skills/writing-stickiness/SKILL.md
Applies the Heath brothers' SUCCESs model (Simple, Unexpected, Concrete, Credible, Emotional, Stories) to make messages memorable and persuasive, with systematic analysis, targeted improvements, and scoring (0-18 stickiness scorecard). Use when making messages more memorable or compelling, preparing presentations, crafting pitches or campaigns, or when user mentions stickiness, making ideas stick, persuasion, SUCCESs framework, or Heath brothers.
npx skillsauth add lyndonkl/claude writing-stickinessInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Related skills: Use writing-structure-planner for planning structure, writing-revision for prose revision, writing-pre-publish-checklist for final quality checks.
Copy this checklist and track your progress:
Stickiness Enhancement:
- [ ] Step 1: Analyze against SUCCESs framework
- [ ] Step 2: Improve weak principles
- [ ] Step 3: Score and refine
Before starting: Review resources/success-model.md for the complete SUCCESs framework with all 6 principles, stickiness scorecard, and before/after examples.
Analyze the entire document first and output findings to an analysis file in the current directory, then read that file to make improvements. This ensures complete coverage.
Step 1: Analyze against SUCCESs framework
Step 1.1: Read entire draft. Create analysis file writer-stickiness-analysis.md assessing the document against all 6 SUCCESs principles:
Step 1.2: Calculate total current stickiness score out of 18. Present findings to user.
See each principle's section in resources/success-model.md for detailed scoring guidance.
Step 2: Improve weak principles
Step 2.1: Read analysis file. Identify the 2-3 weakest principles (scored 0-1).
Step 2.2: Work through entire draft making targeted improvements for each weak principle:
Step 2.3: Present improved version to user with changes highlighted.
See resources/success-model.md for specific techniques and examples for each principle.
Step 3: Score and refine
Step 3.1: Score the revised message using the Stickiness Scorecard.
Step 3.2: Aim for 12+/18 for good stickiness, 15+/18 for excellent. If score is below 12, identify the weakest 2 principles and do another improvement pass focusing on those.
Step 3.3: Present final scored version with before/after comparison.
See resources/success-model.md - Complete Example for transformation patterns.
Validate using resources/evaluators/rubric_stickiness.json. Minimum standard: Average score >= 3.5.
| Principle | Key Question | Technique | |-----------|-------------|-----------| | Simple | What's the ONE core idea? | Commander's intent in 12 words | | Unexpected | What will surprise readers? | Schema violation + curiosity gaps | | Concrete | Can readers visualize it? | Sensory details, specific examples | | Credible | Why should readers believe it? | Human-scale stats, testability | | Emotional | Why should readers care? | Individual focus, identity appeal | | Stories | Can readers simulate the experience? | Challenge/connection/creativity plots |
Scoring: Each principle rated 0-3. Total out of 18. Target 12+ for good, 15+ for excellent.
Requirements:
Common pitfalls:
Key resources:
Inputs required:
Outputs produced:
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.