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/wuisabel-gif/memwhale/memorywhalegit clone --depth 1 https://github.com/wuisabel-gif/MemWhaleWrote 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/wuisabel-gif/memwhale/memorywhale)<a href="https://agentmods.dev/rules/wuisabel-gif/memwhale/memorywhale"><img src="https://agentmods.dev/badge/rules/wuisabel-gif/memwhale/memorywhale.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 | $0.00078 | $0.00488 |
| Opus 5 | $0.00039 | $0.00244 |
| Sonnet 5 | $0.00016 | $0.00098 |
| Haiku 4.5 | $0.00008 | $0.00049 |
Grade A, and why
memorywhale 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
MemoryWhale memory
MemoryWhale records terminal commands, their arguments, exit codes, output, and errors into a local SQLite database — the record of what was already tried, surviving crashes, SSH drops, and switching machines. It also holds freeform lessons a human or a past agent chose to remember.
You reach it through the memorywhale MCP server (tools: recent_errors,
search_memory, get_context, remember). If the MCP server isn't connected,
shell out to the mw CLI instead.
When to READ from it
- A build/test/deploy is failing and it might have failed before.
- You need the exact earlier error text, flags, or working directory — not a paraphrase.
- The user references past work ("last week", "on the Jetson", "how did I fix").
Before proposing a fix for a failure, check whether it already has a known
cause: call search_memory / recent_errors (or mw context --last-error /
mw search "<error text>"). If a remembered lesson matches, say so and apply
it rather than re-deriving from scratch.
When to WRITE to it
Once you've figured out why something failed or how a fix worked — not just
that it's fixed — save it with the remember tool (or mw remember "<text>").
Keep it a self-contained conclusion: what was wrong + what fixed it, e.g.
"the E0308 in camera-driver was the fps field being a string; fix: parse as i32".
That's the part a raw command log doesn't capture, and it's what saves the next
session from re-deriving the same thing.
Note
Captured output is secret-redacted on the way in, but treat it as real project data. Everything is local — nothing is uploaded.
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 · 43 lines · 78 tokens per session scan A 9618061ff83d
memorywhale is a cursor rule published in the GitHub repository wuisabel-gif/MemWhale (52 stars, last pushed 2d ago), licensed MIT. It adds 78 tokens to every session and 488 once invoked, about $0.0004 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
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.
jarvis-agent-rules
MCP server jarvis: notes, Jira, knowledge graph, sessions, preferences.
cursor
Coordination Memory MCP protocol.
alaya-agent-working-contract
Alaya working contract — goal, honesty, modes, review loops, worktrees (fills do-it gaps).
olympuz
Repository rules for every coding agent.
obsidian-hub-spoke-graph
Hub-and-spoke (MOC) linking for Obsidian vault notes — no peer mesh, leaves up-link only.