Memmy is a local memory hub that gives multiple AI agents one shared, persistent context about their user. It helps people continue work across agents such as Claude Code, Codex, OpenClaw, and Hermes Agent. The catalogue entries are skills for using Memmy with supported agents.
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 MemTensor/memmy-agent --skill computer-historygit clone --depth 1 https://github.com/MemTensor/memmy-agentWrote 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/memtensor/memmy-agent/computer-history)<a href="https://agentmods.dev/skills/memtensor/memmy-agent/computer-history"><img src="https://agentmods.dev/badge/skills/memtensor/memmy-agent/computer-history/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/memtensor/memmy-agent/computer-history"><img src="https://agentmods.dev/badge/skills/memtensor/memmy-agent/computer-history.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 | $0.00042 | $0.01997 |
| Opus 5.5 | $0.00017 | $0.00799 |
| Sonnet 5.5 | $0.00008 | $0.00399 |
| Haiku 4.5 | $0.00004 | $0.00200 |
Grade A, and why
computer-history 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 23d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer History
Computer History keeps a local record of the user's desktop activity: readable per-window summaries, and the raw event streams those summaries were written from. They are two different things and answer different questions.
When to use
Use this skill when the user asks about their own recent activity — "who contacted me today", "what was I working on", "where did I leave off", "what did I do this morning" — or refers to Computer History directly.
First, find out where the data is and whether it is fresh
Call computer_history_status. It returns:
state— the recorder lifecycle, includingrunning,paused,stoppedandfailed. If it is stopped and the user expects today's activity, say so rather than reporting an empty result as if nothing happened.summary_directory— the readable summaries.event_stream_root_path— the raw per-segment event streams.privacy.raw_retention_hours— raw streams older than this are deleted. Summaries are not; beyond that window the summaries are all there is.capture_health— current recorder signals and known limitations. A running recorder, a heartbeat or a healthy status does not prove that readable screen content was captured for the application and interval being investigated.
Compare the current date against what you find before treating anything as today's activity.
The two layers
<summary_directory>/
<segment id>-10min-summary.md one window, readable
<6h window id>-6h-summary.md a half-day, rolled up from the 10min ones
<event_stream_root_path>/
<segment id>/
events.jsonl every observed event, one JSON object per line
metadata.json when the window started
Segment ids are UTC and aligned to the ten-minute grid, so
2026-09-08T08-20-00Z covers 08:20–08:30 UTC. Convert to the user's local time
before reporting anything back to them.
How to answer
Broad questions — "what was I doing this afternoon", "what have I been
working on". Read the 6h summaries first, then the 10min ones for a window
that looks relevant. These may suffice for an overview. If the user asks for
detail, verification or an explanation of missing activity, inspect the raw
records before drawing a conclusion. For activity just performed, read the
active segment and adjacent UTC segments even if no summary exists yet.
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.
- 23d ago Changed · +76 lines e1c96116ce89
- 1mo ago First seen · 91 lines · 42 tokens per session scan A 46f5030713cd
computer-history is a skill published in the GitHub repository MemTensor/memmy-agent (2,212 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,997 once invoked, about $0.0002 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 2026-09-21.
computer-history compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| computer-historyMemTensor/memmy-agent | Skill | 2.2k | today | MIT | A | 42 | Claude Code, Codex |
| AgentMailagentic-in/elephant-agent | Skill | 585 | 2mo ago | none | A | 40 | Claude Code, Codex |
| Apple Notesagentic-in/elephant-agent | Skill | 585 | 2mo ago | none | A | 31 | Claude Code, Codex |
| Himalaya Emailagentic-in/elephant-agent | Skill | 585 | 2mo ago | none | A | 29 | Claude Code, Codex |
| YouTube Contentagentic-in/elephant-agent | Skill | 585 | 2mo ago | none | A | 28 | Claude Code, Codex |
| GitHub PR Workflowagentic-in/elephant-agent | Skill | 585 | 2mo ago | none | A | 29 | Claude Code, Codex |
| CLIagentic-in/elephant-agent | Skill | 585 | 2mo ago | none | C | 71 | Claude Code, Codex |