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/qualixar/superlocalmemory/slm-recallnpx skills add qualixar/superlocalmemory --skill slm-recallgit 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/skills/qualixar/superlocalmemory/slm-recall)<a href="https://agentmods.dev/skills/qualixar/superlocalmemory/slm-recall"><img src="https://agentmods.dev/badge/skills/qualixar/superlocalmemory/slm-recall.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 | $0.00060 | $0.03110 |
| Opus 5 | $0.00030 | $0.01555 |
| Sonnet 5 | $0.00012 | $0.00622 |
| Haiku 4.5 | $0.00006 | $0.00311 |
Grade A, and why
slm-recall scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
fetch(fact_ids="f8a2bc91,d4c1e203") 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 cc5c344bc8f3
- 2d ago Changed c552102b7874
- 6d ago First seen · 327 lines · 60 tokens per session scan A f2aad575ca22
slm-recall is a skill published in the GitHub repository qualixar/superlocalmemory (224 stars, last pushed 2d ago), licensed AGPL-3.0. It adds 60 tokens to every session and 3,110 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
engraphis-memory
Give the agent durable, scoped, explainable memory across sessions and repositories through the Engraphis MCP tools. Use when you learn a convention, decision, bug cause/fix, or user preference worth keeping; when prior context would help before you answer or act (to avoid re-asking or re-deriving); when asked "why is…
talamus-memory
Set up and use Talamus as durable, local-first memory for AI agents. Use when an agent needs to initialize or diagnose a Talamus brain, ingest files or repositories, retrieve cited context across sessions, inspect note history, verify memory against sources, review proposed corrections, connect Talamus through MCP, or…
recap
Summarize the last N agent sessions for the current project, grouped by date, with highlight observations per session. Use when the user asks "recap", "what have we been doing", "today", "this week", or wants a rollup of recent work.
remember
Save an insight, decision, or learning to agentmemory's long-term storage with searchable concept tags. Use when the user says "remember this", "save this", "note that", "don't forget", or wants to preserve knowledge for future sessions.
recall
Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions.
agentmemory-rest-api
The agentmemory HTTP REST API surface, the primary protocol for talking to the memory server. Use when calling agentmemory over HTTP, when MCP is unavailable and you need a fallback, or when integrating a host that does not speak MCP.