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 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/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/skills/gitlawb/memlawb/memlawb-memory"><img src="https://agentmods.dev/badge/skills/gitlawb/memlawb/memlawb-memory.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 | $0.00104 | $0.01080 |
| Opus 5.5 | $0.00042 | $0.00432 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo ago First seen · 99 lines · 104 tokens per session scan A 8e33286a984b
memlawb-memory is a skill published in the GitHub repository Gitlawb/memlawb (59 stars, last pushed 4mo ago), licensed MIT. It adds 104 tokens to every session and 1,080 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
knowledge-ingest
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forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…