SKILLEMALL.ai

BF short-drama-novel-analyze

把长篇小说、连载网文或多集散稿拆成可追溯的原著分析:章节索引、改编价值快评、逐章功能提取、剧情单元与节奏聚合、人物与设定归并,最后给出改编价值判定与分集候选,交给 $short-drama-develop 立契约。用户说“导入这本小说”“拆这本书”“分析原著”“这本书能不能改短剧”“先看看值不值得拆”“把长篇拆成分集候选”,或直接给出小说文件路径时使用。只做只读的结构化分析,不写剧本、不建资产、不生成媒体,也不替创作者决定改编方案。

zenstory-ai/drama-skills Agent Skills author: zenstory-ai 9 files · 2 scripts body ≈ 1 776 tokens Open the sourcegithub.com↗ analyzed 2 d ago

把长篇小说、连载网文或多集散稿拆成可追溯的原著分析:章节索引、改编价值快评、逐章功能提取、剧情单元与节奏聚合、人物与设定归并,最后给出改编价值判定与分集候选,交给 $short-drama-develop…

As a process F 33/100 · No process to follow — References files that are not bundled: assets/episode-candidate.example.jsonl

AnalyzerSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
F
33/100
No process to follow
References files that are not bundled: assets/episode-candidate.example.jsonl
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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: 9. 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")
  • warning missing-ref reference to a missing file: assets/episode-candidate.example.jsonl

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: assets/episode-candidate.example.jsonl
  • 0Tools and files. 1 referenced file(s) missing: assets/episode-candidate.example.jsonl
  • 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. 1 mutating operations with no state check
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1776 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 218: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.