BD spotify-openclaw
Full Spotify Premium control + deep music intelligence for OpenClaw. Playback: play by name/URI/playlist, pause, next, prev, volume, shuffle, queue. Auto-launches Spotify if closed. Analysis: top tracks & artists across 3 time periods, genre profile, liked songs breakdown. Discovery: find similar music via related artists graph — works without Spotify's blocked Recommendations API. One-command playlist builder. Credentials stored in macOS Keychain. Triggers: play, pause, next, volume, shuffle, what's playing, top tracks, genre analysis, find similar music, create playlist.
As a process D 49/100 · 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: 3. 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") - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 7 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1210 tokens
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
- -2localhost URLs: will not work for another user
- -222 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 579: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 7 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.