agentleFS
Seeed-ProjectsSeeed-Projects/Seeed-Jetson-DevelopTool/skills/claude/developtool-device/CLAUDE.md

Seeed-Jetson-DevelopTool / developtool-device

CLAUDE.md57 starsChanged 4 months agoMITView on GitHub

Use Seeed Jetson DevelopTool's configured SSH target, camera preview, network-sharing panel and device/robot readings without rediscovering credentials or searching all skills.

The file

We split this CLAUDE.md into the rules it gives an agent

6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run status; run connect if disconnected.

    line 16
  2. Distinctive:

    Do not take a camera away from a robot controller.

    line 34
  3. Distinctive:

    Read-only arm tasks identify the actual model, port, driver and existing owner first.

    line 44
  4. Distinctive:

    Use the pre-matched hardware skill for the actual device:

    line 47
  5. Very specific:

    If a named skill exists only on the Jetson, check exactly ~/.agents/skills/<name>/SKILL.md, ~/.codex/skills/<name>/SKILL.md, …

    line 56
  6. Distinctive:

    Keep a loaded arm's controller alive.

    line 65

≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
CLAUDE.md

DevelopTool device tasks

The current task context supplies an absolute command prefix ending in bridge_client.py --runtime <private manifest>. Run it on the PC; the ssh subcommand executes its argument on the bound Jetson. Use that exact prefix, including its Python executable. Never read or print the private manifest or application configuration: the helper already handles authentication.

Direct routes

  1. Run status; run connect if disconnected. Both use the target already configured in Remote Dev. connect verifies authentication before switching the application's active runner. No configured host: ask the user to configure Remote Dev. An expired/changed runtime: use the new context; do not retry the old target. Auto-execute must be enabled for connect and arbitrary SSH commands.
  2. Execute ssh --command 'hostname; cat /etc/nv_tegra_release' --timeout 30 for device facts. Existing SSH authentication, port and sudo setup are reused. Quote the remote command as one argument; never put passwords in commands.
  3. For networking, use network to open the existing sharing panel. The user selects interfaces and provides host sudo credentials there. Opening it is not sharing success. Verify gateway, public IP, DNS and HTTPS over SSH after configuration. Preserve the existing wired SSH address and original network profile. The matched jetson-pc-wired-share skill describes Linux NM behavior.
  4. For cameras, enumerate /dev/video* and reuse the installed capture stack. camera start --source /dev/video0 opens the native preview. Sources also support RTSP URLs, numeric indexes and an already verified GStreamer pipeline ending in appsink. camera stop stops the preview. Device python3/cv2 is required; report a missing dependency instead of repeatedly guessing sources. Do not take a camera away from a robot controller; reuse its existing stream.
  5. monitor start starts measured CPU, memory, disk, temperature and device readings. monitor stop stops polling. This does not move hardware.
  6. Publish observations with publish --source arm --json '{"joint_1_deg":12.5}'. Replace the illustrative value with a real measurement. Use scalar values and units in field names. Publish after fresh observations; timestamps in the panel identify sample age. Never claim a one-time sample is live polling.

Robot arms and sensors

Read-only arm tasks identify the actual model, port, driver and existing owner first. Prefer the running controller's state endpoint or ROS topic; do not launch a second serial/CAN owner or run calibration/motion as a detection step. Use the pre-matched hardware skill for the actual device:

  • SO-ARM101: soarm-control; use its existing status reader and verified configuration. Local script paths belong to the PC unless the skill is actually installed on the Jetson. For environment setup only, the bundled lerobot-env-setup guide is available.
  • reBot B601: rebot-arm-grasp; use its existing CASCADE MCP session and world_state/observations. Motion goes through its supported MCP controller, never an arbitrary SSH command that bypasses its safety layer.
  • If a named skill exists only on the Jetson, check exactly ~/.agents/skills/<name>/SKILL.md, ~/.codex/skills/<name>/SKILL.md, ~/.claude/skills/<name>/SKILL.md, and ~/.openclaw/workspace/skills/<name>/SKILL.md. Do not scan the entire filesystem.

Publish actual joints, gripper, stop state and sensor values with their source and units. Unsupported values stay unknown. A read request does not authorize motion. For control, follow the user's specified target and the model's verified calibration and limits; no target means ask for it, not a default home/demo. Keep a loaded arm's controller alive. Observe and report execution results using the matching hardware skill's evidence requirements.

Visual commands return queued, which proves delivery only. Check actual frames, readings and controller results before claiming success. Runtime helpers expire when DevelopTool closes. Do not copy manifests or tokens into persistent skills.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.