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/ohad6k/emulo/clawhubnpx skills add ohad6k/emulo --skill clawhubgit clone --depth 1 https://github.com/ohad6k/emuloWrote 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/ohad6k/emulo/clawhub)<a href="https://agentmods.dev/skills/ohad6k/emulo/clawhub"><img src="https://agentmods.dev/badge/skills/ohad6k/emulo/clawhub.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 | $0.00044 | $0.00430 |
| Opus 5 | $0.00022 | $0.00215 |
| Sonnet 5 | $0.00009 | $0.00086 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
emulo 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.
What it actually says
Emulo profile
Load the user's Emulo working profile so you act like them, not a cold start.
Emulo mines the user's own AI coding sessions (Claude Code, Codex, Cursor,
Copilot) into a you.md working profile: their laws, their taste, their voice,
and the failure modes they want you to protect them from.
Use this before working on their task
Run the profile loader and read what it returns before you plan or edit:
emulo --install you.md --target agents
Or load it directly through the MCP server:
uvx emulo mcp
The MCP server exposes one tool, load_emulo_profile, which returns the
profile for the domain you are about to work in (work, design, write, or
video).
What you get
The profile is mined from the user's real sessions, not a questionnaire. It carries:
- Laws they never want broken, each with receipts from real sessions
- Taste in design and code, including what they reject on sight
- Voice for anything written in their name
- Failure modes to protect them from
Privacy
Mining runs locally against session logs already on the machine. The profile is the user's own file. Nothing is uploaded by this skill.
Previously published as ditto-profile. The project was renamed to Emulo in
v0.5.0; see https://github.com/ohad6k/emulo/releases/tag/v0.5.0.
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 · 57 lines · 44 tokens per session scan A f8e65831ba25
emulo is a skill published in the GitHub repository ohad6k/emulo (288 stars, last pushed 11d ago), licensed MIT. It adds 44 tokens to every session and 430 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
deja-search
Search deja before re-deriving past work: when the user refers to earlier sessions or decisions, before debugging an error, and before implementing something that may already exist. It searches this machine's own history across every AI coding tool used on it, going back further than deja itself was installed.
plur-memory
Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains.
deja-history
Search the user's past AI coding sessions. Use when they say things like 'didn't we fix this before', 'what did we decide about X', or before re-debugging an error that may already be solved.
plur-session-end
Extract durable learnings at the end of a session. Saves corrections, preferences, and codebase patterns as engrams — nothing ephemeral, nothing sensitive.
Effective Memory
The essential habits for an AI agent with memory — session bookends, learning triggers, verification, safety, and the operational discipline that turns raw recall into compounding intelligence. Pinned, always-injected.
plur-memory
Your memory stays on your machine. No cloud, no tracking, no API key. PLUR makes your OpenClaw remember — and shares that memory with every other tool you use.