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短剧-公众号信息源 — 每日扫描公众号短剧爆款文章,按阅读量筛选热门内容,智能聚类题材方向后生成包含封面图、互动数据与创作洞察的HTML日报。支持按题材(穿越/霸总/重生等)、公众号、时间范围定向查询。⚠️查询前脚本先做输入校验：关键词需命中短剧题材词库（topic_keywords 中规定的题材名+全部相关词，如「打脸」命中逆袭题材相关词），命中后直接使用该关键词查询数据；不满足时提醒'关键词不满足查询条件'并推荐相关词，且**不发起接口请求**；日期超出有效查询范围时提醒并自动回退最近有数据日期，无需用户确认。**无数据确认制**：查询无匹配数据或数据不足时禁止自动发起任何额外查询（禁止自动降级全量/自动扩展题材），必须先展示推荐题材关键词并等待用户确认后才可查询。当用户需要短剧公众号日报、公众号短剧爆款、短剧热点、短剧创作趋势或自定义题材查询时使用。

redfox-data/redfox-community Agent Skills author: redfox-data 7 files body ≈ 3 462 tokens Open the sourcegithub.com↗ analyzed 5 h ago

短剧-公众号信息源 — 每日扫描公众号短剧爆款文章,按阅读量筛选热门内容,智能聚类题材方向后生成包含封面图、互动数据与创作洞察的HTML日报。支持按题材(穿越/霸总/重生等)、公众号、时间范围定向查询。⚠️查询前脚本先做输入校验：关键词需命中短剧题材词库（topickeywords…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description 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 (bash, python) that frontmatter does not declare
  • 100Steps. 67 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3462 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -239 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 383: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 67 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.