AC chinese-sensitive-words
Chinese sensitive word detection and content compliance checker (中文敏感词/违禁词检测). Scan text for banned, restricted, and risky words across Xiaohongshu (小红书), Douyin (抖音), Kuaishou (快手), and Bilibili (B站). Covers advertising law violations (广告法极限词), medical claims (医疗功效词), political, violent, gambling, and fraud-related terms. Returns risk levels (high/medium/low), word categories, and safe replacement suggestions. 100K+ daily-updated word dictionary with homophone detection (谐音变体), character-jumping detection (跳字检测), phone number & URL detection, and NER filtering. Free 10 requests/month, unlimited with token. Perfect for checking marketing copy, product descriptions, live-streaming scripts (直播话术), social media posts, and ad compliance (内容审核/文案合规).
As a process C 53/100 · Has gaps — 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: 5. 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 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 640 tokens
- 100Running it twice. No mutating operations
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
- +1No license
- +2Single-language instructions
- +3Description length 755: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (9 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.