AC naver-datalab-cli
Korean search-keyword and shopping-trend analytics via the official NAVER DataLab API (openapi.naver.com/v1/datalab/*). Six subcommands wrapping 통합 검색어 트렌드 and 쇼핑인사이트 (분야별, 분야 내 키워드, 디바이스/성별/연령대 분포). Use when researching Korean consumer demand, planning SEO/content for the naver.com search market, comparing keyword popularity over time, building K-pop / K-beauty / K-commerce trend dashboards, or filling the gap that Google Trends leaves on Korean queries. Pairs with naver-papago-translate (translate insights), tistory-api-cli / velog-cli (publish trend posts), and kr-holiday-cli (align campaigns with KR calendar). Free tier (1k req/day for shopping insights, 25k req/day for search trends).
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 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. 2 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1609 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
- -31 of 7 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 698: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.