Mstar E2e

Community

btspoony

mstar-e2e

Separately requested E2E / real-browser / device / installed-deployment verification. Load only for an explicit request or `amazing-e2e-check`; never from routine QA or missing screenshots.

Overview

Publisher	btspoony
Repository	`mstar-harness`
Skill name	`mstar-e2e`
Stars	67
Forks	4
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by btspoony on GitHub. Read the source before you install it.

Installation

Install the Mstar E2e AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/btspoony/mstar-harness/tree/main/skills/mstar-e2e
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/btspoony/mstar-harness.git /tmp/mstar-harness
mkdir -p .claude/skills
cp -r /tmp/mstar-harness/skills/mstar-e2e .claude/skills/mstar-e2e

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mstar E2e in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mstar E2e on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mstar E2e is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Independent E2E Verification

Load Order

Read mstar-harness-core first. PM follows mstar-roles → references/project-manager.md and the existing dispatch/path contracts; the assigned executor reads mstar-roles → references/ops-engineer.md. Resolve available host tools through mstar-host only when needed. No external skill, CLI, or MCP is a required dependency.

Scope

This is an explicitly requested verification workflow, separate from development iterations and routine QA. Trigger phrases include “run these E2E scenarios”, “verify on this device”, “check the installed deployment”, and /amazing-e2e-check. A UI diff, missing screenshot, failed unit test, or reviewer suggestion does not authorize it.

External-dependency language in a development AC (live API, provider receipt, authenticated/named host or account, installed plugin/artifact, real browser/device, deployed environment) does not itself trigger this skill. Prepare rewrites that AC to isolated/local proof; only the user's separate explicit scenario authorization triggers real-environment verification.

Workflow

PM scopes the request. Record the existing user authorization, build/ref, environment/device, named scenarios and expected results, permitted side effects, capability, and report path. Ask only for missing required inputs; never infer production, accounts, or devices. Reuse relevant knowledge without a new global scan.
PM registers independent work. Use existing workflow type: plan, its own workflow/plan IDs and working context. Workflow states (read through the public workflow view against current authority) are running | paused | completed | failed | stopped; plan rows use Todo → InProgress → InReview → Done/Blocked. Do not insert an iteration phase, ordinary QA gate, or automatic QC tri-review into a verification-only run. The scenario rows registered here are the legitimate exception to the "never a development plan's task" boundary in mstar-harness-core § 定向执行与验证边界 — that boundary governs development plans, not this verification-only workflow.
PM dispatches ops. Use Execute as: ops-engineer, Task category: ops, Delegation: forbidden, and the existing Scope / Inputs / Constraints / Evidence Required / Acceptance Criteria fields. Pass the concrete report path under {WORKFLOW_DIR}/<workflow-id>/reports/e2e.md. Ops executes only the named scenarios and records actual results using references/report-template.md.
Run independent scenarios concurrently when sessions, devices, data, and writable state are isolated. Serialize shared state. Stop when the assigned scenarios finish; new environments or broader suites need matching user authorization.
PM reviews the scoped report and closes. InReview means report acceptance against the scenario list, not another broad code review. Ops returns evidence and cannot mark Done. PM owns the final plan/workflow state and any bounded repair handoff; preserve the originating iteration's state and unit-test evidence.

Decision Rules

Global scope and full-test permission remain in mstar-harness-core → ## 定向执行与验证边界. Explicit E2E permission authorizes only its named scenarios; it does not authorize a full suite. Permission for a full unit suite does not imply E2E permission.
QA never executes this workflow or launches its browser/device runner. PM dispatches ops directly; report-only and Skill presets: none do not change the executor boundary.
Verification-only work produces the E2E report, not a Deploy Plan. Production changes, installs, restarts, destructive steps, or deployment/rollback actions require scope-specific authorization; the ops role does not create that authorization.
A missing capability or input is blocked or not-run, never a simulated pass. Report the exact missing prerequisite without improvising another environment.
A failed product scenario can be a completed verification run when all assigned scenarios have determinate results and findings are handed off. workflow completed does not mean product passed. Unresolved execution blocks stay explicit.
Defects go to bounded repair assignments/plans. Repairs use affected unit checks; any real scenario retest stays in this separate workflow. Findings do not automatically reopen or block the originating iteration.

Evidence

Use actual build/environment identity, actions or commands, output/artifact links, and one outcome per scenario: passed | failed | not-run | blocked. State excluded scenarios and unavailable capabilities. Never substitute mocks, static checks, old-build screenshots, or workflow status for real scenario results. Keep secrets out of the report.

References

references/report-template.md — load when preparing or accepting the independent verification report.
mstar-harness-core → ## 定向执行与验证边界 — shared scope and authorization policy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

references/report-template.md1 KB

Frequently asked questions

What does the Mstar E2e AI skill do?

Separately requested E2E / real-browser / device / installed-deployment verification. Load only for an explicit request or `amazing-e2e-check`; never from routine QA or missing screenshots.

Why use Mstar E2e on TypingMind?

Because you install it once and use it with any model. Mstar E2e is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mstar E2e in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/btspoony/mstar-harness/tree/main/skills/mstar-e2e. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mstar E2e?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mstar E2e?

As many as you like. As long as a model supports skills, you can use Mstar E2e with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mstar E2e AI skill free?

Yes. It is published on GitHub by btspoony under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Independent E2E Verification

Load Order

Scope

Workflow

Decision Rules

Evidence

References

Bundled files

Frequently asked questions

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