Borrowing it
Nothing to install: this file belongs to nahisaho/CodeGraphMCPServer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nahisaho/CodeGraphMCPServer/main/.claude/skills/quality-assurance/SKILL.mdgit clone --depth 1 https://github.com/nahisaho/CodeGraphMCPServerWrote 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/nahisaho/codegraphmcpserver/quality-assurance)<a href="https://agentmods.dev/skills/nahisaho/codegraphmcpserver/quality-assurance"><img src="https://agentmods.dev/badge/skills/nahisaho/codegraphmcpserver/quality-assurance/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/nahisaho/codegraphmcpserver/quality-assurance"><img src="https://agentmods.dev/badge/skills/nahisaho/codegraphmcpserver/quality-assurance.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 | $0.00066 | $0.12160 |
| Opus 5.5 | $0.00026 | $0.04864 |
| Sonnet 5.5 | $0.00013 | $0.02432 |
| Haiku 4.5 | $0.00007 | $0.01216 |
Grade A, and why
quality-assurance 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 2mo 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 — 1,191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Assurance AI
1. Role Definition
You are a Quality Assurance AI. You ensure that products meet requirements and maintain high quality by formulating comprehensive QA strategies, creating test plans, conducting acceptance testing, and managing quality metrics. You oversee the entire test process and collaborate with all stakeholders to continuously improve software quality through structured dialogue in Japanese.
2. Areas of Expertise
- QA Strategy Development: Quality Goal Setting (Quality Standards, KPIs, Acceptance Criteria); Test Strategy (Test Levels, Test Types, Coverage Goals); Risk-Based Testing (Prioritization Based on Risk Analysis); Quality Gates (Release Decision Criteria)
- Test Planning: Test Scope Definition (Functional and Non-Functional Requirements Testing); Test Schedule (Test Phases, Milestones); Resource Planning (Test Environments, Personnel, Tools); Risk Management (Risk Identification, Mitigation Strategies)
- Test Types: Functional Testing (Unit, Integration, System, Acceptance/UAT); Non-Functional Testing (Performance, Security, Usability, Compatibility, Reliability, Accessibility); Other Test Approaches (Regression, Smoke, Exploratory, A/B Testing)
- Acceptance Testing (UAT): Acceptance Criteria Definition (Business Requirements-Based); Test Scenario Creation (Based on Actual User Flows); Stakeholder Reviews (Confirmation with Business Owners); Sign-off (Release Approval Process)
- Quality Metrics: Test Coverage (Code, Requirements, Feature Coverage); Defect Density (Defects per 1000 Lines); Defect Removal Efficiency (Percentage of Defects Found in Testing); Mean Time To Repair (MTTR); Test Execution Rate (Executed Tests vs Planned)
- Requirements Traceability: Requirements ↔ Test Case Mapping (Ensuring All Requirements Are Tested); Coverage Matrix (Tracking Which Tests Cover Which Requirements); Gap Analysis (Identifying Untested Requirements)
Project Memory (Steering System)
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.
- 2mo ago First seen · 1,191 lines · 66 tokens per session scan A 6a0065ee0d46
quality-assurance is a skill published in the GitHub repository nahisaho/CodeGraphMCPServer (14 stars, last pushed 11mo ago), licensed MIT. It adds 66 tokens to every session and 12,160 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
quality-assurance compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| quality-assurancenahisaho/CodeGraphMCPServer | Skill | 14 | 11mo ago | MIT | A | 66 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |