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zelixag

Debug Probe

by Bio · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install debug-probe
Description
Provides a precise six-phase hypothesis-driven runtime debugging process with minimal instrumentation to identify and fix root causes of unexpected behavior.
README (SKILL.md)

Debug Probe

Quick Start

When you hit a bug that reading code can't resolve:

  1. Hypothesize — Read source, generate 2-4 falsifiable hypotheses
  2. Instrument — Insert minimal logging (2-4 points per hypothesis), tag [DIAG_\x3Ctopic>]
  3. Collect — Build → user reproduces → user exports logs
  4. Converge — Match logs to hypotheses → confirm root cause
  5. Fix — Minimal fix → verify with user
  6. Clean up — Remove ALL instrumentation, confirm build passes

Never skip to fixing. Always clean up after.

The 6 Phases

Phase 1: Hypothesize

Read relevant source code. Generate 2-4 testable hypotheses:

[H1] Root cause may be X → if true, log would show Y
[H2] Root cause may be Z → if true, log would show W

Share hypotheses with user before touching code.

Phase 2: Instrument

Rules:

  • Only instrument to test hypotheses — no fishing expeditions
  • Tag format: [DIAG_\x3Ctopic>] (short topic like auth, render, state)
  • 2-4 instrumentation points per hypothesis
  • Mark ALL temporary code: // DIAG: remove after debug (adapt comment syntax to language)
  • Set up a diagnostic buffer (pick from TEMPLATES.md)

Use diagLog('H1', 'key=val', ...) — outputs to both console and an in-memory buffer so users can export all logs at once after reproducing the bug.

Phase 3: Collect

  1. Build & deploy
  2. User reproduces the bug
  3. User exports logs (dump function, console output, log file, etc.)
  4. Group logs by hypothesis tag ([DIAG][H1], [DIAG][H2])
  5. If expected paths aren't hit → is instrumentation on the right branch? → adjust and rebuild

Phase 4: Converge

Situation Action
Logs confirm a hypothesis Confirmed root cause → Phase 5
All hypotheses refuted New hypotheses from log clues → Phase 2
Insufficient data More precise instrumentation → Phase 2

Max 2-3 iterations before escalating.

Phase 5: Fix & Verify

  1. Minimal fix targeting confirmed root cause
  2. Build → user verifies fix works
  3. Fix fails → keep key instrumentation, return to Phase 1
  4. Fix works → Phase 6

Phase 6: Clean Up

Mandatory. Search for DIAG: remove after debug and:

  1. Remove all temporary instrumentation code
  2. Remove all diagnostic imports
  3. Remove diagnostic buffer file if no longer referenced
  4. Build to confirm compilation passes
  5. Tell user: instrumentation removed, only fix remains

Anti-Patterns

  • ❌ Skip hypotheses, jump straight to "fixing"
  • ❌ Instrument 10+ points — precision beats coverage
  • ❌ Dump entire objects — signal drowns in noise
  • ❌ Forget Phase 6 cleanup — instrumentation rots
  • ❌ Claim "done" without user verification
  • ❌ Use raw console.log / print — use the diag buffer pattern
Usage Guidance
Review before installing, especially globally. Use it on a clean branch and in development or staging, require confirmation before adding instrumentation or deploying, avoid logging secrets or personal data, inspect any exported logs before sharing them, and verify all DIAG-marked code and dump hooks are removed after debugging.
Capability Assessment
Purpose & Capability
The skill's purpose is coherent: hypothesis-driven runtime debugging using temporary instrumentation, log collection, fixes, and cleanup. The sensitive capabilities are mostly purpose-aligned, but they include source modification and runtime data capture.
Instruction Scope
The trigger language is broad, including generic terms such as debug, bug, broken, not working, and investigate, while the workflow can lead to code edits and log export without requiring explicit user confirmation before instrumentation.
Install Mechanism
The package contains only markdown skill files and templates, with no executable installer, dependencies, or hidden scripts. The README does describe global installation, which would make the broad triggers apply more widely.
Credentials
The templates expose buffered diagnostic dumps through browser globals, REPL/signal hooks, or HTTP/debug commands, and examples include user/auth-related values without strong redaction or development-only guidance.
Persistence & Privilege
There is no evidence of privilege escalation, background persistence, or exfiltration. Temporary diagnostic files and dump hooks may remain until cleanup, although the skill does emphasize mandatory removal.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install debug-probe
  3. After installation, invoke the skill by name or use /debug-probe
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: hypothesis-driven runtime debugging skill with 6-phase loop. Supports TS/JS/Python/Go.
Metadata
Slug debug-probe
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Debug Probe?

Provides a precise six-phase hypothesis-driven runtime debugging process with minimal instrumentation to identify and fix root causes of unexpected behavior. It is an AI Agent Skill for Claude Code / OpenClaw, with 64 downloads so far.

How do I install Debug Probe?

Run "/install debug-probe" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Debug Probe free?

Yes, Debug Probe is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Debug Probe support?

Debug Probe is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Debug Probe?

It is built and maintained by Bio (@zelixag); the current version is v1.0.0.

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