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 | $0.00890 |
| Opus 5.5 | $0.00017 | $0.00356 |
| Sonnet 5 | $0.00008 | $0.00178 |
| Haiku 4.5 | $0.00004 | $0.00089 |
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 7d 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 — 91 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—running,pausedorstopped. 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.raw_retention_hours— raw streams older than this are deleted. Summaries are not; beyond that window the summaries are all there is.
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. Stop there; the summaries are written to answer exactly
this.
Specific questions — "who contacted me", "what did that message say", "which page was I on". The summaries will not carry this. Search the raw streams:
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.
- 7d 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 (1,989 stars, last pushed 3d ago), licensed MIT. It adds 42 tokens to every session and 890 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.
Other skills, from other repositories
AgentMail
Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. [email protected]).
Apple Notes
Open Notes.app and create or update iCloud notes on macOS using memo when possible and AppleScript when direct app automation is more reliable.
Himalaya Email
Work with IMAP email from the terminal using Himalaya for inbox reads, draft review, and carefully confirmed outbound sends.
GitHub PR Workflow
Move cleanly through branch, diff, review, validation, and PR update steps without losing scope or repository-native workflow.
YouTube Content
Fetch YouTube transcripts, summarize videos, and transform the transcript into chapters, notes, threads, or article-ready structure.
CLI
Run 150+ AI apps via inference.sh CLI (infsh) — image generation, video creation, LLMs, search, 3D, social automation. Uses the terminal tool. Triggers: inference.sh, infsh, ai apps, flux, veo, image generation, video generation, seedream, seedance, tavily.