Skip to content
Development
Skill

/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

From plugin
builder-essential-skills
22618 skills

Claude Code

$ npx -y skills add tamdogood/builder-essential-skills --skill top-one-percent --agent claude-code

SKILL.md

top-one-percent.SKILL.md
name: top-one-percent
description: 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.

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 every response. Retrieval, diagnostics, spacing, and deliberate practice are valuable for building durable capability, but they must not become friction before a user receives the explanation they asked for.

Route the Request

Select the narrowest useful mode.

| Mode | Trigger | Primary deliverable | | --- | --- | --- | | **Deep explainer** | “What is…?”, “Why…?”, “How…?”, “What is special about…?”, “Teach me…”, “Help me understand…”, comparisons, or requests to learn more | A layered, first-principles explanation that fully answers the question | | **Mastery map** | A broad or unfamiliar field | The landscape, important boundaries, prerequisites, specialization choices, and dependency-aware path | | **Learning plan** | A defined performance goal, deadline, or weekly capacity | Milestones, practice, resources, evidence, and readiness gates | | **Interactive tutor** | A request for a lesson sequence or ongoing teaching | One meaningful unit at a time with explanation, guided work, assessment, and adaptation | | **Practice coach** | A request to improve a skill through exercises or feedback | Deliberate drills, quality criteria, critique, revision, and the next drill | | **Diagnostic** | An unclear starting level or a request to identify gaps | A short assessment, gap analysis, and revised starting point | | **Capstone review** | A request to prove or evaluate capability | A realistic brief, rigorous rubric, review, and improvement loop |

A new topic does not automatically require a mastery blueprint. Use **Deep explainer** when the user's immediate goal is understanding; use **Mastery map** or **Learning plan** when the goal is sustained capability.

Produce a Deep Explanation

Research before synthesis

Use current research when claims may have changed, when the topic is niche or contested, or when the user asks about a current ecosystem, frontier, product, company, standard, law, or recommendation. Prefer official documentation, primary research, standards bodies, direct data, and credible first-party statements. Use strong secondary sources to add interpretation, not to replace an available primary source.

Separate:

  • Stable principles from current implementation details.
  • Documented facts from your synthesis or inference.
  • Marketing claims from mechanisms and observed trade-offs.
  • Broad consensus from active debate.

Cite sources near the claims they support. Do not pad the response with citations for common knowledge, and do not use a list of links as a substitute for explanation.

Build the causal model

Before drafting, identify the central thesis and the few causal relationships that make the rest of the topic intelligible. Explain mechanisms with explicit links such as “because,” “which means,” and “therefore.” Do not present a feature inventory and expect the learner to infer why the features matter.

Use the following sequence when it fits the question; omit irrelevant sections rather than mechanically filling a template:

1. **Lead with the simplest accurate model.** Give the direct answer or a compact analogy in the opening. If using an analogy, state where it stops being accurate. 2. **Separate commonly conflated layers.** Define the important actors, abstractions, or terms and show their responsibilities. Use a compact table when exact mapping is clearer than prose. 3. **Explain the mechanism.** Trace how the system, idea, or phenomenon works from cause to effect. Make hidden constraints and design decisions visible. 4. **Ground it in a concrete example.** Walk through one representative end-to-end case, worked example, or before-and-after comparison. Anchor every major abstraction in something observable. 5. **Explain why it matters.** Connect the mechanism to user, engineering, business, scientific, or social consequences as relevant. 6. **Use multiple perspectives.** When the topic benefits from it, analyze at least two genuinely different lenses—for example technical architecture, developer experience, economics, competitive strategy, operations, history, ethics, or user behavior. Do not relabel the same point as multiple perspectives. 7. **Present altern

Read more
@tamdogood
Ships withbuilder-essential-skills

A repository for skills that are essential to my daily work

Stars on GitHub

Momentum

stars in total
226
Star history of tamdogood/builder-essential-skills on GitHub, a chart by star-history.com

Other skills on builder-essential-skills.

More skills from other plugins.

brainstorming
Auto-invoked

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…

@obra@obraInstall Skill
ask-matt

ask-matt

Ask which skill or flow fits your situation. A router over the skills in this repo.

skills10d309k
@mattpocock@mattpocockInstall Skill
accessibility
Auto-invoked

accessibility

Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels,…

ecc11d293k
@affaan-m@affaan-mInstall Skill
compiler-commit

compiler-commit

Use when you want to verify compiler changes and commit with the correct convention. Runs tests, lint, and format, then commits…

react1d252k
@facebook@facebookInstall Skill

Questions about top-one-percent.

Which coding agents can use top-one-percent?
top-one-percent installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.