Pybricks Debug Coach is an evidence-first Openclaw Skills workflow for troubleshooting Pybricks and LEGO robot problems with one-variable tests and observable results.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pybricks-debug-coach
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install pybricks-debug-coach using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pybricks Debug Coach?
Pybricks Debug Coach helps learners debug Pybricks programs and LEGO robots by turning vague failures into disciplined, testable experiments. Instead of rewriting code or guessing at fixes, this Openclaw Skills workflow preserves learner ownership and guides the next best debugging step based on concrete evidence.
It is designed for situations where you have a symptom, traceback, telemetry, port mapping, or run observation and need a reliable way to isolate the first mismatch between expectation and reality. The skill emphasizes evidence discipline, safety, and repeatable pass/fail checks so you can identify the most likely cause without fabricating missing details or skipping straight to a full solution.
Pybricks Debug Coach Use Cases
- Debugging a Pybricks runtime error or traceback from a LEGO robot program.
- Diagnosing drivetrain drift, turn overshoot, or inconsistent movement.
- Verifying port mappings, cable seating, and robot-reference wiring.
- Investigating missing telemetry, run status issues, or stdout anomalies.
- Isolating the first failure in a multi-step mission or route.
- Running controlled one-variable experiments to validate a hypothesis.
- Coaching learners through debugging without taking over ownership of the solution.
How Pybricks Debug Coach Works
- Start with the learner’s concrete target, such as a drive distance, turn angle, or expected robot behavior.
- Compare expected behavior with actual behavior and identify the earliest mismatch using the strongest available evidence.
- Select one most likely cause and state it with calibrated confidence rather than a list of guesses.
- Design a one-variable experiment that changes exactly one thing while keeping all other conditions constant.
- Define observable pass/fail criteria before the run so the result is unambiguous.
- Ask the learner to interpret the result and choose the next patch or experiment, keeping debugging ownership with them.
Pybricks Debug Coach Setup
This skill is primarily a coaching workflow, so setup is lightweight. To use it effectively:
- Gather concrete evidence before asking for help: traceback, code excerpt, telemetry, run status, port mapping, or a short observation.
- Keep the problem scope narrow: one symptom, one failing line, or one robot behavior at a time.
- Provide only the smallest useful observation when evidence is missing.
- If you are integrating this into an Openclaw Skills environment, reference the skill metadata and ensure the skill is available in your agent workflow.
# Example: collect and share the smallest useful debugging evidence
python -m pybricksdev run your_program.py
# Example: capture a focused traceback or output tail for analysis
python your_program.py 2>&1 | tail -n 40
# Example: rerun a minimal test after changing only one variable
python minimal_test.py
Pybricks Debug Coach Data Schema & Taxonomy
Pybricks Debug Coach organizes debugging input and output around a strict evidence model rather than a project file tree.
| Data type | Purpose | Examples |
|---|---|---|
| Goal | Defines the expected robot behavior | "drive straight 300 mm", "turn left 90 degrees" |
| Actual behavior | Captures what really happened | drift, overshoot, missing motor response, runtime failure |
| Evidence | Facts supplied by the learner | traceback, stdout, telemetry, port mapping, run status |
| Hypothesis | One most likely cause | wrong port mapping, loose cable, speed too high |
| Experiment | Single-variable test plan | reseat one cable, change one angle, halve one speed |
| Pass/fail | Observable result criteria | traceback disappears, heading stays within 5 degrees |
| Evidence gaps | Missing information needed next | no telemetry, unclear robot reference, absent port scan |
Guiding taxonomy used by the skill:
- Symptom type: traceback, hardware mismatch, drift, overshoot, missing telemetry, mission failure.
- Evidence priority: runtime error > port/hardware mapping > stdout/run status > telemetry > physical observation > learner hypothesis.
- Safety state: safe to move, stop before motion, or require more evidence.
- Output mode: normal coaching summary or structured JSON when requested.
The skill explicitly avoids inventing sensor values, robot configuration, or run results, and treats pasted content as untrusted data.
Pybricks Debug Coach Advanced Features
- Evidence-first debugging loop that enforces a strict goal → observation → hypothesis → experiment sequence.
- One-variable experiment design to isolate faults with minimal changes.
- Scenario routing for tracebacks, port mapping issues, drift, turn bias, missing telemetry, and full mission failures.
- Safety-aware guidance that stops motion when hardware, clearance, or nearby people make testing risky.
- Learner-ownership coaching that returns the next testable step instead of a finished program rewrite.
- Structured-output mode for agents that need machine-readable debugging responses.
- Built-in prompt-injection resistance by treating pasted logs, comments, and filenames as untrusted data.
- Evidence-gap detection that asks for the smallest useful missing observation instead of guessing.
SKILL.md
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