fix_skill

This skill automates and guides bug fixing across modes, optimizing root-cause analysis and verification to accelerate reliable resolutions.
  • Python

0

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill jjuidev/jss --skill fix

  • SKILL.md3.8 KB

Overview

This skill always activates before fixing any bug, error, test failure, CI/CD issue, type error, lint, log error, UI issue, or code problem. It provides an end-to-end, disciplined workflow that selects an operating mode, performs deep debugging, assesses complexity, implements fixes, verifies impact, and finalizes changes with a confidence score. The skill coordinates specialized subagents and parallel exploration to reduce blind spots and speed resolution.

How this skill works

On invocation the skill selects a mode (autonomous, human-in-the-loop review, or quick) and runs a mandatory debug phase that enumerates and tests root-cause hypotheses. It classifies issue complexity (simple, moderate, complex, or parallel) to choose an appropriate workflow and activates required subagents (debugger, researcher, planner, code-reviewer, tester, Bash, and parallel Explore agents). After implementing fixes it runs verification, searches for related risks, produces a summary with confidence score, and can request commits or documentation updates.

When to use it

  • Before attempting any fix to ensure consistent, auditable workflow
  • When a failing test, CI job, or lint/type error appears
  • For production or critical code where human review may be required
  • On multi-file or system-wide bugs that need coordinated investigation
  • When you want automated verification and a confidence score

Best practices

  • Always run in autonomous mode for trivial issues and quick mode for type/lint fixes
  • Choose human-in-the-loop for production-impacting or safety-critical changes
  • Use parallel Explore/Bash agents to validate multiple hypotheses simultaneously
  • Require full verification and related-code scouting before finalizing changes
  • Report confidence, files changed, and test results in the unified step format

Example use cases

  • A failing CI pipeline due to a recent dependency upgrade — detect, isolate, and propose rollback or patch
  • A unit test regression after refactor — find root cause, implement minimal fix, and re-run tests
  • Type errors from static analysis — quick mode to correct annotations and rerun type checks
  • Flaky production logs showing intermittent exceptions — deep investigation and preventative validation
  • UI rendering bug affecting multiple components — classify as complex and coordinate design-aware fixes

FAQ

Autonomous is the default for simple or moderate issues; use human-in-the-loop for critical production changes and quick for trivial type/lint fixes.

Which subagents are always activated?

The debug skill is always activated. Other subagents (problem-solving, planner, code-reviewer, tester, Bash, Explore) are activated conditionally based on complexity.

Built by
VeilStrat
AI signals for GTM teams
© 2026 VeilStrat. All rights reserved.All systems operational