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BD paper-cutout

Make paper cut-out / stop-motion style animated videos (MP4) with code — handmade construction paper, cardstock, kraft, felt and tissue pieces with scissor-cut or torn edges and soft drop shadows, photographed on a table under a warm lamp; jointed paper puppets with brass split-pin joints, moving in 12 fps steps. Canvas 2D rendered frame by frame in headless Chrome, encoded with ffmpeg. Use when a reference video or brief calls for paper craft, cut-paper, papercraft, stop-motion paper, kids' storybook collage, 剪紙 / 紙片 / 紙偶 / 定格動畫 looks, or when the video-clone router picks the paper-cutout style.

edenfunf/reelmimic Agent Skills author: edenfunf 14 files · 7 scripts body ≈ 1 354 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Make paper cut-out / stop-motion style animated videos (MP4) with code — handmade construction paper, cardstock, kraft, felt and tissue pieces with…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorMedia and videotype 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%
98
Quality 40%
87
Run on models
none yet
Process rating
D
46/100
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token template/package-lock.json:84
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…eBL+/1tqG…AR5+magt…Y4Q+w==",
      detector
    • low Secrets in code secret-high-entropy-token template/package-lock.json:115
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…kSb+UKYd…IJ4+lp54i/hhCl…p7i+22QhNU0+/qk8g==",
      detector

    Files scanned: 13. 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 46/100

    • 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
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1354 tokens
    • 100Running it twice. No mutating operations

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 603: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.