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AB notebooklm-content-creation

Create and monitor NotebookLM Studio content — Audio Overview, Video Overview, Infographics, and Slides — via the notebooklm-mcp-cli. Use when user wants to generate a podcast, video, infographic, or slide deck from a NotebookLM notebook. Also triggered by upstream skills (e.g., Deep Research) with pre-filled parameters. Triggers on: create audio, create video, create infographic, create slides, generate podcast from notebook, make a video overview, notebooklm studio create, download notebook audio, notebooklm content creation, 请启动 NotebookLM 工作流. Requires notebooklm-mcp-cli installed and authenticated.

ClawHub Agent Skills author: Skywalker326 v2.0.0 MIT-0 2 files body ≈ 2 815 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, consistency

GeneratorMedia 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
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
B
65/100
Nearly there
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

How to improve

    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
    • low Dangerous commands cmd-background-process SKILL.md:164
      Starts a background / autostarted process
      nohup bash /tmp/notebooklm-studio/poll.sh > /dev/null 2>&1 &

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 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
    • 40Consistency. Frontmatter name (notebooklm-content-creation) differs from the folder (jclaw-notebooklm-content-creation)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 36 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Execution cost. Instruction body is 2815 tokens
    • 100Running it twice. Mutating operations check current state
    • low The response is described with custom markup (14 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
    • -212 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 610: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    This skill fits its NotebookLM automation purpose, but it can upload local files and run background notifications without enough per-run user control.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026