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 qe-quality-assessmentgit 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/qe-quality-assessment)<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/qe-quality-assessment"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/qe-quality-assessment/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/qe-quality-assessment"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/qe-quality-assessment.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 | $0.00021 | $0.01219 |
| Opus 5 | $0.00010 | $0.00609 |
| Sonnet 5 | $0.00004 | $0.00244 |
| Haiku 4.5 | $0.00002 | $0.00122 |
Grade A, and why
QE Quality Assessment 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QE Quality Assessment
Purpose
Guide the use of v3's quality assessment capabilities including automated quality gates, metrics aggregation, trend analysis, and deployment readiness evaluation.
Activation
- When evaluating code quality
- When setting up quality gates
- When assessing deployment readiness
- When tracking quality metrics
- When generating quality reports
Quick Start
# Run quality assessment
aqe quality assess --scope src/ --gates all
# Check deployment readiness
aqe quality deploy-ready --environment production
# Generate quality report
aqe quality report --format dashboard --period 30d
# Compare quality between releases
aqe quality compare --from v1.0 --to v2.0
Agent Workflow
// Comprehensive quality assessment
Task("Assess code quality", `
Evaluate quality for src/:
- Code complexity (cyclomatic, cognitive)
- Test coverage and mutation score
- Security vulnerabilities
- Code smells and technical debt
- Documentation coverage
Generate quality score and recommendations.
`, "qe-quality-analyzer")
// Deployment readiness check
Task("Check deployment readiness", `
Evaluate if release v2.1.0 is ready for production:
- All tests passing
- Coverage thresholds met
- No critical vulnerabilities
- Performance benchmarks passed
- Documentation updated
Provide go/no-go recommendation.
`, "qe-deployment-advisor")
Quality Dimensions
1. Code Quality Metrics
await qualityAnalyzer.assessCode({
scope: 'src/**/*.ts',
metrics: {
complexity: {
cyclomatic: { max: 15, warn: 10 },
cognitive: { max: 20, warn: 15 }
},
maintainability: {
index: { min: 65 },
duplication: { max: 3 } // percent
},
documentation: {
publicAPIs: { min: 80 },
complexity: { min: 70 }
}
}
});
2. Quality Gates
await qualityGate.evaluate({
gates: {
coverage: { min: 80, blocking: true },
complexity: { max: 15, blocking: false },
vulnerabilities: { critical: 0, high: 0, blocking: true },
duplications: { max: 3, blocking: false },
techDebt: { maxRatio: 5, blocking: false }
},
action: {
onPass: 'proceed',
onFail: 'block-merge',
onWarn: 'notify'
}
});
What ships with it
3 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.
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.
- 6d ago First seen · 220 lines · 21 tokens per session scan A 61f9785356ac
QE Quality Assessment is a skill published in the GitHub repository summarybotng/summarybot-ng (2 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,219 once invoked, about $0.0001 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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