SKILLEMALL.ai
GitHub ↗ RU Sign in

AD playlet-douyin-feed

短剧-抖音信息源 — 每日扫描抖音短剧爆款内容,按点赞量筛选热门短剧,智能聚类题材方向后生成包含封面、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)、达人、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**；查询无匹配数据或全量数据不足时先询问用户是否按推荐题材重新查询，确认后才可查询（不自动扩展、不自动降级全量）。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。当用户需要短剧抖音日报、抖音短剧爆款、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 6 files body ≈ 2 053 tokens Open the sourcegithub.com↗ analyzed 5 h ago

短剧-抖音信息源 — 每日扫描抖音短剧爆款内容,按点赞量筛选热门短剧,智能聚类题材方向后生成包含封面、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)、达人、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

IntegrationSoftware developmenttype 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
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: 6. 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. 43 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2053 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
  • -215 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 353: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (6 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: 77.