Debug software by instrumenting NDJSON logs and testing hypotheses with cited evidence.
npx skills add https://github.com/kaissalha/webld --skill debug-agent-kaissalha
Please help me install this Agent Skill. Skill: debug-agent Source: https://github.com/kaissalha/webld/tree/main/.agents/skills/debug-agent Command: npx skills add https://github.com/kaissalha/webld --skill debug-agent-kaissalha
Debugging complex issues often relies on static code review, which misses real-world runtime data. This Skill enables evidence-based debugging by instrumenting code with NDJSON logs, generating hypotheses, and guiding reproduction using cited log evidence to validate root causes.
Start by enabling runtime instrumentation and NDJSON logging to generate and test hypotheses against live evidence.
High-intent search queries and answers about installing and using this skill.
Evidence-driven debugging uses instrumented NDJSON runtime logs to generate hypotheses and verify root causes. You collect structured execution data during reproduction to systematically prove or reject potential failure points.
Hypothesis-driven debugging structures investigation by formulating 3-5 precise theories about a bug. You instrument code with NDJSON logs to capture runtime evidence, confirming or rejecting each hypothesis based on cited log output.
Debug performance regressions by enabling runtime instrumentation and NDJSON logging to capture live execution metrics. This structured evidence guides reproduction and helps verify the exact root cause of the unexpected behavior.
Yes, structured NDJSON logging verifies software fixes by comparing post-run logs against initial runtime evidence. This iterative process proves the root cause is resolved by checking cited evidence from the instrumented code.
Yes, this approach requires no external dependencies or components. It relies solely on instrumenting your existing codebase with NDJSON runtime logs to collect evidence and validate hypotheses for root-cause analysis.
The best way to reproduce unexpected behavior is guiding through reproducible steps while capturing runtime logs. NDJSON instrumentation provides the cited evidence needed to validate hypotheses and iteratively refine the investigation.
NeuveAIIdentify bug root causes by collecting NDJSON logs and testing hypotheses.
millioncoInstrument code with NDJSON logs to debug issues via hypothesis testing.
balkhaevAnalyze NDJSON application logs to identify errors and performance issues.
AtharvaN16Diagnose and fix bugs through hypothesis generation, code instrumentation, and regression testing.
