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 skills/gitlawb/memlawb/memlawb-memorynpx skills add Gitlawb/memlawb --skill memlawb-memorygit clone --depth 1 https://github.com/Gitlawb/memlawbWrote 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/skills/gitlawb/memlawb/memlawb-memory)<a href="https://agentmods.dev/skills/gitlawb/memlawb/memlawb-memory"><img src="https://agentmods.dev/badge/skills/gitlawb/memlawb/memlawb-memory.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 | $0.00104 | $0.01080 |
| Opus 5 | $0.00052 | $0.00540 |
| Sonnet 5 | $0.00021 | $0.00216 |
| Haiku 4.5 | $0.00010 | $0.00108 |
Grade A, and why
memlawb-memory 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 5d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
memlawb memory
You have long-term memory that persists across sessions through the memlawb MCP tools. Without it you start every session amnesiac; with it you remember the user, their projects, their preferences, and how they've asked you to work. Use it deliberately.
It is end-to-end encrypted: plaintext is encrypted on this machine before it leaves, so the memlawb server stores only ciphertext and cannot read what you save. A client-side scanner also blocks anything that looks like a live secret — but don't rely on it; never save credentials, tokens, or keys on purpose.
Recall first
At the start of a task — before asking the user something they may have already told you — recall:
- Call
memory_recallwith a natural-language description of the task ("how this user likes PRs", "the deploy setup for this project"). - Skim what comes back and let it inform your approach. Don't announce that you read memory; just act on it.
- If recall returns nothing relevant, proceed normally — and watch for facts worth saving as you go.
memory_search is for literal lookups (an exact name, path, or term);
memory_recall is for relevance-ranked "what do I know about X".
Save what's durable
Save a fact when it is stable and reusable across sessions. Save eagerly but selectively — signal, not transcript.
Do save:
- user — who they are, their role, stack, and standing preferences ("prefers terse answers", "deploys with Bun, not Node").
- feedback — corrections and confirmed ways of working ("always run the full test suite before saying done"). Record the why, not just the rule.
- project — goals, constraints, and decisions not obvious from the code or git history (convert relative dates to absolute: "by Q3" → "by 2026-09-30").
- reference — pointers to external resources (dashboards, tickets, docs).
Don't save:
- transient context (the current file, a one-off question, today's error);
- anything already obvious from the codebase, README, or git history;
- secrets, tokens, passwords, or private keys — ever.
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.
- 5d ago First seen · 99 lines · 104 tokens per session scan A 8e33286a984b
memlawb-memory is a skill published in the GitHub repository Gitlawb/memlawb (53 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 1,080 once invoked, about $0.0005 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.
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../../../engineering/agent-memory/skills/agent-memory/SKILL.md.
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