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 M-T-D-N/agentmemory-codex-windows --skill recallgit 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/skills/m-t-d-n/agentmemory-codex-windows/recall)<a href="https://agentmods.dev/skills/m-t-d-n/agentmemory-codex-windows/recall"><img src="https://agentmods.dev/badge/skills/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/skills/m-t-d-n/agentmemory-codex-windows/recall"><img src="https://agentmods.dev/badge/skills/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 | $0.00061 | $0.00572 |
| Opus 5.5 | $0.00024 | $0.00229 |
| Sonnet 5.5 | $0.00012 | $0.00114 |
| Haiku 4.5 | $0.00006 | $0.00057 |
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 — 28 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
The user wants to recall past context about: $ARGUMENTS
Quick start
memory_smart_search { "project": "my-project", "query": "jwt refresh token rotation", "limit": 10 }
Expected output:
2 results across 2 sessions.
[importance 8] decision · "Rotate refresh tokens on every use" (session 7f3a9c21)
[importance 5] code · "limit.ts counts per-IP" (session b21d004e)
Why
Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.
Workflow
- Call
memory_smart_searchwith the user's text asqueryandlimit: 10. Passprojectwhen the user scopes to a specific repo. - Group results by session. Records carry a provenance channel (
user,agent,tool,import,shared); when results conflict, preferuseroveragentinference, and flagsharedrecords as another teammate's write. - For each observation show its type, title, and narrative.
- Lead with the high-signal observations (importance >= 7).
- If zero results, suggest 2-3 alternative search terms and stop. Do not guess.
Anti-patterns
WRONG: results are empty, so you write "We probably discussed token expiry last week" from assumption.
RIGHT: "No memories matched that query. Try refresh token, session expiry,
or auth rotation."
Checklist
- Every observation shown came from the tool response.
- Project scope is exact, or
*was an explicit cross-project choice. - Results grouped by session, high-importance first.
- Empty results trigger alternative-term suggestions, not invention.
- No session id or score was paraphrased or rounded.
See also
remember: the write side; recall retrieves what it stores.recap,handoff,session-history: session-scoped views of the same data.memory-discipline: when to run this search unprompted.
Troubleshooting
See ../_shared/TROUBLESHOOTING.md if memory_smart_search is not available.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 65 lines · 61 tokens per session scan A 5bafc4016e1a
recall is a skill published in the GitHub repository M-T-D-N/agentmemory-codex-windows (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 61 tokens to every session and 572 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to recall, differing in 28 lines, and is treated as a copy.
Other skills, from other repositories
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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…
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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…
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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…