AD huo15-openclaw-mit-48h-learning-method
麻省理工学院48小时学习法技能（青岛火一五信息科技有限公司）。完整还原 Ihtesham Ali 原始三问框架 + 反馈循环 + 完整 48h 三阶段时间线，叠加网上最佳实践（synthesis / contradictions / gaps / Feynman teach-back / weakness analysis / practice exam）和 NotebookLM 2026 原生子命令（flashcards / quiz / mindmap / chat-config / download）。 核心三问（精确措辞）： Q1 心智模型：该领域每位专家共享的 5 个核心心智模型 Q2 专家分歧：3 个根本不同意的问题及各方最强论证（steelman） Q3 暴露性问题：10 个区分真懂和假背的问题 Q+ 反馈循环：错答时 → 诊断错误 + 给真懂回答 + 生成追击问题 科学原理：Active Recall（主动回忆）+ Desirable Difficulty（必要难度）+ Conceptual Frameworks First（先框架后细节） 触发场景：（1）用户要求快速学习某个领域；（2）用户提到 MIT 学习法、48 小时学习、NotebookLM 三问、context stacking；（3）用户需要生成播客/视频/抽认卡/思维导图概览；（4）用户想用 AI 辅助构建知识体系；（5）用户提到 Ihtesham Ali 或他的 viral tweet。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "aliases"
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 (bash) that frontmatter does not declare
- 100Steps. 37 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1939 tokens
- 100Running it twice. No mutating operations
- low 10 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
- +2Single-language instructions
- +3Description length 645: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (10 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.