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plesty-hub-agents

Skills and agent roles for building PLESTY lab experiments and the hub modules behind them — device drivers, analyzers and experiments — with the coding agent you already use.

Tell it which instruments you have and what you want to measure. It writes the device servers, field-tests them on your hardware until they pass, builds the experiment, runs it with you on the bench, and then improves it run by run — asking you before every step that touches the hardware and before every new round.

Install

/plugin marketplace add https://gitlab.com/plesty/ai/plesty-hub-agents.git
/plugin install plesty-hub-agents@plesty

The skills appear as /plesty-hub-agents:hub-experiment, /plesty-hub-agents:hub-comply, … and the roles as subagents. /plugin update brings in each release. For the MCP tools (plesty check, GitLab and PyPI look-ups) also install the CLI:

uv tool install "plesty-hub-agents[mcp] @ git+https://gitlab.com/plesty/ai/plesty-hub-agents"
hub-agents doctor        # uv, git, glab, Python — installs what it can, never with sudo

Claude app (claude.ai, Claude Desktop) — as skills

Zip a skill folder and upload it under Settings → Capabilities → Skills:

git clone https://gitlab.com/plesty/ai/plesty-hub-agents.git && cd plesty-hub-agents/skills
zip -r hub-experiment.zip hub-experiment

The app has no access to your lab network or your checkouts, so use it for the stages that are conversation and analysis — requirements, the plan, reading a finished run and proposing the next round. Building, field-testing and running happen in Claude Code (see the experiment guide).

Codex CLI, Gemini CLI — through the CLI

hub-agents workspace init ~/plesty-hub    # one directory for all modules; projects every harness on PATH
cd ~/plesty-hub && codex                  # or gemini — or claude, if you do not use the plugin

Build an experiment

/plesty-hub-agents:hub-experiment pow-pl
Stage What happens You
1 Requirements Q&A: each device (model, role, bench, connection, manual, safety limits), then the experiment (plan, records, in-run decisions, success) answer, confirm
2 Plan what each device module and the experiment need, what runs in parallel, the risks approve
3 Devices one agent per device, in parallel; then the hardware set-up and a field-test loop — find the cause, fix, test again — until each device passes connect the hardware, say when it is free to act
4 Experiment the experiment module, a dry run and a full run against the device simulators review the MR
5 Run you run it from the bench console with the agent monitoring, or the agent runs it pick one
6 Refine efficiency, parameters, in-run decisions, data quality, robustness — a proposal for the next round approve each round

The pipeline keeps its state in .hub-agents/experiments/<slug>/, so it resumes where it stopped. That folder holds bench addresses and lab notes and is never committed.

What ships

Skill Does
hub-experiment instruments to a successful, improving experiment — the six stages above
hub-module a new device module: architect → engineer → publisher
hub-field-test configure a device's field test, or run it on the hardware
hub-comply plesty check → triage → fix → repeat until every gate passes
hub-develop branch off the right line → implement → comply → commit → MR
hub-docs audit a module's docs/ against its code and fix it
hub-publish release merge, tag, CI, PyPI, merge back
hub-workspace clone / status / update checkouts by name, path or group

Roles hub-architect → hub-engineer → hub-publisher hand off through .hub-agents/handoff/*.md. Branch guards stop an edit or a push on main, exp or a maintain/* line. Skills are validated for plesty-lib >=0.6.0,<0.7 and plesty-sdk >=0.5.0,<0.6; a module pinned outside that range is told so.

Documentation

Develop

uv sync --all-extras
uv run pytest            # catalog, plugin manifests, contracts against the installed lib/sdk
uv run plesty check      # quantum, every gate

Changes land through a merge request into exp; main moves only by release.

License

LGPL-3.0-or-later, see LICENSE.

Metadata

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