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CD boceto-a-animacion-f100k

Convierte un DIBUJO A MANO (foto o captura mandada por Telegram) o una frase del guion en un clip animado 9:16 de motion graphics para acompañar un reel. Lee el boceto como plano de composición (objetos, texto literal, movimiento implícito), decide si el dibujo es el cuadro INICIAL o el FINAL, elige estilo de un catálogo de 7, escribe los 3 prompts (primer cuadro, último cuadro, movimiento) y, con ✅ de créditos, lo genera en Higgsfield: GPT Image 2.5 con el boceto de referencia → el cuadro opuesto EDITANDO el primero → Seedance 2.0 con start_image + end_image. Entrega clip + cuadros + PROMPTS.md; el split «Animación | Boceto» y el revelado se arman en CapCut o Edits. Activar con 'anima este dibujo', 'convierte mi boceto en animación', 'de dibujo a video', 'primer y último cuadro', 'first frame last frame', 'dibujé esto, dale vida'. NO para overlays sobre la cara, historias con varios clips (storytelling-animado-f100k) ni para componer video grabado.

ClawHub Claude Code author: Fórmula 100K v1.0.0 MIT-0 3 files body ≈ 3 608 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

Convierte un DIBUJO A MANO (foto o captura mandada por Telegram) o una frase del guion en un clip animado 9:16 de motion graphics para acompañar un reel.

As a process D 35/100 · Unfinished process — References files that are not bundled: scripts/componer.py

GeneratorTelegramAI and agentstype 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
82/100
safety, quality, tests
Safety 60%
95
Quality 40%
62
Run on models
none yet
Process rating
D
35/100
Unfinished process
References files that are not bundled: scripts/componer.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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 text references files that are not there: add them or drop the references.
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 Skill mcp__higgsfield__generate_image mcp__higgsfield__generate_video mcp__higgsfield__media_upload mcp__higgsfield__media_confirm mcp__higgsfield__

Files scanned: 3. 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 missing-ref reference to a missing file: scripts/componer.py

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/componer.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/componer.py
  • 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
  • 100Steps. 33 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3608 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (4 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 963: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
This skill is a disclosed sketch-to-animation workflow that uses Higgsfield and local deliverable files, with no evidence of hidden persistence, exfiltration, or destructive behavior.
LLM: benign (high) · VirusTotal: · 17 Sept 2026