Agent skill · Workflow & Productivity

top-one-percent

Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current paradigms, a complete roadmap or curriculum, deliberate practice and feedback, or a top-1%-level mastery plan. Answer explanation questions completely before offering a curriculum; route explicit mastery goals to an adaptive practice and proof-of-capability system.

tamdogoodgithub.com/tamdogoodGitHub ↗
claude-codecodexMIT
Install
npx skills add tamdogood/builder-essential-skills --skill top-one-percent --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 16 KB
Bundled scripts: none
Path: skills/top-one-percent/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 90
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Top One Percent Produce unusually clear understanding and evidence-backed mastery. Treat “top one percent” as a direction and a quality standard, not a percentile promise that cannot be measured. ## Follow the Answer-First Contract Match the response to the user's actual intent: - Answer a question as a complete explanation. Do not replace the answer with a roadmap, diagnostic, study plan, or quiz. - Build a mastery system when the user asks to become excellent, requests a curriculum, or wants sustained practice. - Tutor interactively when the user asks for lessons, exercises, assessment, or ongoing coaching. - Combine these only when the combination directly serves the request. If the user asks “Why is X special?”, explain X first; a brief learning path may follow only if useful. Infer the learner's level, goals, and constraints from the request and conversation. State only assumptions that materially affect the answer. Ask at most one high-leverage question when different answers would produce substantially different work; otherwise begin with a sensible default. Personalize examples to the learner's background when known. Do not force every learning-science technique into ever

What's inside
Steps it walks through
  1. Follow the Answer-First Contract
  2. Route the Request
  3. Produce a Deep Explanation
  4. Research before synthesis
  5. Build the causal model
  6. Calibrate depth and form
  7. Build a Mastery System
  8. Establish the learning contract
  9. Map the field
  10. Design stages and gates
  11. Teach and Coach Interactively
  12. Use Deliberate Practice
  13. Measure and Adapt
  14. Choose the Output by Mode
Ships with 3 files
  • README.md
  • agents/openai.yaml
  • references/learning-principles.md
More from builder-essential-skills
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About this skill
What does the top-one-percent skill do?

Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current paradigms, a complete roadmap or curriculum, deliberate practice and feedback, or a top-1%-level mastery plan. Answer explanation questions completely before offering a curriculum; route explicit mastery goals to an adaptive practice and proof-of-capability system.

How do I install it?

Run `npx skills add tamdogood/builder-essential-skills --skill top-one-percent --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From tamdogood/builder-essential-skills, a repository with 90 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going