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Agent skill

Model Based Testing

by majiayu000 in majiayu000/claude-skill-registry

A skill your agent uses when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.

MITAuto-check passed

Install Model Based Testing

skills CLI
$ npx skills add majiayu000/claude-skill-registry --skill model-based-testing -a claude-code

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

GitHub CLI
$ gh skill install majiayu000/claude-skill-registry model-based-testing --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/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/model-based-testing .claude/skills/model-based-testing && 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
model-based-testing
GitHub stars
666
Used in
1 other repo
Token cost
~814 tokens
SKILL.md length
394 words
Files
2
Skills in repo
1,273
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.

  • Works in 5 steps: Read prompts/model-based-testing.md and… → Start with separate known, missing,… → Produce MBT-## findings with source,… → …
  • You need to derive test-path candidates from sourced behavior
  • SKILL.md covers When to Use, Output Format Options, How to Use and Core Constraints, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Model Based Testing is an agent skill from majiayu000/claude-skill-registry. Use this skill when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.

Its SKILL.md is about 810 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

  • You need to derive test-path candidates from sourced behavior
  • Triggers include 基于模型的测试 and model-based test design

Example prompts

  • “/model-based-testing”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Read prompts/model-based-testing.md and provide the objective, scope, material, environment, and evidence.
  2. Start with separate known, missing, conflicting, stale, out_of_scope, and assumptions entries.
  3. Produce MBT-## findings with source, evidence state, applicability, impact/priority, owner, close condition, and validation.
  4. Separate facts, evidence-backed inferences, recommendations, and Human decisions.
  5. Recommend follow-up validation without claiming execution.

What it can do on your machine

Read from SKILL.md and the folder at commit 2d14a69. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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

Model Based Testing loads about 814 tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 394 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~814

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 394 words, ~814 tokens.

Download SKILL.mdSave it as .claude/skills/model-based-testing/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
model-based-testing
description
Use this skill when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.

Model-Based Test Design

Derive test-path candidates from sourced behavior, state, or process models. Produce MBT-## design candidates within the evidence boundary; do not execute tests or claim coverage or pass results.

When to Use

  • Analyze behavior models, states or nodes, events, path constraints, model versions, and existing execution evidence.
  • Preserve selection rationale, evidence gaps, priority, and validation actions.
  • Inputs are incomplete but a bounded first pass can mark items unassessed or blocked.

Output Format Options

  • Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
  • Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.

How to Use

  1. Read prompts/model-based-testing.md and provide the objective, scope, material, environment, and evidence.
  2. Start with separate known, missing, conflicting, stale, out_of_scope, and assumptions entries.
  3. Produce MBT-## findings with source, evidence state, applicability, impact/priority, owner, close condition, and validation.
  4. Separate facts, evidence-backed inferences, recommendations, and Human decisions.
  5. Recommend follow-up validation without claiming execution.

Core Constraints

  • Do not invent model nodes, paths, or versions, or treat model presence as runtime evidence.
  • File presence, names, templates, and Eval configuration are not runtime evidence.
  • Do not edit requirements, code, test assets, or target systems, or accept risk for a Human.

Pre-delivery Check

  • The six-part input audit is complete.
  • Every MBT-## has source, evidence state, applicability, concern, impact/priority, owner, close condition, and validation.
  • Facts, inferences, recommendations, and Human decisions are separate.
  • Unexecuted, unverified, unassessed, and pending-decision items are explicit.
Show full SKILL.md (139 more words)Show less

Reference Files

  • Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
  • Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.

Common Pitfalls

  • Do not treat a method name, file presence, or candidate count as execution, coverage, pass, or release evidence.
  • Do not fill missing model rules, paths, versions, or results with convention; preserve unassessed, blocked, and pending items.
  • Do not expand this specialist design into a complete strategy, full test cases, runtime execution, or a release decision.

Best Practices

  • Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
  • Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
  • Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.

© majiayu000, 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 1 other file in skills/analysis/model-based-testing of majiayu000/claude-skill-registry.

  • SKILL.md
  • metadata.json

Open the folder on GitHubat commit 2d14a69

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Model Based Testing 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.

Model Based Testing compared with similar skills
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Model Based Testing this skillmajiayu000/claude-skill-registry6661 repos~814Automated safety check: PassMIT
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Candidate Talent Poolsickn33/agentic-awesome-skills47k1 repos~4.2kAutomated safety check: PassMIT
Release Candidate Prepopenai/openai-agents-python30k—~4.9kAutomated safety check: PassMIT
Math Derivation Auditortradecatlabs/vibe-coding-cn17k—~429Automated safety check: PassMIT

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Questions about Model Based Testing

What does Model Based Testing do?

A skill your agent uses when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design. Model Based Testing is an agent skill from majiayu000/claude-skill-registry. Use this skill when you need to derive test-path candidates from sourced behavior, state, or process models; triggers include 基于模型的测试 and model-based test design.

When should I use Model Based Testing?

Model Based Testing fits situations like: you need to derive test-path candidates from sourced behavior; triggers include 基于模型的测试 and model-based test design.

How do I install Model Based Testing in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill model-based-testing -a claude-code`. Or copy the skill folder (skills/analysis/model-based-testing in majiayu000/claude-skill-registry) into .claude/skills/model-based-testing in your project. Claude Code loads it when a task matches its description.

How do I install Model Based Testing in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill model-based-testing -a codex`. Or copy the skill folder (skills/analysis/model-based-testing in majiayu000/claude-skill-registry) into .agents/skills/model-based-testing in your project. Codex loads it when a task matches its description.

Can I use Model Based Testing 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 majiayu000/claude-skill-registry --skill model-based-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-based-testing, .gemini/skills/model-based-testing, .github/skills/model-based-testing and .opencode/skills/model-based-testing in your project.

What does Model Based Testing need to run?

SKILL.md names no scripts, command-line tools or credentials: Model Based Testing is instructions for the agent only.

Does Model Based Testing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Model Based Testing 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. Review the folder before installing.

What licence does Model Based Testing use?

Model Based Testing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Based Testing use?

About 814 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Model Based Testing?

Skills that share tags, products or a category with Model Based Testing: Longbridge Derivatives (sickn33/agentic-awesome-skills, 47k stars), Crypto Derivatives Strategies (HKUDS/Vibe-Trading, 35k stars), Candidate Talent Pool (sickn33/agentic-awesome-skills, 47k stars) and Release Candidate Prep (openai/openai-agents-python, 30k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Based Testing?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 1,273 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.