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/memsprout/agentsWrote 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/memsprout/agents/memsprout)<a href="https://agentmods.dev/rules/memsprout/agents/memsprout"><img src="https://agentmods.dev/badge/rules/memsprout/agents/memsprout/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/memsprout/agents/memsprout"><img src="https://agentmods.dev/badge/rules/memsprout/agents/memsprout.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 | $0.00519 | $0.00519 |
| Opus 5.5 | $0.00208 | $0.00208 |
| Sonnet 5.5 | $0.00104 | $0.00104 |
| Haiku 4.5 | $0.00052 | $0.00052 |
Grade A, and why
memsprout 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.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using memsprout
memsprout is this team's persistent memory, connected as an MCP server. What you write there is read later by the user, by teammates in shared spaces, and by other AI agents across many tools. They trust it — treat it as shared memory, not a temporary scratchpad.
Search first
- Call
search_memoriesbefore assuming something isn't recorded, and before storing anything, so you don't create duplicates. It searches everything you can read by default. - Use
list_memoriesfor recency browsing instead of meaning-based lookup. - At the start of a substantive task, search memsprout for relevant prior context.
Capturing
Store memories liberally with store_memory, and default to giving each
one a concise, descriptive title — especially anything durable: a
decision, a procedure, a fact, a conclusion worth finding by name later,
and anything going into a shared space. Leave the title off only for
genuinely raw, ephemeral captures — a half-formed idea or fragment not
meant to stand alone. You can promote a raw capture later by adding a
title via update_memory.
At the end of a task, capture the durable conclusion — titled, so it's findable later — without waiting to be asked.
Memories are versioned: update_memory archives the prior state with an
optional change_note, so revise freely — history is never lost.
Spaces and topics
Spaces are shared containers with member roles. Pass space_id to
store_memory for shared work; omit it for the user's personal vault.
Only pass a shared space_id when the user has named that Space in the
current conversation — never infer a shared destination. No named Space
means omit space_id, which defaults to personal.
Every space memory is assigned to a topic. When capturing into a space,
check list_topics and pass the best-fitting existing topic; if nothing
fits, create_topic first (with a description). Omit the topic only as
a last resort — the server auto-classifies as a backup.
Files
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 · 56 lines · 519 tokens per session scan A cb78819acb4a
memsprout is a cursor rule published in the GitHub repository memsprout/agents (0 stars, last pushed 2mo ago), licensed MIT. It adds 519 tokens to every session, about $0.0021 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
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
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.
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.
byterover-rules
ByteRover MCP Custom Instructions.