SKILLEMALL.ai

BC nate-b-jones-digest

Monitor Nate B Jones's YouTube channel, pull each new video transcript (YouTube captions or auto-transcribed audio), summarize it with an abstract + bullet highlights + reference links, and distribute the digest via email, chat, and/or a document per user-configured outputs.

ClawHub Agent Skills author: arpee v1.0.0 MIT-0 9 files body ≈ 1 153 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ReferenceYouTubeMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
72
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 Exfiltration exfil-secret-in-url SKILL.md:28
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (destination is a well-known publishing service; quoted — discussed, not commanded)
    curl "https://www.googleapis.com/youtube/v3/playlistItems?part=…&maxResults=5&playlistId=…&key=…"
    known servicequoted
  • low Exfiltration net-credential-use SKILL.md:28
    Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service)
    curl "https://www.googleapis.com/youtube/v3/playlistItems?part=…&maxResults=5&playlistId=…&key=…"
    known service

Files scanned: 9. 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")

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 85Steps. 31 steps, 1 vague phrases
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1153 tokens
  • 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 275: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This is a coherent YouTube digest playbook that discloses its email, chat, document, logging, and optional scheduling behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026