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 agentmods add skills/johnwayneeee/casely-qa-skill/caselynpx skills add JohnWayneeee/casely-qa-skill --skill caselygit clone --depth 1 https://github.com/JohnWayneeee/casely-qa-skillWhat 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 | $0.00067 | $0.02696 |
| Opus 5 | $0.00034 | $0.01348 |
| Sonnet 5 | $0.00013 | $0.00539 |
| Haiku 4.5 | $0.00007 | $0.00270 |
Grade A, and why
casely 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 2d 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Casely — QA Test Case Generator
Casely automates the most time-consuming part of a QA engineer's job: writing test cases. It reads requirement documents and learns from your team's existing test case examples to produce structured, style-consistent test suites ready for import into any Test Management System.
Why this matters
Manual test case writing accounts for ~40% of a QA engineer's time. Requirements come in fragmented formats (PDF, DOCX, XLSX). Every team has its own column structure, naming conventions, and writing style. Casely solves this by:
- Converting any document format to clean Markdown via
docling. - Extracting formal style rules from your team's example test cases.
- Generating test cases that match your team's exact structure and tone.
- Exporting to Excel with correct column mapping for TMS import.
Commands
/init [ProjectName]
Creates a new isolated project workspace under projects/ and verifies that the
repository-level environment is ready.
/parse
Runs the CaselyParser to convert all raw assets (requirements and examples) to Markdown.
/style
Analyzes example test cases and generates a persistent test_style_guide.md.
/plan
Scans parsed requirements and suggests a testing plan with modules and test types.
/generate [type]
Generates atomic test cases of the specified type (functional, negative, integration, boundary, etc.).
/export
Converts generated Markdown test cases into a formatted .xlsx file.
Full Workflow
Phase 1: Project Initialization & Environment Setup (/init)
When the user runs /init [ProjectName] (or asks to start a new testing project):
- Resolve the Repository Root:
- Use the current working directory if it contains
pyproject.toml. - Otherwise walk upward until
pyproject.tomlis found. - If no
pyproject.tomlis found, stop and ask the user to run the command from thecasely-qa-skillrepository root. - Do not run
uv initinside a user's QA project folder.
- Use the current working directory if it contains
What ships with it
6 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.
- 2d ago First seen · 232 lines · 67 tokens per session scan A 6df406d42897
casely is a skill published in the GitHub repository JohnWayneeee/casely-qa-skill (7 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 2,696 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 2026-08-31.
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