BD lygo-resonance
LYGO RESONANCE — Image-to-Sound & Creative Profiles. Transforms any image into rich stereo soundscapes (WAV via Resonance Engine) or structured creative JSON profiles + AI-ready music/lyric briefs (via Profile Generator). Uses computer vision (edges, color, contours, keypoints) for generative music, Suno/Udio prompts, DAW work, video motion audio, Gradio GUI, batch, MIDI export, and local LLM lyric expansion. Full open-source modules provided. Website + complete instructions included. Ties to LYGO protocol, creative intelligence limb for LYRA.
LYGO RESONANCE — Image-to-Sound & Creative Profiles.
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 · 0
✓ No critical or high findings
Files scanned: 8. 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Steps. 75 steps, 5 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2435 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
- +4No input/output examples
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 549: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 75 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.