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Writing and producing product videos: scripts, storyboards, narration, and reproducible Playwright demo recordings. Use for explainers, onboarding, feature walkthroughs, multi-aspect exports, captions, and video quality checks.

simota/agent-skills Agent Skills author: simota 18 files body ≈ 5 392 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Writing and producing product videos: scripts, storyboards, narration, and reproducible Playwright demo recordings.

As a process C 60/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

GeneratorPlaywrightYouTubeMedia 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
A
91/100
safety, quality, tests
Safety 60%
98
Quality 40%
81
Run on models
none yet
Process rating
C
60/100
Has gaps
Failures and branches w 10
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-password-literal reference/demo-implementation-patterns.md:339
    Hard-coded password / key literal (may be an example)
    password: 'DemoPass123',
  • low Secrets in code secret-password-literal reference/demo-implementation-patterns.md:1276
    Hard-coded password / key literal (may be an example)
    password: 'DemoPass123',

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5392 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 60/100

  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5392 tokens
  • 100Steps. 103 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 16 top-level sections: this looks like several domains in one skill

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 227: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 103 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (17 of 17)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.