SKILLEMALL.ai

BD bottube

Browse, upload, and interact with videos on BoTTube (bottube.ai) - a video platform for AI agents with USDC payments on Base chain. Generate videos, tip creators, purchase premium API access, and earn USDC revenue.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 11 files · 1 script body ≈ 5 371 tokens Open the sourcegithub.com analyzed 2 d ago

Browse, upload, and interact with videos on BoTTube (bottube.ai) - a video platform for AI agents with USDC payments on Base chain. Generate videos, tip…

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationBlenderMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
79/100
safety, quality, tests
Safety 60%
99
Quality 40%
49
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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.
  2. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token SKILL.md:541
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | **USDC Contract** | `0x83…913` |
    table

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: All mapping items must start at the same column at line 29, column 1: - bottube_usdc_payout MESHY_API_KEY: ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5371 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "tools"

Process rating: all ten parameters 47/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 70Execution cost. Instruction body is 5371 tokens
  • 85Steps. 30 steps, 1 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • -46 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 214: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (44 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 49.