skills/synthesis-and-analogy/SKILL.md
Synthesizes information from multiple sources into coherent insights and applies analogical reasoning to transfer knowledge across domains. Use when conducting literature reviews, integrating stakeholder feedback, reconciling conflicting viewpoints, identifying cross-source patterns, creating explanatory analogies ("X is like Y"), finding creative solutions through cross-domain transfer, or testing whether analogies hold (surface vs deep). Use when user mentions "synthesize", "combine sources", "analogy", "similar to", "transfer from", "integrate findings".
npx skillsauth add lyndonkl/claude synthesis-and-analogyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Copy this checklist and track your progress:
Synthesis & Analogy Progress:
- [ ] Step 1: Clarify goal and gather sources/domains
- [ ] Step 2: Choose approach (synthesis, analogy, or both)
- [ ] Step 3: Apply synthesis or analogy techniques
- [ ] Step 4: Test quality and validity
- [ ] Step 5: Refine and deliver insights
Step 1: Clarify goal
For synthesis: What sources? What question are we answering? What conflicts need resolving? For analogy: What's source domain (familiar)? What's target domain (explaining)? What's goal (explain, solve, ideate)? See Common Patterns for typical goals.
Step 2: Choose approach
Synthesis only → Use Synthesis Techniques. Analogy only → Use Analogy Techniques. Both → Start with synthesis to find patterns, then use analogy to explain or transfer. For straightforward cases → Use resources/template.md. For complex multi-domain synthesis → Study resources/methodology.md.
Step 3: Apply techniques
For synthesis: Identify themes across sources, note agreements/disagreements, resolve conflicts via higher-level framework, extract patterns. For analogy: Map structure from source to target (what corresponds to what?), identify shared relationships (not surface features), test mapping validity. See Synthesis Techniques and Analogy Techniques.
Step 4: Test quality
Self-assess using resources/evaluators/rubric_synthesis_and_analogy.json. Synthesis checks: captures all sources? resolves conflicts? identifies patterns? adds insight? Analogy checks: structure preserved? deep not surface? limitations acknowledged? helps understanding? Minimum standard: Score ≥3.5 average.
Step 5: Refine and deliver
Create synthesis-and-analogy.md with: synthesis summary (themes, agreements, conflicts, patterns, new insights) OR analogy explanation (source domain, target domain, mapping table, what transfers, limitations), supporting evidence from sources, actionable implications.
Thematic Synthesis (identify recurring themes):
Conflict Resolution Synthesis (reconcile disagreements):
Pattern Identification (find cross-cutting insights):
Example: Synthesizing 10 postmortems → Pattern: 80% of incidents involve config change + lack of rollback plan. Outliers: 2 incidents hardware failure. Meta-insight: Need config change review process + automatic rollback capability.
Structural Mapping Theory:
Surface vs Deep Analogies:
Example - Surface: "Brain is like computer (both process information)" - too vague, doesn't help Example - Deep: "Brain neurons are like computer transistors: neurons fire/don't fire (binary), connect in networks, learning = strengthening connections (weights). BUT neurons are analog/probabilistic, computer precise/deterministic" - preserves structure, acknowledges limits
Analogy Quality Tests:
Pattern 1: Literature Review Synthesis
Pattern 2: Multi-Stakeholder Synthesis
Pattern 3: Explanatory Analogy
Pattern 4: Cross-Domain Problem-Solving
Pattern 5: Creative Ideation via Analogy
Synthesis Quality:
Analogy Quality:
Avoid:
Inputs Required:
For synthesis:
For analogy:
Techniques to Use:
Synthesis:
Analogy:
Outputs Produced:
synthesis-and-analogy.md with:
Resources:
Minimum Quality Standard:
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.