QE Iterative Loop

QE Iterative Loop is a skill for Claude Code from summarybotng/summarybot-ng. It costs 60 tokens per session (3,267 once invoked), scanned A, original, MIT.

An iterative testing workflow that repeatedly runs checks and applies improvements until measurable quality goals are met. These goals can include passing tests, coverage targets, quality gates, or stabilised flaky tests.

In plain words
What is it for?
Use it to fix failing tests, reach coverage targets, satisfy quality gates, and investigate or stabilise flaky tests.
Why use it?
It reduces the manual back-and-forth involved in fixing failures and rechecking whether the project now meets its quality targets.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to fix failing tests, reach coverage targets, satisfy quality gates, and investigate or stabilise flaky tests.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/summarybotng/summarybot-ng/qe-iterative-loop
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add summarybotng/summarybot-ng --skill qe-iterative-loop
Clone the repo
git clone --depth 1 https://github.com/summarybotng/summarybot-ng

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for QE Iterative Loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/qe-iterative-loop/github.svg)](https://agentmods.dev/skills/summarybotng/summarybot-ng/qe-iterative-loop)
Your own site
<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/qe-iterative-loop"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/qe-iterative-loop/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for QE Iterative Loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/qe-iterative-loop"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/qe-iterative-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,267 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00060 $0.03267
Opus 5 $0.00030 $0.01633
Sonnet 5 $0.00012 $0.00653
Haiku 4.5 $0.00006 $0.00327

Measured 5d ago against content hash 279026a30e36, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

QE Iterative Loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/qe-iterative-loop/SKILL.md · 446 lines

How it starts

The opening of the file, as written. The whole thing — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QE Iterative Loop

Overview

QE Iterative Loop is a specialized adaptation of the Ralph Wiggum technique for Quality Engineering workflows. It enables autonomous, self-correcting quality cycles where AI agents iterate until quality objectives are achieved - tests pass, coverage targets met, quality gates satisfied, or flaky tests stabilized.

Why QE Benefits from Iteration

Quality Engineering has objective, measurable success criteria:

  • Tests either pass or fail (exit code 0 vs non-zero)
  • Coverage is quantifiable (78.5% vs 80% target)
  • Quality gates have binary outcomes (pass/fail)
  • Contract validation has clear schemas

This makes QE ideal for iterative loops - we know exactly when we're done.

Prerequisites

  • AQE v3 fleet initialized
  • Test framework configured (Jest, Vitest, Pytest, etc.)
  • Coverage tooling (c8, istanbul, coverage.py)
  • Quality gate definitions

Quick Start

Pattern 1: Test Fix Iteration

# Task: Fix all failing tests
/qe-loop "Run npm test and fix all failing tests.
Success: npm test exits with code 0
Output <promise>TESTS_GREEN</promise> when all tests pass."

Pattern 2: Coverage Target Iteration

# Task: Achieve 80% coverage
/qe-loop "Increase test coverage to 80%.
Success: Coverage report shows >= 80%
Output <promise>COVERAGE_MET</promise> when target achieved."

Pattern 3: Quality Gate Iteration

# Task: Pass all quality gates
/qe-loop "Pass all quality gates for deployment.
Gates:
- Unit tests: pass
- Integration tests: pass
- Coverage: >= 80%
- No critical vulnerabilities
- Performance < 200ms P95
Output <promise>QUALITY_GATES_PASSED</promise> when all pass."

QE Iteration Patterns

Pattern 1: Test-Fix Iteration Loop

Goal: All tests pass

## QE Test-Fix Loop

### Success Criteria
- `npm test` (or test command) returns exit code 0
- No skipped tests (unless explicitly allowed)
- No pending tests

### Iteration Steps
1. Run full test suite
2. Parse output for failures
3. Analyze first failure:
   - Identify failing test file
   - Understand assertion that failed
   - Check if production code or test is wrong
4. Fix the issue
5. Re-run failed test file only (faster feedback)
6. If file passes, run full suite
7. If all pass -> output <promise>TESTS_GREEN</promise>
8. If failures remain -> continue to next failure

### Safety
- Max iterations: 30
- After 10 iterations: report remaining failures
- Stop if same test fails 5 times (possible design issue)

Read the full file on GitHub · 446 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 446 lines · 60 tokens per session scan A 279026a30e36

Subscribe to this mod's changes

QE Iterative Loop is a skill published in the GitHub repository summarybotng/summarybot-ng (2 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 3,267 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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