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/M-T-D-N/agentmemory-codex-windowsWrote 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/m-t-d-n/agentmemory-codex-windows/recall)<a href="https://agentmods.dev/commands/m-t-d-n/agentmemory-codex-windows/recall"><img src="https://agentmods.dev/badge/commands/m-t-d-n/agentmemory-codex-windows/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/m-t-d-n/agentmemory-codex-windows/recall"><img src="https://agentmods.dev/badge/commands/m-t-d-n/agentmemory-codex-windows/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 | $0.00205 |
| Opus 5.5 | $0.00000 | $0.00082 |
| Sonnet 5.5 | $0.00000 | $0.00041 |
| Haiku 4.5 | $0.00000 | $0.00020 |
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.
This is a copy
88% identical to recall — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Search past session observations and lessons for relevant context. Wrap the memory_smart_search and memory_lesson_recall MCP tools.
Usage
/recall [query]
Instructions
- Resolve the exact registered project for the current repository.
- Call
memory_smart_searchwith thatproject, the query, andlimit: 10(hybrid BM25 + vector + graph search). - Call
memory_lesson_recallwith the sameproject, query, andlimit: 5(lesson search). - Combine results and present to the user:
- Group by session
- Show type, title, and narrative for each observation
- Highlight high-importance (>= 7) observations
- Show lessons separately with confidence scores
- If no results, suggest 2-3 alternative search terms.
- Never hallucinate results. Only present what the MCP tools actually return.
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 · 21 lines · 0 tokens per session scan A cb5f4fb2430a
recall is a command published in the GitHub repository M-T-D-N/agentmemory-codex-windows (2 stars, last pushed 6d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 205 tokens. A static security scan graded it A with 0 findings. It is 88% identical to recall, differing in 11 lines, and is treated as a copy.
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-web-access
Set up and troubleshoot SuperLocalMemory Web access, the optional link that lets web apps (ChatGPT, Claude on the web, Muse, Composio, other remote MCP clients) use the memory on this computer. Covers turning it on from the dashboard, adding and removing apps, read/save/session permissions, the second yes for bot…
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…