BD audd-musikerkennung
Musikerkennung und AudD-Kontoverwaltung über den offiziellen AudD-MCP-Server (https://mcp.audd.io) und/oder die direkte HTTP-API. Ein eigener api_token ist optional — funktioniert per OAuth-MCP ganz ohne Token, mit Trial-Plan und mit bezahltem Plan, ebenso mit dauerhaft hinterlegtem Token. Nutze diesen Skill bei Songerkennung — "welcher Song ist das?", Audio-Clip oder Datei identifizieren, Shazam-artig, Radio-/Twitch-/YouTube-Streams überwachen, DJ-Sets, Podcasts oder Videos nach Tracks durchsuchen, Tracklists, Airplay-Monitoring, Copyright-Check — und bei allem rund um das AudD-Konto: api_token eintragen, hinterlegen, aus der Zwischenablage in den Secret-Vault übernehmen, in .env setzen, rotieren; Request-Kontingent, Verbrauch, Trial-Restlaufzeit, Plan-Upgrade, Rechnungen. Auch anwenden, wenn AudD nicht namentlich genannt wird, aber ein Audio-/Video-Link oder eine lokale Audiodatei identifiziert werden soll.
Musikerkennung und AudD-Kontoverwaltung über den offiziellen AudD-MCP-Server (https://mcp.audd.io) und/oder die direkte HTTP-API. Ein eigener apitoken ist…
As a process D 42/100 · 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: 2. 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 42/100
- 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
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4392 tokens
- 100Steps. 17 steps
- 100Consistency. Name and required fields are in place
- low The response is described with custom markup (4 tags): a typed call is more reliable
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)
- +3Description length 922: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 22 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.