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BC magic-moment

Make a "magic moment" video — turn a creator's talking-head recording into a vertical video preserving the source narration where their Muse story replays through artifacts and brief exchanges synced to their voiceover — bubbles, typing, emoji reactions, real widgets and pages, message sounds, closing Muse lockup finisher. Use whenever a user with talking-head or selfie footage wants it turned into a shareable clip of their Muse story — "make a magic moment", "turn this video of me into...", "add the chat over my video", "retell what Muse did for me" — even if they never say the words "magic moment".

asgeirtj/system_prompts_leaks Agent Skills author: asgeirtj CC0-1.0 12 files body ≈ 1 720 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Make a "magic moment" video — turn a creator's talking-head recording into a vertical video preserving the source narration where their Muse story replays…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorFigmaMedia 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%
100
Quality 40%
85
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
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: 12. 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 65 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1720 tokens
    • 100Progress reporting. Reports progress

    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)
    • +4Structure: 2 headings, hard to scan
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 607: enough signal without eating the budget
    • +3Step-by-step instructions: 65 items
    • +4Reference files are cited in the instructions (4 of 4)

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