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/m-t-d-n/agentmemory-codex-windows/recallnpx 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.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 | $0.00061 | $0.00572 |
| Opus 5 | $0.00030 | $0.00286 |
| Sonnet 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 5d 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 — 3 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.
- 5d 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 5d ago), licensed Apache-2.0. It adds 61 tokens to every session and 572 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to recall, differing in 3 lines, and is treated as a copy.
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…
slm-recall
Search and retrieve facts, decisions, and past context from SuperLocalMemory. Use when the user asks to recall, find, search, or "what did we decide/say about X". Triggers multi-channel semantic retrieval with reranking; always call before storing anything new.
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-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-compress
Compress large text, tool output, or transcripts to reduce context-window usage while keeping the full 1M window intact — call slmcompress(content, mode, reversible, ttlseconds) to shrink content; if the result is lossy a ccrid is returned so you can call slmretrieve(ccrid) later to recover the exact original; always…
slm-scope
Controls memory visibility across profiles — personal (private, default), shared (selected profiles), or global (all profiles on this machine). Default is always personal. Only change scope when the user explicitly asks to share a memory across workspaces. Works with both remember (write scope) and recall (read scope…