skills/mapping-visualization-scaffolds/SKILL.md
Creates visual maps that make implicit relationships, dependencies, and structures explicit through diagrams, concept maps, and architectural blueprints. Guides through identifying nodes and relationships, choosing visualization approaches, and validating completeness. Use when complex systems need visual documentation, mapping component relationships and dependencies, creating hierarchies or taxonomies, documenting process flows or decision trees, understanding system architectures, visualizing data lineage or knowledge structures, or when user mentions concept maps, system diagrams, dependency mapping, relationship visualization, or architecture blueprints.
npx skillsauth add lyndonkl/claude mapping-visualization-scaffoldsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Copy this checklist and track your progress:
Mapping Visualization Progress:
- [ ] Step 1: Clarify mapping purpose
- [ ] Step 2: Identify nodes and relationships
- [ ] Step 3: Choose visualization approach
- [ ] Step 4: Create the map
- [ ] Step 5: Validate and refine
Step 1: Clarify mapping purpose
Ask user about their goal: What system/concept needs mapping? Who's the audience? What decisions will this inform? What level of detail is needed? See Common Patterns for typical use cases.
Step 2: Identify nodes and relationships
List all key elements (nodes) and their connections (relationships). Identify hierarchy levels, dependency types, and grouping criteria. For simple cases (< 20 nodes), use resources/template.md. For complex systems (50+ nodes) or collaborative sessions, see resources/methodology.md for advanced strategies.
Step 3: Choose visualization approach
Select format based on complexity: Simple lists for < 10 nodes, tree diagrams for hierarchies, network graphs for complex relationships, or layered diagrams for systems. For large-scale systems or multi-map hierarchies, consult resources/methodology.md for mapping strategies and tool selection. See Common Patterns for guidance.
Step 4: Create the map
Build the visualization using markdown, ASCII diagrams, or structured text. Start with high-level structure, then add details. Include legend if needed. Use resources/template.md as your scaffold.
Step 5: Validate and refine
Check completeness, clarity, and accuracy using resources/evaluators/rubric_mapping_visualization_scaffolds.json. Ensure all critical nodes and relationships are present. Minimum standard: Score ≥ 3.5 average.
Architecture Diagrams:
Concept Maps:
Dependency Graphs:
Hierarchies & Taxonomies:
Flow Diagrams:
Scope Management:
Clarity Over Completeness:
Validation:
Common Pitfalls:
Resources:
resources/template.md - Structured scaffold for creating mapsresources/evaluators/rubric_mapping_visualization_scaffolds.json - Quality criteriaOutput:
mapping-visualization-scaffolds.md in current directorySuccess Criteria:
testing
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.