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CD code-standards

代码规范与完整开发工作流，涵盖 Python、PyTorch/深度学习、Shell 脚本、配置文件。贯穿始终的四条设计原则：易读直观、拒绝过度抽象、单一职责、命名自解释。强制三段式流程：（1）写代码前——在已有项目上开发时必须先了解项目全貌避免重复实现，然后先出 plan（思路+实现步骤）并获得用户批准才能开工；（2）写代码中——遵循命名/类型/docstring/张量操作/异常处理等规范，同时时刻守住四条设计原则；（3）写代码后——必须先补齐对应单元测试并本地跑通，再主动调用 simplify 简化新代码，最后调用 review 做审查。触发条件：用户要求写/生成/重构 Python 或 PyTorch 代码；用户要求在已有项目/代码库上开发；用户创建新模块、脚本或训练循环；用户说"按代码规范来"或引用 /code-standards。

Tencent-Hunyuan/UniRL Agent Skills author: Tencent-Hunyuan 1 file body ≈ 4 431 tokens Open the sourcegithub.com↗ analyzed 9 h ago

代码规范与完整开发工作流，涵盖 Python、PyTorch/深度学习、Shell 脚本、配置文件。贯穿始终的四条设计原则：易读直观、拒绝过度抽象、单一职责、命名自解释。强制三段式流程：（1）写代码前——在已有项目上开发时必须先了解项目全貌避免重复实现，然后先出…

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
D
42/100
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 · 0

✓ No critical or high findings

Files scanned: 1. 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 42/100

  • 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. 4 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, git, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4431 tokens
  • 100Steps. 295 steps
  • 100Consistency. Name and required fields are in place

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 374: enough signal without eating the budget
  • +4Structure: 58 headings
  • +3Step-by-step instructions: 295 items
  • +4Has examples (15 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.