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/OnourImpram/mnemeWrote 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/onourimpram/mneme/recall)<a href="https://agentmods.dev/commands/onourimpram/mneme/recall"><img src="https://agentmods.dev/badge/commands/onourimpram/mneme/recall/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/onourimpram/mneme/recall"><img src="https://agentmods.dev/badge/commands/onourimpram/mneme/recall.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 | $0.00000 | $0.00236 |
| Opus 5.5 | $0.00000 | $0.00094 |
| Sonnet 5.5 | $0.00000 | $0.00047 |
| Haiku 4.5 | $0.00000 | $0.00024 |
Grade A, and why
recall 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.
What it actually says
/mneme:recall
Retrieve a specific past session by id or by date range.
Usage
/mneme:recall session=<session_id>
/mneme:recall from=<YYYY-MM-DD> to=<YYYY-MM-DD>
/mneme:recall # most recent sessions
What it does
Calls the mneme_recall MCP tool. The tool reads only the FTS5 index
and the on-disk markdown files. No LLM call is made. Returns matching
sessions ranked by mtime descending with full markdown bodies when
the include_body argument is true (default).
Use cases
- "What did we decide about authentication last Thursday?"
- "Pull up the session where I sketched the migration plan."
- "List the last five sessions, no bodies, just titles."
Tips
- Combine with
/mneme:primewhen you want a curated bundle instead of raw session text. - The session_id format follows your vault's frontmatter convention.
If you do not know it, prefer
from/todate ranges.
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 · 32 lines · 0 tokens per session scan A 5de818693f18
recall is a command published in the GitHub repository OnourImpram/mneme (3 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 236 tokens. 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 2026-08-31.
Other commands, from other repositories
slm-remember
Capture durable facts, decisions, constraints, and gotchas into SuperLocalMemory. Use when the user says "remember that", "save this decision", "note this constraint", or when a session produces a conclusion worth persisting across sessions. Always recall first to avoid duplicates.
slm-media
Save pictures and PDFs in SuperLocalMemory and find them again by describing them. Covers when to save a picture or document, remembermedia, rememberdocument, getmedia, mediastatus and mediauploadlink, how recall returns pictures and PDF pages, the limits, what stays private, turning the feature on with slm media (16…
slm-session
Manage SuperLocalMemory session lifecycle — call sessioninit once at the start of every fresh session to load relevant project context and get a sessionid; call closesession when work is meaningfully complete to commit temporal summaries. Correct lifecycle hygiene is what makes SLM's learning loop work.
slm-status
Health and optimization stats for SuperLocalMemory — call slmoptimizestats() for live compression and cache counters (compressruns, tokenssavedcompress, cacheproxyhits, cacheproxymisses, cachekvhits, cachekvmisses); run slm status [--json] for system state (mode, profile, DB size, fact/entity/edge counts) and slm…
slm-profile
Memory profiles (separate namespaces inside one SuperLocalMemory store) and MCP tool sets (which tools your host sees). Use switchprofile or slm profile switch to change the active memory profile, the profileid argument to read or write another profile for one call, and slm tools set to choose the tool set every…
slm-bot-memory
Cross-bot memory on a shared computer (Grok Bot, Cursor plugin, any host where several agents share one machine). Explains agentid attribution vs. profile/scope access, how to namespace by agentid + profile + scope, and what must never be written to memory. Read this before the first remember/recall on a new…