SKILLEMALL.ai
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BD mcp-apps-builder

Build MCP Apps with interactive React UIs using the Leanmcp UI SDK. Use this skill when users ask for "MCP with UI", "MCP App", "interactive MCP", "leanmcp UI", "MCP dashboard", or want to create rich visual interfaces for their MCP tools. This skill covers @UIApp decorator, React components with useTool/useResource hooks, ToolDataGrid, ToolForm, ToolButton, and ChatGPT Apps support.

The skillemall take

The skill promises to build MCP apps with interactive React UIs using Leanmcp UI SDK. Seven files in the archive, two scripts, no critical errors or broken references. Code quality scores 85, safety at max.

In practice it covers @UIApp decorator, useTool and useResource hooks, ready-made components like ToolDataGrid and ToolForm. ChatGPT Apps support included. Process score of 43 signals incomplete automation—you'll dig into details yourself. No model runs or sandbox tests. Worth installing if you need a template for quick MCP UI app startup, but expect to debug on your own.

chatgptprojects/clear-code Agent Skills author: chatgptprojects 7 files · 2 scripts body ≈ 3 574 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Build MCP Apps with interactive React UIs using the Leanmcp UI SDK.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 36 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3574 tokens
    • low 16 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 386: enough signal without eating the budget
    • +4Structure: 36 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (22 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.