OpenLoomi is an open-source desktop AI coworker that connects work tools, gathers context, and highlights decisions or actions needing attention. It is for people managing work across multiple apps, and its catalogue add-ons extend the resident desktop for agent frameworks such as Claude Code, Codex, OpenCode, Hermes, and OpenClaw.
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 melandlabs/openloomi --skill create-taskgit clone --depth 1 https://github.com/melandlabs/openloomiWrote 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/melandlabs/openloomi/create-task)<a href="https://agentmods.dev/skills/melandlabs/openloomi/create-task"><img src="https://agentmods.dev/badge/skills/melandlabs/openloomi/create-task/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/melandlabs/openloomi/create-task"><img src="https://agentmods.dev/badge/skills/melandlabs/openloomi/create-task.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 | $0.00071 | $0.04037 |
| Opus 5.5 | $0.00028 | $0.01615 |
| Sonnet 5.5 | $0.00014 | $0.00807 |
| Haiku 4.5 | $0.00007 | $0.00404 |
Grade A, and why
create-task 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 1mo 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 — 482 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Task
Create a complete, runnable Continual Learning Bench task under src/tasks/<task_name>/.
This skill is intentionally interactive. The goal is not merely to write code; it is to give the human repeated, concrete chances to notice design bugs before they become benchmark bugs.
Non-Negotiables
- The user must provide a task description. If missing, ask for it first.
- Ask questions one at a time.
- Ask only high-signal questions that collapse major ambiguity: latent learnable structure, scoring, interaction mode, feedback, debug gain, task name, or model access.
- For every question, provide your recommended answer.
- If a question can be answered by exploring the codebase, explore the codebase instead of asking.
- The task name is public CLI/API surface. Infer a candidate if useful, but require explicit user confirmation before scaffolding.
- The debug model must be decided up front. Ask once which model to use for required
iclandicl_notepadruns; the user may explicitly choose system defaults. - Run provider preflight immediately after description/model selection. Missing provider/model access is a hard fail.
- Do not implement until the user explicitly approves the prompt, action, observation, debug-gain, and implementation plan gates.
- User-facing action spaces are literal JSON payload shapes and examples. Do not show Pydantic or JSON Schema unless explicitly requested.
- Prompt and observation templates live in
src/tasks/<task_name>/prompts.pyas Jinja templates rendered withStrictUndefined. - Hidden ground truth, answer keys, scoring logic, and generators do not belong in
prompts.py. - Behavioral E2E tests go in
tests/tasks/<task_name>/and exercise the public task lifecycle. - No separate implementation report. The task
README.mdis the durable design + results artifact. - The task
README.mdmust followreferences/readme-standard.md; do not improvise a new report shape.
Hard STOP Conditions
Stop and ask the user / report the fix before continuing when any of these occur:
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.
- 1mo ago First seen · 482 lines · 71 tokens per session scan A 51868f1a8763
create-task is a skill published in the GitHub repository melandlabs/openloomi (1,038 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 71 tokens to every session and 4,037 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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