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.
npx skills add summarybotng/summarybot-ng --skill agentic-quality-engineeringgit clone --depth 1 https://github.com/summarybotng/summarybot-ngWrote 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.
[](https://agentmods.dev/skills/summarybotng/summarybot-ng/agentic-quality-engineering)<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/agentic-quality-engineering"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/agentic-quality-engineering/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.
<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/agentic-quality-engineering"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/agentic-quality-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.02661 |
| Opus 5.5 | $0.00012 | $0.01064 |
| Sonnet 5.5 | $0.00006 | $0.00532 |
| Haiku 4.5 | $0.00003 | $0.00266 |
Grade A, and why
agentic-quality-engineering 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 1mo 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.
This is a copy
88% identical to agentic-quality-engineering — 103 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Quality Engineering
<default_to_action> When implementing agentic QE or coordinating agents:
- SPAWN appropriate agent(s) for the task using
Tasktool with agent type - CONFIGURE agent coordination (hierarchical/mesh/sequential)
- EXECUTE with PACT principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus
- VALIDATE results through quality gates before deployment
- LEARN from outcomes - store patterns in
aqe/learning/*namespace
Quick Agent Selection:
- Test generation needed →
qe-test-generator - Coverage gaps →
qe-coverage-analyzer - Quality decision →
qe-quality-gate - Security scan →
qe-security-scanner - Performance test →
qe-performance-tester - Full pipeline →
qe-fleet-commander
Critical Success Factors:
- Agents amplify human expertise, not replace it
- Human-in-the-loop for critical decisions
- Measure: bugs caught, time saved, coverage improved </default_to_action>
Quick Reference Card
When to Use
- Designing autonomous testing systems
- Scaling QE with intelligent agents
- Implementing multi-agent coordination
- Building CI/CD quality pipelines
PACT Principles
| Principle | Agent Behavior | Human Role |
|---|---|---|
| Proactive | Analyze pre-merge, predict risk | Set guardrails |
| Autonomous | Execute tests, fix flaky tests | Review critical |
| Collaborative | Multi-agent coordination | Provide context |
| Targeted | Risk-based prioritization | Define risk areas |
19-Agent Fleet
| Category | Agents | Primary Use |
|---|---|---|
| Core Testing (5) | test-generator, test-executor, coverage-analyzer, quality-gate, quality-analyzer | Daily testing |
| Performance/Security (2) | performance-tester, security-scanner | Non-functional |
| Strategic (3) | requirements-validator, production-intelligence, fleet-commander | Planning |
| Advanced (4) | regression-risk-analyzer, test-data-architect, api-contract-validator, flaky-test-hunter | Specialized |
| Visual/Chaos (2) | visual-tester, chaos-engineer | Edge cases |
| Deployment (1) | deployment-readiness | Release |
| Analysis (1) | code-complexity | Maintainability |
What ships with it
1 file 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.
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.
- 1mo ago First seen · 340 lines · 29 tokens per session scan A 127ba878f9d0
agentic-quality-engineering is a skill published in the GitHub repository summarybotng/summarybot-ng (2 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 2,661 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to agentic-quality-engineering, differing in 103 lines, and is treated as a copy.
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