SKILLEMALL.ai

BC content-orchestrator

内容生成+发布统一编排器,15条管道文件(12 PL-*+3 E2E-*):VIDEO/VIDEO-BATCH/IMAGE/AUDIO/LIPSYNC/COMIC/COMIC-BATCH/ARTICLE-BATCH/NOVEL-BATCH/PRODUCT/HOTSPOT/NEWPROD+E2E-VIDEO/E2E-IMAGE/E2E-DAILY+3内置虚拟路由(PL-NOVEL连载/PL-DRAMA短剧/PL-UPLOAD上传)。平台注册表自动路由+代理自动注入+多租户感知(tenant_id→风格/人设/素材/平台隔离)+素材→闲鱼商品(PL-PRODUCT)+热点→商品(PL-HOTSPOT)+小说连载(PL-NOVEL/PL-NOVEL-BATCH)+短剧生成(PL-DRAMA内置)。触发:生成内容/发布内容/一条龙/日常运营/素材转商品/热点选品/热点上架/小说连载/短剧生成/上传内容生成 不触发:纯闲鱼运营/纯客服回复/数据分析查询

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 35 files body ≈ 8 321 tokens Open the sourceclawhub.ai analyzed 2 d ago

内容生成+发布统一编排器,15条管道文件(12 PL-+3…

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
80/100
safety, quality, tests
Safety 60%
100
Quality 40%
51
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 34. 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 body-long SKILL.md body ≈ 8321 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 51 mutating operations with no state check
  • 40Execution cost. Instruction body is 8321 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 254 steps
  • 100Consistency. Name and required fields are in place
  • low 23 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
  • -248 emoji in the instructions: noise for the model
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • -34 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 427: enough signal without eating the budget
  • +4Structure: 66 headings
  • +3Step-by-step instructions: 254 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 51.

External checks

ClawHub: suspicious
This skill is a powerful publishing orchestrator, but it needs Review because it can execute broad local pipelines, publish externally, and persist tenant workflow data with weak user-facing boundaries.
LLM: suspicious (high) · 15 Aug 2026