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CA youtube-summary

Summarize YouTube videos into structured Markdown with youtube2md, including chaptered notes, timestamp links, and key takeaways. Use when the user provides one or more YouTube URLs and asks for summaries, study notes, language-specific summaries, transcript extraction, or machine-readable JSON output.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 17 files · 2 scripts body ≈ 1 088 tokens Open the sourcegithub.com↗ analyzed 8 d ago

Summarize YouTube videos into structured Markdown with youtube2md, including chaptered notes, timestamp links, and key takeaways.

As a process A 80/100 · Runs to the end — weak spots: result and completion, progress reporting

ProcedureYouTubeMedia 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
84/100
safety, quality, tests
Safety 60%
82
Quality 40%
86
Run on models
none yet
Process rating
A
80/100
Runs to the end
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
60
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Broad scope
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. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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

  • high Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: OPENAI_API_KEY (high-value: OPENAI_API_KEY) — verify each one is needed for the stated purpose
    required_environment_variables: OPENAI_API_KEY

Files scanned: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • note frontmatter-key unknown frontmatter key "env"

Process rating: all ten parameters 80/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 60Failures and branches. 2 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 49 steps
  • 100When it triggers. States when to use and when not to
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1088 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (10 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 303: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 49 items
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.