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nexpath

hi0001234d · hi0001234d/nexpath

Helps your AI coding agent catch missing tests, safety checks, and maintainability issues by enhancing prompts and providing guidance.

Local-first AI coding workflow for vibe coders, indie hackers, technical founders and product managers — catch missing tests and safety checks across Claude Code, Cursor, Windsurf, Replit, Lovable and Bolt.new.

355 stars16License: Apache-2.0Updated 17 days ago0 opens

Preview

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What you can do with it

  • Ensure AI-generated code includes proper testing and safety checks before deployment.
  • Improve the maintainability of agent-generated code by catching potential issues early.
  • Guide AI agents to follow mature development flows, reducing the risk of unverified changes.

Safety check

Safe

No risky patterns found in the files we scanned.

Automated scan of the repo files. Always review code you run.

From the README

Nexpath CLI — Build Fast. Ship with Confidence.

A local-first behaviour guidance layer that helps builders catch missing checks before AI-generated work becomes shipped risk.

AI coding agents make it easy to move fast. Nexpath helps reduce the chance that speed quietly becomes an unverified bug, an unsafe change, an unmaintainable codebase, or hours of manual review later.

Prompt Enhancement — A Practical Twist

Within Nexpath's broader behaviour-guidance vision, Prompt Enhancement is the first feature we're introducing as a practical twist: it saves vibe coders from writing every prompt detail by hand while preserving their original intent. When a task needs more rigour, it can suggest missing development practices, verification, or confirmation steps — encouraging users to give a mature development flow the time it needs to finish.

Demo: Prompt Enhancement in action, on YouTube


Why Nexpath Exists

  • AI can introduce a bug without making the risk obvious.
  • Fast generation can leave a codebase harder to maintain after every change.
  • Security, rollback, regression, and acceptance checks are easy to miss while momentum is high.
  • Reviewing every AI-generated decision manually can cost more time than the generation saved.

Nexpath reduces these risks by noticing what the current workflow is missing and bringing the relevant check into the work at the right moment. It does not replace developer judgement or promise bug-free code; it helps make that judgement faster, more structured, and harder to skip.


A Workflow, Not Another Tool

  1. Build with your AI coding agent — Work in the tools and flow you already use.
  2. Review with Nexpath — Let the Prompt Quality Layer surface missing verification, safety, scope, or maintenance steps while the context is still fresh.
  3. Ship confidently — Move forward after the important checks are visible, reviewed, and included in the task.

Nexpath is one focused step inside a better shipping workflow — not a new place where development has to happen.

Built during AI Hackfest 2026 by MLH.


The Prompt Quality Layer — How It Works

  1. Understand — Nexpath reviews the prompt, the current development stage, and relevant workflow signals while preserving the complete original request.
  2. Structure — It builds one editable prompt with the sections the task needs, such as scope, constraints, acceptance expectations, verification, or missing-practice guidance.
  3. Safeguard — Higher-risk work can receive confirmation, safety, rollback, or evidence requirements. Complex work can also receive a sequence-aware breakdown when multiple prompts would be more effective.
  4. Review — You inspect and edit the result before sending it, or return to the original prompt. Nexpath provides the quality layer; you keep the final decision.

Benchmarked on SWE-bench Verified

Prompt quality is easy to demonstrate and hard to prove. To check whether Nexpath actually changes what a coding agent produces, we ran it against a public benchmark where the answer is graded by each project's own test suite — not by a person, and not by another model.

Setup: Claude Code (sonnet) attempted 40 tasks from SWE-bench Verified twice — once on its own, once with the Nexpath-reviewed prompt. The task, starting commit, container, model, time limit, and tools were identical in both runs. The prompt is the only thing that changed. Grading used the official SWE-bench harness.

Claude CodeClaude Code + Nexpath
Tasks solved27/40 (67.5%)29/40 (72.5%)
Issue's own tests pass27/40 (67.5%)29/40 (72.5%)
Ran the project's tests before submitting38/40 (95.0%)39/40 (97.5%)

Paired outcomes: solved by both — 27 · solved only with Nexpath — 2 · solved only without Nexpath — 0 · solved by neither — 11

Full results

Every number above traces to a file below. The full benchmark report (Nexpath_benchmark/README.md) has the method, the two tasks Nexpath won, and the eleven neither run could solve. These are the raw files behind it — all 40 tasks, both runs, and the list we locked before the first run.

Complete reportBenchmark report
Every task, both runsfinal_per_task.csv
All measurementsfinal_summary.json
Task list and settings, locked before the runstrat40-manifest.json
Prompt checks run before startingpreflight-strat40.json
Raw data for the confirmation-line testbefore · after

How Nexpath Reduces Risk

Fewer Silent Bugs and Missed Checks

  • Verification and test expectations can be added for debugging, maintenance, planning, review, and other tasks that need proof of completion.
  • Absence signals can surface missing regression checks, acceptance criteria, reproduction evidence, or project grounding.
  • Weak or unrelated signals are not used as filler; guidance must match the current task. The core interaction keeps your request and the added workflow guidance together:

Safer High-Risk Changes

  • Sensitive actions can receive explicit confirmation, rollback, backup, or safety requirements before the reviewed prompt is used.
  • Double confirmation, cross-confirmation, and final confirmation are used only when the task's complexity or risk justifies them.
  • The quality layer does not silently expand authority, auto-send work, or treat an agent response as proof of completion.

A More Maintainable Development Flow

  • Scope, constraints, acceptance expectations, affected surfaces, and behaviour-preservation needs stay visible instead of bein
View source on GitHub