skills/translation-reframing-audience-shift/SKILL.md
Adapts content for different audiences while preserving core accuracy, changing tone, depth, emphasis, and framing to match audience expertise and goals. Use when technical content needs business framing, strategic vision needs tactical translation, expert knowledge needs simplification, formal content needs casual tone, long-form needs summarization, internal content needs external framing, or cross-cultural adaptation is needed. Use when user mentions "explain to", "reframe for", "translate for [audience]", "adapt for [executives/engineers/customers]", or "same content, different audience".
npx skillsauth add lyndonkl/claude translation-reframing-audience-shiftInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Copy this checklist and track your progress:
Translation & Reframing Progress:
- [ ] Step 1: Analyze source and target audiences
- [ ] Step 2: Identify translation type and constraints
- [ ] Step 3: Apply translation strategy
- [ ] Step 4: Validate fidelity and appropriateness
- [ ] Step 5: Refine and deliver
Step 1: Analyze source and target audiences
Characterize both audiences using Audience Analysis framework (expertise, goals, context, constraints). Identify gap between source and target.
Step 2: Identify translation type and constraints
Classify as: technical↔business, strategic↔tactical, expert↔novice, formal↔informal, long↔short, internal↔external, or cross-cultural. See Common Translation Types for patterns.
Step 3: Apply translation strategy
For simple cases → Use resources/template.md for structured translation. For complex cases (multiple audiences, high stakes, nuanced reframing) → Study resources/methodology.md for advanced techniques.
Step 4: Validate fidelity and appropriateness
Self-assess using resources/evaluators/rubric_translation_reframing_audience_shift.json. Check: semantic accuracy preserved? tone appropriate? emphasis aligned with audience priorities? See Validation section.
Step 5: Refine and deliver
Create translation-reframing-audience-shift.md with source, target audience, translated content, and translation rationale. See Delivery Format.
Before translating, characterize source and target:
1. Expertise Level
2. Primary Goals
3. Context & Constraints
4. Cultural/Demographic
Mapping exercise: Source audience is [expertise/goals/context] → Target audience is [expertise/goals/context] → Gap requires [translation strategy].
Technical → Business:
Business → Technical:
Strategic → Tactical:
Tactical → Strategic:
Expert → Novice:
Novice → Expert:
Formal → Informal:
Informal → Formal:
Long → Summary:
Summary → Long-form:
Before finalizing, check:
Semantic Fidelity (highest priority):
Audience Appropriateness:
Emphasis Alignment:
Medium & Format:
Cultural/Demographic:
Minimum Standard: Use rubric (resources/evaluators/rubric_translation_reframing_audience_shift.json). Average score ≥ 3.5/5 before delivering.
Create translation-reframing-audience-shift.md with:
1. Source Analysis
2. Target Analysis
3. Translated Content
4. Translation Rationale
5. Validation Notes
"So What?" Test (Technical → Business): Every technical detail answers "so what?" - "Migrated to Kubernetes" → "Auto-scale during traffic spikes, 30% cost reduction" | "OAuth 2.0" → "Enterprise SSO, removes adoption barrier"
"How?" Test (Strategic → Tactical): Every goal answers "how?" - "Improve satisfaction" → "Response <2hr, add help center, NPS survey" | "AI-first company" → "Train PMs (Q1), hire 3 ML engineers (Q2), pilot feature (Q3)"
Analogy Bridge (Expert → Novice): Familiar → Unfamiliar - "Git branching" = essay draft versions | "Microservices" = food trucks not one restaurant | "API rate limiting" = nightclub capacity
Inverted Pyramid (Long → Summary): Most important first - Lede (1-2 sentences) → Key details (2-3 bullets) → Supporting (optional depth)
Code-Switching (Cross-Cultural): Replace cultural references - "Home run" (US) → "Big success" (neutral) | "Fire hose" idiom → "Overwhelming info" (literal) | MM/DD/YYYY → YYYY-MM-DD (ISO)
Resources:
Key Principles:
Red Flags:
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.