AD music-weekly
Weekly music album curation system — auto-search new releases across regions (English/Chinese/Japanese/Korean/Latin), score-filter ≥7.5, dedup against history, write to Notion DB, and push to your preferred channel (QQ/Telegram/Discord/Signal/WeCom/Feishu). Includes one-command setup script that creates the Notion database from scratch, config file with sensible defaults, and all required directories. Ideal for cron-driven music recommendation workflows. Use when: (1) running weekly music recommendation cron, (2) user asks "本周有什么好专辑", (3) user needs to fix/backfill cover URLs, (4) setting up the music weekly system for the first time.
As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Weekly music album curation system — auto-search new releases acro… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 45/100
- 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. 4 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 23 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1931 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
- -222 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 642: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.