Learn anything 24h
Skill adityak74/learn-anything-24h/.codex/skills/learn-anything-24h
Turn any complex topic into a 24-hour active-learning sprint with 80/20 concept mapping, prerequisite compression, active recall, hands-on exercises, teach-back prompts, and a final artifact. Use when the user wants to learn, understand, crash-course, prepare for, or become operationally fluent in a difficult topic quickly.From its SKILL.md
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SKILL.md
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Learn Anything 24h
Use this skill when the user wants to learn a hard topic quickly and needs a serious plan rather than passive resources.
This skill produces operational fluency plans, not fake mastery claims.
Invocation
- This skill is invoked by mentioning
$learn-anything-24h. - Treat the remaining user text as the learning request.
- If the request is already specific enough, do not ask clarifying questions.
Default Assumptions
If the user does not specify otherwise, assume:
- learner level: strong software engineer
- time budget: 24 hours
- learning style: active recall, implementation, and teach-back
- outcome: explain + apply + build
- output quality: professional and concrete
Ask at most two clarifying questions, and only if the topic or intended outcome is materially unclear:
- What is your current level: beginner, intermediate, or advanced?
- What is your target outcome: interview, project, research, implementation, writing, or teaching?
Core Behavior
Build a compressed learning sprint around:
- ruthless 80/20 prioritization
- prerequisite compression
- active recall
- applied work
- a required final artifact
Bias toward output. The user should leave with tasks, exercises, and a proof-of-learning artifact.
Avoid:
- giant reading lists
- vague study advice
- generic motivational language
- long link dumps
- passive "watch these videos" plans
- claims of guaranteed mastery
Mode Detection
Infer the mode from the request. If multiple modes fit, choose the one closest to the user's stated goal.
default: general learning sprintinterview: prioritize tradeoffs, whiteboarding, explanation, and likely questionsbuilder: prioritize implementation, debugging, and hands-on milestonesresearch: prioritize papers, assumptions, equations, experiments, and critiquestaff-engineer: prioritize architecture, scaling, tradeoffs, failure modes, and decision qualitypaper: convert a specific paper into an understanding sprintcrash: use the same structure compressed to the stated time budget
Mode cues:
- "for interviews", "interview prep" ->
interview - "by building", "implement", "project" ->
builder - "paper", "from the paper", "research" ->
paperorresearch - "staff engineer", "architecture", "scaling" ->
staff-engineer - explicit sub-24-hour deadline ->
crash
Output Contract
Always produce this structure:
# 24-Hour Learning Sprint: <Topic>
## 0. Target Outcome
## 1. The 80/20 Map
## 2. Prerequisite Compression
## 3. 24-Hour Schedule
## 4. Exercises
## 5. Misconception Traps
## 6. Resource Strategy
## 7. Final Artifact
## 8. Mastery Rubric
## 9. Next 7 Days
If the user gives a different time budget, keep the same section structure and rename the title to match the stated duration.
Section Requirements
0. Target Outcome
Define a concrete end state. Use "By the end of this sprint, you should be able to..." and make it observable.
1. The 80/20 Map
List 5-9 concepts.
For each concept, include:
- why it matters
- what confusion it removes
- a simple analogy
- a practical example
2. Prerequisite Compression
Split prerequisites into:
Must knowCan ignore for nowLearn only if blocked
Aggressively reduce prerequisite sprawl.
3. Schedule
Default 24-hour schedule:
### Hour 0-1: Orientation
### Hour 1-3: Foundations
### Hour 3-6: Mechanisms
### Hour 6-9: Guided Examples
### Hour 9-12: Build / Solve / Derive
### Hour 12-16: Advanced Concepts
### Hour 16-20: Independent Challenge
### Hour 20-22: Retrieval and Feynman Test
### Hour 22-24: Final Artifact
Each time block must include:
- goal
- what to learn
- what to do
- output
- self-check question
For crash mode, compress proportionally while preserving orientation, foundations, applied work, retrieval, and final artifact.
4. Exercises
Always include:
- 5 warm-up questions
- 5 applied questions
- 5 hard questions
- 3 teach-back prompts
- 1 final boss challenge
Questions must be topic-specific and should expose shallow understanding quickly.
5. Misconception Traps
List common wrong mental models using this pattern:
Wrong model: ...
Correct model: ...
Favor misconceptions that would cause bad design choices or wrong explanations.
6. Resource Strategy
Recommend categories, not a dump of links.
Default structure:
- one canonical doc, book, or paper
- one implementation or repository
- one high-quality lecture or video
- one hands-on exercise
If the user explicitly asks for current links, recommendations, or latest sources, gather them. Otherwise stay focused and minimal.
7. Final Artifact
Every sprint must end with a required artifact selected from the user's goal:
interview-> answer bank + system design explanationprojectorbuilder-> working implementationresearchorpaper-> paper memo + derivation noteswriting-> technical blog postteaching-> lesson plan + explain-back scriptstaff-engineerorarchitecture-> design doc + diagramdebugging-> checklist + failure-mode guide
If no clear mapping exists, choose the artifact that best proves applied understanding.
8. Mastery Rubric
Define four levels:
- Beginner
- Useful
- Strong
- Expert
Keep the rubric concrete and measurable. "Useful" should mean the learner can actually do something with the topic.
9. Next 7 Days
Provide a lightweight continuation plan to avoid false confidence after the sprint.
Style
Write with these qualities:
- intense
- practical
What ships with it: 1 file
258 B alongside SKILL.md
agents/
- openai.yaml258 B