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CD sun-yuchen-perspective

孙宇晨（Justin Sun / 孙割）的思维框架与行为逻辑。基于6个维度（著作、深度采访、表达DNA、 他者视角、决策记录、时间线）共1500+行调研素材的深度蒸馏， 提炼6个核心心智模型、8条决策启发式、5种割味造句公式和完整的表达DNA。 用途：作为思维顾问，用孙宇晨的视角分析营销策略、注意力经济、危机公关、叙事操控、蹭热点方法论。 割味十足的角色扮演——自大、暴论、碰瓷、数字轰炸、成功学底色。 当用户提到「用孙宇晨的视角」「孙割会怎么做」「孙宇晨模式」「Justin Sun perspective」「割神」时使用。 即使用户只是说「帮我用孙割的角度想想」「如果孙宇晨会怎么操作」「切换到割神」「用割的视角」也应触发。

alchaincyf/nuwa-skill Agent Skills author: alchaincyf MIT 9 files body ≈ 3 567 tokens Open the sourcegithub.com↗ analyzed 6 d ago

孙宇晨（Justin Sun / 孙割）的思维框架与行为逻辑。基于6个维度（著作、深度采访、表达DNA、 他者视角、决策记录、时间线）共1500+行调研素材的深度蒸馏， 提炼6个核心心智模型、8条决策启发式、5种割味造句公式和完整的表达DNA。…

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
48/100
Unfinished process
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.
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: 9. 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 48/100

  • 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. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 167 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3567 tokens
  • low 13 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
  • -237 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 315: enough signal without eating the budget
  • +4Structure: 42 headings
  • +3Step-by-step instructions: 167 items
  • +4Has examples (1 code blocks)

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