Agent skill

Casely

by aiskillstore in aiskillstore/marketplace

Intelligent QA assistant that automates writing test cases from project documentation.

MITAuto-check passedDocuments & Office

Install Casely

skills CLI
$ npx skills add aiskillstore/marketplace --skill casely -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aiskillstore/marketplace casely --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aiskillstore/marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/johnwayneeee/casely .claude/skills/casely && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
casely
GitHub stars
430
Token cost
~2.5k tokens
SKILL.md length
1,182 words
Files
8 (incl. scripts, references)
Skills in repo
1,108
Repo updated
First seen
Licence
MIT

At a glance

Intelligent QA assistant that automates writing test cases from project documentation.

  • Works in 6 steps: Project Initialization & Environment… → Document Parsing (/parse) → Style Guide Creation (/style) → …
  • The user wants to generate test cases from requirements
  • SKILL.md covers Why this matters, Commands, Full Workflow and Important Guidelines, plus 1 more section
  • Runs Python scripts from its folder; calls uv

What it does

Casely is an agent skill from aiskillstore/marketplace. Intelligent QA assistant that automates writing test cases from project documentation. Use when the user wants to generate test cases from requirements, runs /init, /parse, /style, /plan, /generate, /export, or works with PDF/DOCX/XLSX requirement documents and TestRail-ready Excel export.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `evals/evals.json`, `references/export_guide.md` and `references/parser_usage.md`).

It sits in Documents & Office, covering Test generation, Excel spreadsheets and Word documents. It works with Microsoft Excel and Microsoft Word. The repository describes itself as: Security-audited skills for Claude, Codex & Claude Code. One-click install, quality verified. The licence is MIT.

When your agent uses it

  • The user wants to generate test cases from requirements
  • Works with PDF/DOCX/XLSX requirement documents and TestRail-ready Excel export

Example prompts

  • “/casely”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Project Initialization & Environment Setup (/init)
  2. Document Parsing (/parse)
  3. Style Guide Creation (/style)
  4. Professional Test Design & Planning (/plan)
  5. Test Case Generation (/generate [type])
  6. Export to Excel (/export)

What it can do on your machine

Read from SKILL.md and the folder at commit ad8daf7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Casely loads about 2.5k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 74 tokens; SKILL.md has 1,182 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aiskillstore/marketplace at commit ad8daf7, republished under its MIT licence (© aiskillstore). 1,182 words, ~2,495 tokens.

Download SKILL.mdSave it as .claude/skills/casely/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
casely
description
Intelligent QA assistant that automates writing test cases from project documentation. Use when the user wants to generate test cases from requirements, runs /init, /parse, /style, /plan, /generate, /export, or works with PDF/DOCX/XLSX requirement documents and TestRail-ready Excel export.
license
MIT
metadata.author
John Wayne
metadata.version
1.5.0
metadata.category
QA Automation
metadata.repository
https://github.com/JohnWayneeee/casely-qa-skill

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 and verifies the environment.

/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):

  1. Create Directories: Create the project directory structure under projects/ in the repository root:

    • input/requirements/
    • input/examples/
    • processed/requirements/
    • processed/examples/
    • results/
    • exports/
  2. Environment Setup via uv:

    • Location: Dependencies are defined in pyproject.toml at the repository root (not inside the skill folder). Scripts expect uv sync to have been run from that root.
    • Check if pyproject.toml exists at the repo root. If not, run uv init there.
    • Install/verify dependencies: uv add docling openpyxl (or uv sync from repo root).
    • This ensures a lightning-fast setup and handles all sub-dependencies (e.g. torch for docling) automatically.
  3. Confirm to the user:

    • "Project {project_name} initialized via UV. Environment and dependencies (docling, openpyxl) are ready."
    • "Place your requirement documents into projects/{project_name}/input/requirements/ and examples into projects/{project_name}/input/examples/."
Phase 2: Document Parsing (/parse)

When the user runs /parse (or asks to parse/process documents):

  1. Locate the project. If there's only one project under projects/, use it automatically. If multiple exist, ask the user which one.

  2. Run CaselyParser — The parser is located at scripts/casely_parser.py within this skill. It uses docling and supports all major formats.

    Via CLI (optional arguments, auto-detects latest project if omitted):

    bash
    uv run python <skill-path>/scripts/casely_parser.py

    (Or manual path if needed)

    bash
    uv run python <skill-path>/scripts/casely_parser.py "projects/{name}/input/requirements" "projects/{name}/processed/requirements"
  3. Report results to the user: how many files were parsed, any errors, and summary of processed files.

Phase 3: Style Guide Creation (/style)
  1. Read all parsed example files from processed/examples/.

  2. Analyze the table structure to extract headers, data types, and mandatory fields.

    • CRITICAL: The style guide MUST be an exact replica of the example's column structure.
    • MANDATORY: Transfer ALL headers from the example files to the test_style_guide.md in their exact order. Do not rename, omit (e.g., "Comments", "Author"), or add new columns unless explicitly requested.
  3. Analyze the writing style to extract language, tone, and formatting patterns (e.g., how steps are phrased).

  4. Generate test_style_guide.md in the project root. This file acts as the "source of truth" and must explicitly define the horizontal table row structure.

  5. Present the style guide to the user for review. Any manual adjustments to this file will be respected by the generator.

Phase 4: Professional Test Design & Planning (/plan)
  1. Load Context & Analysis:

    • Read parsed requirements from processed/requirements/.
    • Load test_style_guide.md to match example structure (columns → test complexity).
  2. Structural Breakdown:

    • Extract modules/endpoints/logic blocks from requirements.
    • Categorize by Level: API (fields/status), Integration (flows), E2E (scenarios).[web:8]
  3. Smart Estimation (Style-Driven):

    • Metrics from Style Guide: Fields per test (from columns), branches from logic.
    • Coverage Tiers (total cases based on examples):
      TierCases/ModuleCoverageFocus
      Smoke1-3MinGolden Path[web:13]
      Critical (80%)N (fields*0.8)Key pathsHigh-risk (finance/auth)
      FullAll perms100%Edges/negatives
    • Risk Scoring: High (security), Med (logic), Low (UI).[web:8]
  4. Traceability & Prep:

    • Quick RTM Preview: Req ID → Planned Cases (e.g., "REQ-001 → 5 cases").
    • Data/Deps: Test data rules (valid/edge), mocks needed.
  5. Output Plan:

    • Table by Module: Module | Level | Est. Cases (80%) | Type | Tools.
    • MANDATORY: Provide ready-to-copy commands for each module.
    • Save test_plan.md (importable to TMS).
    • Ask: "Generate Critical Path? /generate functional MODULE_NAME" or "/generate negative MODULE_NAME".

Next: "/generate [type] will create exactly the estimated number of files, with each file containing one atomic test case matching your style guide."

Show full SKILL.md (430 more words)Show less
Phase 5: Test Case Generation (/generate [type])
  1. Load context:

    • BIDING: Read test_style_guide.md (Mandatory Source of Truth).
    • Read relevant parsed requirement files.
    • Target specific module and test type.
  2. Generate ATOMIC test cases:

    • One File = One Test Case (1 ID = 1 Scenario): Each test case MUST be saved as a separate Markdown file in results/.
    • Horizontal Structure: Each file MUST contain exactly ONE horizontal table row (header row + data row). Do NOT use vertical "key-value" lists.
    • Naming Convention: {type}_{id}_{short_description}.md.
    • Match the style guide exactly — same columns (1:1 with example), same tone, same structure.
    • No Hallucinations — only use columns and data points supported by the guide and requirements.
  3. Proactive Report:

    • Notify the user of created files.
    • Mandatory Next Step: Always advise the user on what else they can generate. Example: "I've generated functional cases. You can now run /generate negative to check error handling or /generate security for device metadata."
Phase 6: Export to Excel (/export)
  1. Convert Markdown files to Excel using scripts/export_to_xlsx.py.
    • Smart Execution: The script automatically detects the most recently modified project in the projects/ directory if no paths are provided.
  2. Atomic One-to-One Export: For every .md file in results/, the tool creates exactly one corresponding .xlsx file in exports/.
    • Behavior: Direct format conversion preserving the file count.
    • Naming: Files are named identically to their source: {type}_{id}_{short_description}.xlsx.
  3. Internal Structure: Each Excel file contains a single sheet called "Test Case" with the columns exactly matching the project's style guide.
  4. Plain Text Export: Content is exported as plain text with support for multi-line cells (using <br>).
  5. Save to exports/.

Important Guidelines

Proactive Guidance (Crucial)

After every command, Casely MUST provide a "Next Step" block.

  • After /init -> suggest /parse.
  • After /parse -> suggest /style.
  • After /style -> suggest /plan.
  • After /plan -> list specific commands like /generate functional or /generate negative.
  • After /generate -> suggest /export OR other generation types.
Language Awareness

Casely is language-agnostic for data. It will detect the language of the provided examples (e.g., Russian) and generate test cases in that same language. The internal logic and style guide should bridge this gap.

Atomic over Composite

Validators should always prefer multiple specialized test cases over one "all-in-one" case. This ensures clearer test results and easier bug localization.

Style Guide is King

The style guide is the single source of truth. Do not invent new columns or change formatting unless the style guide is updated first.


Skill Files

Scripts (scripts/)
  • scripts/casely_parser.py — Document-to-Markdown converter (Docling).
  • scripts/export_to_xlsx.py — Markdown-to-Excel exporter.
References (references/)
  • references/parser_usage.md — Technical details on calling the parser.
  • references/export_guide.md — Details on the MD-to-Excel conversion logic.
  • references/style_analysis_prompts.md — Methodologies for style extraction.

© aiskillstore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in skills/johnwayneeee/casely of aiskillstore/marketplace.

  • SKILL.md
  • evals/evals.json
  • references/export_guide.md
  • references/parser_usage.md
  • references/style_analysis_prompts.md
  • scripts/casely_parser.py
  • scripts/export_to_xlsx.py
  • skill-report.json

Open the folder on GitHubat commit ad8daf7

Compare with similar skills

Casely next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Casely compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Casely this skillaiskillstore/marketplace430—~2.5kAutomated safety check: PassMIT
MarkitdownImCa0/just-laws78114 repos~3.2kAutomated safety check: NotesMIT
Docx4jplutext/docx4j2.4k—~2.5kAutomated safety check: PassNone
Cyber Pptcrazyykhllc-bit/CyberPPT1.8k—~10kAutomated safety check: PassMIT
Markitshift-labs-ai/markit1.3k—~299Automated safety check: PassMIT
Markitdownjimmc414/Kosmos5942 repos~1.7kAutomated safety check: PassNone

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Questions about Casely

What does Casely do?

Intelligent QA assistant that automates writing test cases from project documentation. Casely is an agent skill from aiskillstore/marketplace. Intelligent QA assistant that automates writing test cases from project documentation.

When should I use Casely?

Casely fits situations like: the user wants to generate test cases from requirements; works with PDF/DOCX/XLSX requirement documents and TestRail-ready Excel export.

How do I install Casely in Claude Code?

Run `npx skills add aiskillstore/marketplace --skill casely -a claude-code`. Or copy the skill folder (skills/johnwayneeee/casely in aiskillstore/marketplace) into .claude/skills/casely in your project. Claude Code loads it when a task matches its description.

How do I install Casely in Codex?

Run `npx skills add aiskillstore/marketplace --skill casely -a codex`. Or copy the skill folder (skills/johnwayneeee/casely in aiskillstore/marketplace) into .agents/skills/casely in your project. Codex loads it when a task matches its description.

Can I use Casely in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiskillstore/marketplace --skill casely -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/casely, .gemini/skills/casely, .github/skills/casely and .opencode/skills/casely in your project.

What does Casely need to run?

Going by SKILL.md and its folder, Casely needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Casely access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Casely safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Casely use?

Casely is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Casely use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 975 tokens, read only when the agent opens those files.

What are the alternatives to Casely?

Skills that share tags, products or a category with Casely: Markitdown (ImCa0/just-laws, 781 stars), Docx4j (plutext/docx4j, 2.4k stars), Cyber Ppt (crazyykhllc-bit/CyberPPT, 1.8k stars) and Markit (shift-labs-ai/markit, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Casely?

aiskillstore (a GitHub organization) maintains it in aiskillstore/marketplace, which has 430 GitHub stars. The repository holds 1,108 skills in this directory. The repository was last updated on October 7, 2026.

Source: aiskillstore/marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.