A Claude Code agent skill for hypothesis-driven runtime debugging. 6-phase loop: hypothesize → instrument → reproduce → converge → fix → clean up.
SaferSkills independently audited debug-probe (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
When you hit a bug that reading code can't resolve:
[DIAG_<topic>]Never skip to fixing. Always clean up after.
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 WShare hypotheses with user before touching code.
Rules:
[DIAG_<topic>] (short topic like auth, render, state)// DIAG: remove after debug (adapt comment syntax to language)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.
[DIAG][H1], [DIAG][H2])| 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.
Mandatory. Search for DIAG: remove after debug and:
console.log / print — use the diag buffer pattern~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.