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llm-debugger-vscode-extension

mohsen1/llm-debugger-vscode-extension

VSCode extension that debugs by running code, not reading it.

Agent Decisions1.00Runner-up: Classification & RoutingQuestionnaire
Automated README summary

Automatically summarized from the README on 14 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

A research proof-of-concept VSCode extension for developers that finds bugs by driving the real debugger — breakpoints, stepping, live values — instead of reading static source. It is not a supported product.

How it uses Jev

At each debugger pause, Jev routes the next action (step, continue, break, evaluate) from the paused state — stack, locals, source, output, history — and says whether evidence is sufficient. The generation model is only woken for hypotheses, breakpoint locations, expressions, and the final patch.

Primitives:choicescore

Technique worth stealing

Route each debugger step with a typed classifier over paused runtime state; reserve generation calls for locations and expressions.

Try it

cp api.env.example api.env, add keys, npm run build, install from VSIX, then use @debugger in chat.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectFound in material0.83
  • Shows a System One patternFound in material0.87
  • Describes handling uncertaintynot found in the excerpt0.05
  • Reports measured numbersnot found in the excerpt0.23
  • Is a runnable toolFound in material0.96
  • Top-10 learning shortlist signalunclear0.48
  • Is a replica of the modelnot found in the excerpt0.03
  • Solves a common problem (0–2)score on a 0–2 scale1.07 / 2
  • About JevFound in material0.96
Signals
Jev is core to the project0.83Shows a System One pattern0.87Describes handling uncertainty0.05Reports measured numbers0.23Is a runnable tool0.96Top-10 learning shortlist signal0.48Is a replica of the model0.0300.51Solves a common problem (0–2)1.07 / 2012

no-judgment band (0.40–0.60)

Design patterns
Questionnaire0.97Funnel0.68Ghostwriter0.66Check-up0.45Blind review0.40Viewfinder0.2600.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 14 Oct 2026.