of topic notes | compiled |
Use mnemos_compile to distill knowledge. |
- Do not store plans, TODOs, raw diffs, obvious code summaries, or simple routine commands (like
git status,ls). - Continue normally if Mnemos is unavailable, and do not mention it 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.
- 2d ago First seen · 19 lines · 287 tokens per session scan A b12a7928ce2c
cursorrules is a cursor rule published in the GitHub repository s60yucca/mnemos (21 stars, last pushed 1mo ago), licensed MIT. It adds 287 tokens to every session, about $0.0014 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 2026-08-30.
Other cursor rules, from other repositories
050-plan
When the user types /plan or asks to create a project plan, feature PRD, or retrospective.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
context-recorder-system
Context Recorder System (记录员系统) - 模块化索引文件.
rules
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
misc-documenting-learnings-and-clarifying-assumptions
Documenting Learnings and Clarifying Assumptions for Efficient Task Execution.