qa-implementation-feedback-loop

Build, test, scan code, and generate remediation reports for US-* stories.

Updated Mar 22, 2026
One-click install
npx skills add https://github.com/FernanSuoza/AIDD-project-bootstrap --skill qa-implementation-feedback-loop
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qa-implementation-feedback-loop
Source: https://github.com/FernanSuoza/AIDD-project-bootstrap/tree/main/templates/skills/qa-implementation-feedback-loop
Command: npx skills add https://github.com/FernanSuoza/AIDD-project-bootstrap --skill qa-implementation-feedback-loop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates rapid, local validation of implementation work by running build, test, static analysis, and vulnerability checks and producing actionable remediation guidance so developers can catch and fix issues before they compound or block delivery.

Core Features & Use Cases

  • Phased validation: Executes build, unit tests, static analysis, and dependency/vulnerability scans in a prioritized order to surface the most likely failures first.
  • Safe auto-fixes: Optionally applies deterministic, low-risk fixes (formatters, linters) between attempts when enabled to resolve trivial failures without hiding real bugs.
  • Stagnation detection & escalation: Detects repeated identical failures, stops retries to avoid loops, generates support requests, and updates linked US-* story artifacts with run summaries and failure signatures.
  • Reporting & story updates: Produces a human-readable report under execution-plan/reports and can inject an Automated feedback loop summary into the linked story file.
  • Use case: Run after a material implementation change to verify a US-* story compiles, passes tests, meets static rules, and has no critical dependency vulnerabilities before marking it done.

Quick Start

Run the feedback loop for a linked US story in the repository root with execute enabled to generate a report and update the story.

Frequently Asked Questions about qa-implementation-feedback-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a local feedback loop for build, tests, and static analysis after implementation changes?

A local feedback loop builds your project, runs tests, performs static analysis, and executes vulnerability scans to produce actionable remediation guidance after implementation changes. It validates code quality iteratively before marking a story complete.

Can I automatically fix linting and formatting errors during static analysis runs?

Yes, the feedback loop optionally applies deterministic, low-risk safe fixes like formatters and linters between attempts when enabled. This resolves trivial failures automatically without hiding real bugs detected during static analysis.

What CLI tools do I need installed to run vulnerability analysis and tests in my developer workspace?

You need standard CLI tooling like pytest, npm, ruff, eslint, or cmake present in your developer workspace when applicable. The feedback loop requires these tools to execute tests, static analysis, and dependency vulnerability scans.

How does stagnation detection work when repeated test or build failures occur?

Stagnation detection identifies repeated identical failures and stops retries to avoid infinite loops. It then generates support requests and updates linked US-* story artifacts with run summaries and specific failure signatures.

Where are feedback loop reports saved after running static analysis and vulnerability scans?

Feedback loop reports are saved as human-readable files under the execution-plan/reports directory. The loop can also inject an automated summary directly into the linked US-* story file artifact for tracking.

What is the best way to verify a US-* story compiles and passes vulnerability scans before marking it done?

Run the local feedback loop in your repository root with execute enabled. It prioritizes build, unit tests, static analysis, and vulnerability checks to ensure the US-* story meets all quality criteria before completion.