SKILLEMALL.ai

AB notebooklm-skill-factory

Orchestrate NotebookLM research into SKILL.md generation and Claude Code validation in a single automated pipeline. Use when user asks to create a new Claude Code skill from source materials (PDFs, articles, YouTube, URLs), batch-produce skills, or convert domain knowledge into reusable skills. Trigger phrases -- create a skill for X, make me a skill that does X, turn this into a skill, generate a skill from these sources, build a skill factory pipeline. NOT for manual skill editing (use skill-creator) or NotebookLM-only tasks (use notebooklm directly).

ClawHub Agent Skills author: Kidcvs Choi v0.1.0 MIT-0 6 files body ≈ 1 168 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: result and completion

AnalyzerYouTubeAI and agentsInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
When it triggers w 12
50
Tools and files w 18
60
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 · 0

    ✓ No critical or high findings

    Files scanned: 6. 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 70/100

    • 0Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 37 steps
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1168 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 559: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 37 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: suspicious
    This skill appears intended to build other skills, but it can upload user research to NotebookLM and install model-generated instructions into a persistent agent skills directory without clear approval boundaries.
    LLM: suspicious (medium) · VirusTotal: benign · 28 May 2026