BC cn-geo-monitor
Chinese AI search engine optimization tool with API backend — 中国AI搜索引擎优化工具+引擎深度数据API (NOT generic GEO — focused on DeepSeek/Kimi/豆包/通义/文心). ONLY skill with per-engine citation logic, content preference data, and optimization tips for each Chinese AI engine. Features: (1) API-powered Chinese AI engine database with citation styles, preferred sources, content preferences, and optimization tips per engine, (2) Real AI visibility checking — query DeepSeek API to test if your brand appears in AI search results, (3) Competitor comparison — compare your brand visibility vs competitors across 5 Chinese AI engines, (4) Content prediction & calibration system (5-dimension scoring), (5) Engine-specific content adaptation framework (DeepSeek→数据型, Kimi→深度型, 豆包→短视频型), (6) Executable scripts for CLI access. 75% Chinese users use domestic AI search first. Use when: DeepSeek优化, Kimi优化, 豆包优化, 通义优化, 文心一言优化, 中国AI搜索优化, 品牌AI可见度, AI引用优化, competitor comparison, 竞品对比. Triggers: Chinese AI search, DeepSeek optimization, Kimi optimization, 豆包优化, 通义优化, 文心一言优化, 中国AI搜索引擎, 品牌AI可见度, AI引用优化, Chinese GEO, 中国GEO, AI搜索优化, cn-ai-engines, predict calibration, 内容预测校准, competitor analysis, 竞品对比, brand visibility check, AI搜索排名.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Shorten the description to 1024 characters.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1206 chars, limit 1024
Process rating: all ten parameters 51/100
- 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. 5 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 54 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1835 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 1206: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -212 emoji in the instructions: noise for the model
- -31 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Structure: 22 headings
- +3Step-by-step instructions: 54 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 51.