SKILLEMALL.ai

AC make-motion-comic

Create or revise low-cost motion-comic videos from a story or script using consistent AI-generated keyframes, multi-character Chinese Edge TTS, captions, synthesized or licensed audio, and FFmpeg assembly. Use for 动态漫画、漫剧、条漫视频、animated manga/comic, narrated image-story shorts, vertical story videos, or when Codex must turn generated still images into a polished video without a generative video model; also use to diagnose or fix character drift, robotic TTS, subtitle timing, micro-jitter, shaky zoompan motion, audio balance, covers, and reusable episode production assets.

ClawHub Agent Skills author: ToBeWin v0.1.0 MIT-0 15 files · 2 scripts body ≈ 1 935 tokens Open the sourceclawhub.ai analyzed 2 d ago

Create or revise low-cost motion-comic videos from a story or script using consistent AI-generated keyframes, multi-character Chinese Edge TTS, captions…

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorGitHubMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 14. 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 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 51 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1935 tokens

    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
    • +2Single-language instructions
    • +3Description length 577: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 4 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a coherent motion-comic production skill whose file, command, image-generation, and online TTS behavior matches its stated purpose, with privacy and input-validation caveats.
    LLM: benign (high) · VirusTotal: · 26 Aug 2026