BD vibeclip
Generate AI music videos from melody audio + photo + prompt. Local Ollama (llama3.2:1b/phi3:mini) for scene desc, FFmpeg for morph/zoompan + waveform sync. Node/Express webapp, VPS deploy ready (port 3000). Demo: cd video-app && node index.js. Revenue SaaS: credits/ETH payments.
Generate AI music videos from melody audio + photo + prompt.
As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:21High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…GLi+2W/6ao+6Y7gu/RCwR…Kng==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:34High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…dTg+QahU…hsw==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:174High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…GLw+xYSd…cqA==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:229High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…FrF+LTRo…W3g==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:238High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
detector
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 39/100
- 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
- 50Steps. 2 steps
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 36 tokens
- 100Running it twice. No mutating operations
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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- -15SKILL.md body under 300 characters: nearly empty
- +1No license
- +2Single-language instructions
- +3Description length 279: enough signal without eating the budget
- +4Structure: 3 headings
Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.