BD xhs-feedback-analyzer
小红书跑腿类帖子监控与用户反馈分析工具。多关键词搜索（美团跑腿/跑腿/帮买/帮送等），按发帖日期过滤，相关性筛选，深度分类（服务类型/情感/反馈类型），输出 Markdown 报告写入 KM 学城文档。当用户需要分析跑腿产品在小红书的用户口碑、定期舆情监控、竞品用户反馈时使用。触发词：小红书反馈分析、跑腿帖子监控、抓小红书、分析用户评价、舆情。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.
Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Obfuscation
uni-zero-widthoutput/xhs_paotui_2026-03-17.json:209Zero-width / invisible characters (possible hidden text) (12 occurrences)"body": "本人于2026年3月10日10:39:12在美团平台闪购商家「气球鲜花派对」购买「仙子之吻花束【甜蜜守护】」一束，订单号：2802 0295 9359 9887 500，实付货款83元。\n收到的鲜花实物与商家宣传图片严重不符、质量差距极大，属于明显货不对板。发现问题后，我第一时间与商家沟通，并自费1…
Files scanned: 8. 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 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 258 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
- -2localhost URLs: will not work for another user
- -31 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 173: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.