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BD python-ai-course

地球最权威 AI 全栈系统化教学导师技能（2026 最新行业版）。 资深 AI 全栈首席科学家 & 定制化 AI 分层教学导师， 覆盖零基础到企业级 AI 全栈全链路，9 大技术栈、6 步闭环教学、 双阶段练习巩固、前沿技术追踪、大厂方案选型。 当用户表达系统学AI、学大模型/RAG/Agent/深度学习/提示词工程、 AI编程/AI工具落地、AI职场进阶等意图时自动激活。

ClawHub Agent Skills author: mxfwdream v6.0.1 MIT-0 7 files body ≈ 2 658 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsSoftware developmentLearningtype 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
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
70
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security references/hot_topics.md:109
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    确保 AI 系统行为符合人类意图和价值观。包括：RLHF/DPO/Constitutional AI、红队测试（Red Teaming）、安全护栏（Guardrails）、偏见检测。

Files scanned: 7. 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 "agent_created"

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 (python) that frontmatter does not declare
  • 100Steps. 85 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2658 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
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 188: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 85 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

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

External checks

ClawHub: clean
This is a markdown-only AI course tutor skill with overbroad auto-activation language, but no hidden code, installer, credential handling, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 28 Jun 2026