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/qualixar/superlocalmemoryWrote 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/commands/qualixar/superlocalmemory/slm-mesh)<a href="https://agentmods.dev/commands/qualixar/superlocalmemory/slm-mesh"><img src="https://agentmods.dev/badge/commands/qualixar/superlocalmemory/slm-mesh/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/commands/qualixar/superlocalmemory/slm-mesh"><img src="https://agentmods.dev/badge/commands/qualixar/superlocalmemory/slm-mesh.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 | $0.00150 | $0.03570 |
| Opus 5.5 | $0.00060 | $0.01428 |
| Sonnet 5.5 | $0.00030 | $0.00714 |
| Haiku 4.5 | $0.00015 | $0.00357 |
Grade A, and why
slm-mesh 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 today.
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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- today Changed 90697dbecb7b
- yesterday Changed feeae221aa67
- 2d ago Changed 40b9e58265d4
- 3d ago Changed 82dbaed6e4a5
- 4d ago Changed · +100 lines · +63 tokens per session fd9ff77306d9
- 8d ago Changed · +5 lines · +20 tokens per session dca9249377c3
- 10d ago Changed 330691064ef3
- 12d ago Changed 71d0ad872ff1
- 14d ago Changed a67408e7695c
- 1mo ago First seen · 286 lines · 67 tokens per session scan A 70400088775c
slm-mesh is a command published in the GitHub repository qualixar/superlocalmemory (249 stars, last pushed today), licensed AGPL-3.0. It adds 150 tokens to every session and 3,570 once invoked, about $0.0006 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 2026-09-13.
Other commands, from other repositories
recall
Search past session observations and lessons for relevant context. Wrap the memorysmartsearch and memorylessonrecall MCP tools.
remember
Explicitly save an insight, decision, or learning to agentmemory for future sessions. Wraps the memorysave MCP tool.
tree-ring-dox-sync
Preview and synchronize DOX-style AGENTS.md guidance as source-linked Tree Ring memory.
tree-ring-certify
Generate Tree Ring harness or recall-quality evidence without confusing it with the full framework release suite.
scorecard
Run a repeatable weekly health check. Score Brain OS out of 100. Compare week-over-week.
prime
Inject preflight context for the current task into the conversation.