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BD omniscient

全知全能技能 — 整合认知套件、执行框架、系统控制三大能力层，并配备编排引擎。 认知层：四种思维操作码（直用/改进/迁移/构建）覆盖所有思考任务； 执行层：大语言模型 + 命令执行工具，自动化代码生成与脚本执行； 操控层：Windows桌面软件、系统硬件、串口设备、物联网平台、图形界面自动化、蓝牙设备、GPU显卡、存储磁盘、电池电源、音频设备、显示器、温度风扇、打印机、摄像头； 编排引擎：将所有操作统一为基元，自由组合为任意复杂度的执行链条。 此技能应用于任何任务，无例外。

ClawHub Agent Skills author: 波动几何 v1.0.2 MIT-0 23 files body ≈ 2 945 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
73
Run on models
none yet
Process rating
D
43/100
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/storage_controller.py:267
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    $logical = Get-CimInstance Win3…ion | Where-Object { $_.Antecedent -eq $part.__PATH }
    quoted

Files scanned: 23. 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 43/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 80 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2945 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

  • +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
  • -31 of 19 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 239: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 80 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

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

External checks

ClawHub: suspicious
This skill openly provides broad system-control automation, but it also includes sensitive credential, camera, microphone, screen, network, and runtime installation capabilities that need review before use.
LLM: suspicious (high) · VirusTotal: · 29 May 2026