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AB wechat-cover-maker

Turn an article title, topic, summary, or reference image into a WeChat Official Account cover, WeChat article cover, article hero image, post cover, headline image, or supporting article visual. This AI cover generator and article cover maker distills one clear visual hook, then creates either a rendered headline or a text-free headline-safe area. Use logos, portraits, products, and brand references to shape a brand cover image, and refine composition, focal point, color, thumbnail clarity, and crop resilience for a publish-ready WeChat cover design.

ClawHub Agent Skills author: beatra-ai v0.2.1 MIT-0 17 files body ≈ 1 698 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Turn an article title, topic, summary, or reference image into a WeChat Official Account cover, WeChat article cover, article hero image, post cover, headline…

As a process B 67/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting

GeneratorWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 17. 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 67/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 11 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1698 tokens
    • 100Running it twice. Mutating operations check current state

    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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 557: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (11 of 11)

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

    External checks

    ClawHub: suspicious
    The skill mostly does what it says, but it asks for broad Beatra account authority and can silently update its own installed files during normal use.
    LLM: suspicious (high) · 28 Aug 2026