abstraction-concrete-examples
$
npx mdskill add lyndonkl/claude/abstraction-concrete-examplesTranslate abstract principles into concrete examples across multiple levels.
- Bridges communication gaps between strategy and execution.
- Tests ideas against scenarios to reveal hidden assumptions.
- Decomposes complex problems into actionable implementation steps.
- Delivers layered documentation with clear progression examples.
SKILL.md
.github/skills/abstraction-concrete-examplesView on GitHub ↗
---
name: abstraction-concrete-examples
description: Builds structured abstraction ladders that translate high-level principles into concrete, actionable examples across 3-5 levels. Bridges communication gaps, reveals hidden assumptions, and tests whether abstract ideas work in practice. Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps.
---
# Abstraction Ladder Framework
## Table of Contents
- [Workflow](#workflow)
- [1. Gather Requirements](#1-gather-requirements)
- [2. Choose Approach](#2-choose-approach)
- [3. Build the Ladder](#3-build-the-ladder)
- [4. Validate Quality](#4-validate-quality)
- [5. Deliver and Explain](#5-deliver-and-explain)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#quick-reference)
The ladder uses 3-5 levels connecting universal principles to concrete details. Example:
- L1: "Software should be maintainable"
- L2: "Use modular architecture"
- L3: "Apply dependency injection"
- L4: "UserService injects IUserRepository"
- L5: `constructor(private repo: IUserRepository) {}`
## Workflow
Copy this checklist and track your progress:
```
Abstraction Ladder Progress:
- [ ] Step 1: Gather requirements
- [ ] Step 2: Choose approach
- [ ] Step 3: Build the ladder
- [ ] Step 4: Validate quality
- [ ] Step 5: Deliver and explain
```
**Step 1: Gather requirements**
Ask the user to clarify topic, purpose, audience, scope (suggest 4 levels), and starting point (top-down, bottom-up, or middle-out). This ensures the ladder serves the user's actual need.
**Step 2: Choose approach**
For straightforward cases with clear topics → Use `resources/template.md`. For complex cases with multiple parallel ladders or unusual constraints → Study `resources/methodology.md`. To see examples → Show user `resources/examples/` (api-design.md, hiring-process.md).
**Step 3: Build the ladder**
Create `abstraction-concrete-examples.md` with topic, 3-5 distinct abstraction levels, connections between levels, and 2-3 edge cases. Ensure top level is universal, bottom level has measurable specifics, and transitions are logical. Direction options: top-down (principle → examples), bottom-up (observations → principles), or middle-out (familiar → both directions).
**Step 4: Validate quality**
Self-assess using `resources/evaluators/rubric_abstraction_concrete_examples.json`. Check: each level is distinct, transitions are clear, top level is universal, bottom level is specific, edge cases reveal insights, assumptions are stated, no topic drift, serves stated purpose. Minimum standard: Average score ≥ 3.5. If any criterion < 3, revise before delivering.
**Step 5: Deliver and explain**
Present the completed `abstraction-concrete-examples.md` file. Highlight key insights revealed by the ladder, note interesting edge cases or tensions discovered, and suggest applications based on their original purpose.
## Common Patterns
**For communication across levels:**
- Share L1-L2 with executives (strategy/principles)
- Share L2-L3 with managers (approaches/methods)
- Share L3-L5 with implementers (details/specifics)
**For validation:**
- Check if L5 reality matches L1 principles
- Identify gaps between adjacent levels
- Find where principles break down
**For design:**
- Use L1-L2 to guide decisions
- Use L3-L4 to specify requirements
- Use L5 for actual implementation
## Guardrails
**Do:**
- State assumptions explicitly at each level
- Test edge cases that challenge the principles
- Make concrete levels truly concrete (numbers, measurements, specifics)
- Make abstract levels broadly applicable (not domain-locked)
- Ensure each level is understandable given the previous level
**Don't:**
- Use vague language ("good", "better", "appropriate") without defining terms
- Make huge conceptual jumps between levels
- Let different levels drift to different topics
- Skip the validation step (the rubric check ensures quality)
- Front-load expertise - explain clearly for the target audience
## Quick Reference
- **Template for standard cases**: `resources/template.md`
- **Methodology for complex cases**: `resources/methodology.md`
- **Examples to study**: `resources/examples/api-design.md`, `resources/examples/hiring-process.md`
- **Quality rubric**: `resources/evaluators/rubric_abstraction_concrete_examples.json`
More from lyndonkl/claude
- academic-letter-architectGuides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
- adr-architectureDocuments significant architectural and technical decisions with full context, alternatives considered, trade-offs analyzed, and consequences understood. Creates a decision trail that helps teams understand why decisions were made. Use when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when user mentions ADR, architecture decision, technical decision record, or decision documentation.
- adverse-selection-priorProduces a Bayesian prior probability that an offered transaction is +EV for the recipient, given that the counterparty chose to propose it. Applies Akerlof market-for-lemons logic -- if they offered it, they believe it is +EV for them, so the prior that it is +EV for us is materially below 50%. Reusable across trade evaluation, waiver drops (another team dropping a player is also adverse selection), job-offer analysis, M&A, and any "someone offered me this" situation. Use when you receive an unsolicited trade/offer/proposal, analyzing incoming trade prior, evaluating why a counterparty proposed a deal, or when user mentions adverse selection, market for lemons, why did they offer this, incoming trade prior, they proposed it, Bayesian adjustment on received offer.
- alignment-values-north-starCreates actionable alignment frameworks that give teams a shared North Star (direction), values (guardrails), and decision tenets (behavioral standards). Enables autonomous decision-making while maintaining organizational coherence. Use when starting new teams, scaling organizations, defining culture, establishing product vision, resolving misalignment, creating strategic clarity, or when user mentions North Star, team values, mission, principles, guardrails, decision framework, or cultural alignment.
- analogy-weight-checkFor every analogy in a substacker draft, verifies it carries mechanical weight — the analogy does real work explaining the mechanism, not merely decorates it. Cross-references analogy-catalog.md for novelty (is this analogy reused from a prior post?) and domain fit (biology > organizational > sports preferred; physics/military disfavored). Use whenever an analogy appears in the draft. Trigger keywords: analogy weight, decorative, mechanical weight, reused analogy, catalog check, metaphor check.
- answer-uncomfortable-questionTakes one strategic question about substacker ("should we launch paid?", "is this section dead?", "are we writing for the wrong audience?") and produces the mandatory evidence + reasoning + downside triad plus a recommendation. Used 3 times per Growth Strategist review. Trigger keywords: uncomfortable question, strategic question, evidence reasoning downside, triad.
- attribute-performanceFor each substacker post that materially over- or under-performs the rolling baseline (|z| ≥ 1.0), produces a plain-English attribution paragraph with calibrated confidence (high / medium / low / unexplained). Considers subject-line effect, topic zeitgeist, external share, day-of-week, length effect, and audience-notes signals. Labels unexplained outliers explicitly rather than fabricating a story. Use after compute-baseline when outlier posts exist. Trigger keywords: attribution, why did this post work, outlier explanation, performance analysis.
- auction-first-price-shadingComputes the optimal shaded bid for a first-price sealed-bid auction given a true private value, an estimate of the number of competing bidders N, and a value-distribution assumption. Implements the `(N-1)/N` equilibrium shading rule for uniform private values, adjusts for log-normal or empirical value distributions, layers a risk-aversion adjustment, and caps output against the bidder's remaining budget. Domain-neutral auction theory reusable across fantasy sports (baseball FAAB, NBA/NHL waiver auctions), prediction-market limit sizing, sealed procurement bids, and any blind-bid context. Use when user mentions "first-price auction bid", "sealed bid shading", "(N-1)/N", "FAAB bid amount", "auction shading", "optimal bid first-price", "bid for sealed-bid", "blind bid sizing", or when downstream logic needs a principled shade factor rather than an ad-hoc heuristic.
- auction-winners-curse-haircutApplies a Bayesian haircut to a bid valuation for common-value auctions where winning is itself evidence the bidder over-estimated. Takes a raw valuation, a value-type classification (common_value / private_value / mixed), the number of informed bidders N, and a signal-dispersion estimate, and returns an adjusted valuation. Domain-neutral and reusable across fantasy FAAB, prediction markets, M&A bids, ad-auction budgets, and any generic bidding context. Use when user mentions "winner's curse", "common value auction", "valuation haircut", "adverse valuation", "Bayesian bid adjustment", or "over-paying in auction".
- audit-driftChecks every post currently assigned to a substacker section against that section's promise and flags posts that no longer fit. Distinguishes acceptable-stretch (minor) from borderline (surface for review) from genuine-drift (violates promise). Never reassigns automatically — only flags. Use on every Curator run where at least one section already exists. Trigger keywords: drift, drift audit, section fit, promise violation, post in wrong section.