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CC motion-reels-f100k

Diseña motion graphics para un reel 9:16 talking-head estilo softgirlnocode: con una captura del video detecta dónde está la persona y calcula zonas seguras; con el .srt auto-clasifica el guion para elegir el overlay correcto (antes/ahora → comparison card, listas → chapter card, herramientas → UI card, identidad → pill labels, frases → kinetic, citas → quote pull) y cumple la densidad de 1 gráfico cada 5 segundos. Entrega las tarjetas como PNG transparentes 1080×1920 renderizadas desde HTML + un motion board + una hoja de montaje con segundo de entrada, salida, posición y animación para ponerlas en tu editor. En este agente no edita ni compone el video. Activar cuando se diga: 'agrega motion graphics a este reel', 'motion graphics 9:16', 'overlays animados reel', 'estilo softgirlnocode', 'quiero motion graphics para este talking-head'. NO usar para 16:9.

ClawHub Agent Skills author: Fórmula 100K v1.0.0 MIT-0 2 files · 1 script body ≈ 12 426 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

Diseña motion graphics para un reel 9:16 talking-head estilo softgirlnocode: con una captura del video detecta dónde está la persona y calcula zonas seguras…

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureYouTubeTelegramMedia and videotype 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
C
79/100
safety, quality, tests
Safety 60%
95
Quality 40%
55
Run on models
none yet
Process rating
C
55/100
Has gaps
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash Read Write Edit AskUserQuestion

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 12426 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 55/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 12426 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 54 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 13 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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)
  • +3Description length 867: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -212 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 54 items
  • +4Has examples (41 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.

External checks

ClawHub: suspicious
The skill mostly matches a reel-overlay workflow, but it asks for persistent system installs, local secret-file access, third-party transcript calls, and includes conflicting instructions about whether it should edit/render final videos.
LLM: suspicious (high) · 17 Sept 2026