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CD pizarra-explicativa-f100k

Convierte un video grabado (talking head) en un clip de PIZARRA de apoyo para pantalla dividida: lo que dice se va escribiendo, encerrando y conectando a mano. Con el .srt del video arma el mapa de 8-12 beats anclado al rubro real de quien habla, PIDE APROBACIÓN con el costo, genera un clip por beat con los presets Whiteboard Doodle o Hand Drawn de Higgsfield, los junta en un solo pizarra.mp4 y entrega un LEEME con el segundo de cada beat para alinearlo en su editor. En este agente no edita ni compone el video. Activar cuando se pida 'hazme la pizarra de este video', 'clip de pizarra para este reel', 'apoyo visual explicativo en pantalla dividida', 'motion graphics de pizarra', 'acompaña este video con una pizarra'. NO caza b-roll (recursos-de-video-formula100k).

ClawHub Claude Code author: Fórmula 100K v1.0.0 MIT-0 1 file body ≈ 2 908 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Convierte un video grabado (talking head) en un clip de PIZARRA de apoyo para pantalla dividida: lo que dice se va escribiendo, encerrando y conectando a mano.

As a process D 35/100 · Unfinished process — References files that are not bundled: templates/composicion_base.html, assets/fonts/

ProcedureTelegramMedia 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
80/100
safety, quality, tests
Safety 60%
95
Quality 40%
58
Run on models
none yet
Process rating
D
35/100
Unfinished process
References files that are not bundled: templates/composicion_base.html, assets/fonts/
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 Glob Grep

Files scanned: 1. 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: templates/composicion_base.html
  • warning missing-ref reference to a missing file: assets/fonts/

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: templates/composicion_base.html, assets/fonts/
  • 0Tools and files. 2 referenced file(s) missing: templates/composicion_base.html, assets/fonts/
  • 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. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2908 tokens
  • 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)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 773: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.

External checks

ClawHub: clean
The skill is a disclosed video-to-whiteboard workflow that reads user-provided media/transcripts, asks approval before paid generation, and writes expected output files.
LLM: benign (high) · VirusTotal: · 17 Sept 2026