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CC cue-omni-reader

输入URL或上传文件（PDF/Office/图片/扫描件/音视频/网页/压缩包等不限定文件格式），将任意文档/文件/网页内容解析为Markdown，即插即用、默认不落盘，具有极高隐私保护。可精准还原文档目录结构和复杂表格结构，适用于高精度高性能要求的文档处理OCR任务；可将音视频内容一键解析转化为文字稿，并具备画面视觉理解能力，适用于视频转脚本、主流视频网站url提取ppt等场景。

ClawHub Agent Skills author: panting09266-ai v1.2.13 MIT-0 6 files body ≈ 3 816 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

输入URL或上传文件（PDF/Office/图片/扫描件/音视频/网页/压缩包等不限定文件格式），将任意文档/文件/网页内容解析为Markdown，即插即用、默认不落盘，具有极高隐私保护。可精准还原文档目录结构和复杂表格结构，适用于高精度高性能要求的文档处理OCR任务；可将音视频内容一键解析转化为文字稿，并具备画面视…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWordCustomer supportSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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")
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "description_zh"

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3816 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 16 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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 192: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This appears to be a real document-parsing integration, but its local-file, credential, install, and privacy behavior is under-disclosed and inconsistent enough that users should review it carefully before installing.
LLM: suspicious (high) · 16 Sept 2026