ai-memory is a shared long-term memory system for coding agents that preserves project knowledge, unfinished work, failed approaches, and open questions across tools and machines. It is used by individual developers and teams to hand work between different coding agents and continue projects without repeating the context.
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 akitaonrails/ai-memory --skill ai-memory-retrievalgit clone --depth 1 https://github.com/akitaonrails/ai-memoryWrote 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/akitaonrails/ai-memory/ai-memory-retrieval)<a href="https://agentmods.dev/skills/akitaonrails/ai-memory/ai-memory-retrieval"><img src="https://agentmods.dev/badge/skills/akitaonrails/ai-memory/ai-memory-retrieval/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/akitaonrails/ai-memory/ai-memory-retrieval"><img src="https://agentmods.dev/badge/skills/akitaonrails/ai-memory/ai-memory-retrieval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00065 | $0.02500 |
| Opus 5.5 | $0.00026 | $0.01000 |
| Sonnet 5.5 | $0.00013 | $0.00500 |
| Haiku 4.5 | $0.00006 | $0.00250 |
Grade A, and why
ai-memory-retrieval 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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-memory retrieval
Use this skill for read-only ai-memory lookups, catch-up, and evaluating remembered project knowledge before you design, debug, or edit.
Tools in this cluster
memory_querysearches the current project's wiki for prior decisions, gotchas, procedures, rules, and session notes.memory_recentlists the most recently updated pages when the user wants a light activity check.memory_read_pagefetches a full page body after a search hit or direct path lookup. Passinclude_related: true(optionalrelated_depth, default 1, hard cap 3) to also walk the link graph outward and return arelatedarray of reachable pages, each with its hopdepthand edgedirection(link/backlink); default off omits it.memory_read_session_observationsreads one session's raw hook observations (prompts, tool calls, stops) in capture order, paged and body-capped, when the user asks what actually happened in a session or wants to check a compiled page against its evidence. It also continues a session that was cut off without ending: pass thesession_ida session-start notice,recent_sessionsornewer_unended_sessionnames (a harness's native id, such as an OpenCodeses_…id, works too) withorder: "desc", and its newest prompts and tool results are where the work stopped.memory_statusreports whether ai-memory is healthy and how large the knowledge base is.memory_briefingreturns a structured read-only snapshot for agent consumption, including a boundedpinnedlist of the project's pinned standing-context pages (present only when the project has pins) andrecent_sessions, the sessions with the newest captured work. An entry there withended: falseandhanded_off: falsethat is notcurrentwas cut off or is still running; read it before an older handoff or page when the user asks to continue.memory_explorereturns a prose digest when the user asks for an open-ended catch-up.
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 Changed · +8 lines 6230a240ff72
- 20d ago Changed 3866f9a8bc88
- 1mo ago Changed · +37 lines 819d0682a3a8
- 1mo ago Changed · +5 lines 838eed977750
- 1mo ago First seen · 85 lines · 65 tokens per session scan A 31de3bbcc3ff
ai-memory-retrieval is a skill published in the GitHub repository akitaonrails/ai-memory (13,818 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 2,500 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
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