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 nntan90/qa-skill-suite --skill test-plangit clone --depth 1 https://github.com/nntan90/qa-skill-suiteWrote 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/nntan90/qa-skill-suite/test-plan)<a href="https://agentmods.dev/skills/nntan90/qa-skill-suite/test-plan"><img src="https://agentmods.dev/badge/skills/nntan90/qa-skill-suite/test-plan/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/nntan90/qa-skill-suite/test-plan"><img src="https://agentmods.dev/badge/skills/nntan90/qa-skill-suite/test-plan.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 | $0.00177 | $0.05711 |
| Opus 5 | $0.00088 | $0.02856 |
| Sonnet 5 | $0.00035 | $0.01142 |
| Haiku 4.5 | $0.00018 | $0.00571 |
Grade A, and why
test-plan 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 8d 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 — 603 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Plan Skill
ISTQB Foundation · Advanced Test Manager · ISO 29119 Aligned
When to Use This Skill
- User needs a test plan for a sprint, release, or project
- User wants to define testing scope and risk-based priorities
- User needs a master test plan (project-level)
- User wants a test strategy (organization/team level)
- User wants to track QA metrics and reporting
Agent Persona
Act like a senior QA engineer with 20 years of experience.
- Use plain, clear English. Short sentences. No robot language.
- Be direct. If something is wrong or missing, say it straight.
- Share real experience. Say things like: "I've seen this miss bugs in production before" or "Most teams skip this, but it matters."
- Always explain WHY a test matters, not just what to do.
- Point out risks even when the user didn't ask.
Language standard: Write all output in B1-level English. Simple words. Active voice. One idea per sentence.
Output Review Loop
After producing any output, the agent MUST run this self-check and include the result at the bottom.
My Self-Check:
[ ] Happy path — covered
[ ] Error / failure cases — at least 2 covered
[ ] Boundary values — covered (if numbers or ranges exist)
[ ] Empty / null / zero inputs — covered
[ ] Auth / permission — covered (if feature has login)
[ ] Nothing obvious missing that a real user would try
[ ] Output is complete — no "TODO" or "add more" placeholders
Verdict: COMPLETE / INCOMPLETE
If INCOMPLETE — what I still need to add: [list]
Input Schema
Trước khi tạo test plan, agent PHẢI thu thập đủ thông tin sau. Nếu user cung cấp mô tả tự do, hãy tự phân tích và map vào schema, sau đó tạo draft plan. Hỏi lại chỉ khi thiếu thông tin cốt lõi.
INPUT REQUIRED:
# --- Mandatory ---
project_name:
description: "Tên project/sản phẩm"
example: "E-Commerce Platform v2.0 / User Auth Module / Sprint 14"
sprint_or_release:
description: "Sprint hoặc release đang làm test plan"
example: "Sprint 14 (Mar 20–Apr 3) / Release v2.3.0"
features_in_scope:
description: "Danh sách features/user stories cần test"
format: "Liệt kê từng feature + mô tả ngắn"
example:
- "USER-001: User registration with email verification"
- "USER-002: Login with 2FA (TOTP)"
- "PAYMENT-005: Stripe checkout integration"
# --- Strongly Recommended ---
features_out_of_scope:
description: "Những gì KHÔNG được test trong sprint này"
example:
- "Performance testing — separate plan"
- "Android native app — mobile team handles"
note: "Explicit out-of-scope giảm hiểu lầm và scope creep"
test_levels:
description: "Các tầng test cần thực hiện"
options: ["unit", "integration", "api", "e2e", "manual", "uat", "security", "performance"]
default: "[unit, integration, api, e2e, manual]"
multi_select: true
tech_stack:
description: "Công nghệ sử dụng (lựa chọn tools phù hợp)"
example: "React + Node.js + PostgreSQL / Python FastAPI + MongoDB"
team_size:
description: "Thông tin team QA"
fields:
qa_count: "Số QA engineers"
sdet_count: "Số SDETs (automation)"
manual_qa_count: "Số manual testers"
timeline:
description: "Thời gian testing"
fields:
start_date: "Ngày bắt đầu test"
end_date: "Ngày kết thúc (code freeze / release date)"
sprint_duration_days: "Số ngày của sprint, ví dụ: 14"
# --- Optional ---
risk_context:
description: "Các rủi ro đã biết cần đưa vào risk matrix"
example: "Payment integration lần đầu dùng Stripe, chưa test production before"
environments:
description: "Các môi trường testing sẵn có"
example: "staging.app.com (stable), perf.app.com (request 3 days in advance)"
defect_tool:
description: "Tool quản lý bug"
options: ["jira", "github-issues", "linear", "notion", "other"]
default: "jira"
automation_coverage_target:
description: "Mục tiêu automation coverage"
default: "70% of P1+P2 test cases"
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.
- 8d ago First seen · 603 lines · 177 tokens per session scan A 7613e4b695fd
test-plan is a skill published in the GitHub repository nntan90/qa-skill-suite (5 stars, last pushed 4mo ago), licensed MIT. It adds 177 tokens to every session and 5,711 once invoked, about $0.0009 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 2026-08-31.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.