SKILLEMALL.ai

AB glmv-prd-to-app

Build a complete, production-ready full-stack web application from PRD documents, prototype images, and resource files. Handles the entire pipeline: system design, database schema, seed data, backend API, frontend UI, visual verification against prototypes, and deployment script generation. Use this skill whenever the user: - Provides a PRD (product requirement document) and wants a working app built - Says things like "根据PRD开发", "build from PRD", "implement this product", "把需求文档做成应用", "develop this app from requirements" - Has prototype images + requirements and wants full-stack implementation - Wants to turn product specifications into a running web application - Mentions building an app from wireframes/mockups combined with a requirements doc Trigger this skill even if the user just says "帮我开发" or "build this" with PRD materials present in the working directory.

zai-org/GLM-skills Agent Skills author: zai-org Apache-2.0 6 files body ≈ 3 467 tokens Open the sourcegithub.com analyzed 2 d ago

Build a complete, production-ready full-stack web application from PRD documents, prototype images, and resource files.

As a process B 68/100 · Nearly there — weak spots: progress reporting

GeneratorPostgreSQLSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
B
68/100
Nearly there
Progress reporting w 2
0
Failures and branches w 10
55
Tools and files w 18
60
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: 6. 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 68/100

    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 60Steps. 135 steps, 5 vague phrases
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3467 tokens
    • 100Running it twice. Mutating operations check current state
    • low 11 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)
    • +3Description length 879: 120–800 characters recommended
    • -5TODO / placeholder text left in the skill
    • -2localhost URLs: will not work for another user
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Structure: 42 headings
    • +3Step-by-step instructions: 135 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.