skills/abstraction-concrete-examples/SKILL.md
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.
npx skillsauth add lyndonkl/claude abstraction-concrete-examplesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The ladder uses 3-5 levels connecting universal principles to concrete details. Example:
constructor(private repo: IUserRepository) {}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.
For communication across levels:
For validation:
For design:
Do:
Don't:
resources/template.mdresources/methodology.mdresources/examples/api-design.md, resources/examples/hiring-process.mdresources/evaluators/rubric_abstraction_concrete_examples.jsontesting
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.