skills/writing-revision/SKILL.md
Applies a systematic three-pass revision system (Zinsser, King, Pinker, Clark) to existing drafts — Pass 1 cuts clutter, Pass 2 reduces cognitive load, Pass 3 improves rhythm. Use when revising, editing, or polishing drafts, cutting word count, tightening prose, improving readability, or fixing flow, or when user mentions revision, editing, cut clutter, too wordy, improve readability, fix the flow, reduce word count.
npx skillsauth add lyndonkl/claude writing-revisionInstall 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-stickiness for memorable messaging, writing-pre-publish-checklist for final publishing checks.
Copy this checklist and track your progress:
Three-Pass Revision:
- [ ] Pass 1: Cut clutter (analyze -> improve)
- [ ] Pass 2: Reduce cognitive load (analyze -> improve)
- [ ] Pass 3: Improve rhythm (analyze -> improve)
Before starting: Review resources/revision-guide.md for the complete three-pass system with examples and the full transformation demonstration.
For each pass, analyze the entire draft first and output findings to an analysis file in the current directory, then read that file to make improvements. This ensures complete coverage.
Goal: Cut 10-25% of word count. Make every word earn its place.
Step 1.1 - Analysis: Read entire draft. Create analysis file writer-pass1-clutter-analysis.md identifying all instances of: adverbs (-ly words), qualifiers (very, really, quite, somewhat), passive voice, weak verbs (is, are, was, were, has/have/had), throat-clearing phrases, and cliches. Calculate word count and set target for 10-25% reduction.
Step 1.2 - Improvement: Read analysis file. Work through entire draft making improvements: remove 70% of adverbs, delete qualifiers, convert passive to active voice, replace weak verbs with action verbs, eliminate throat-clearing, remove cliches. Verify word count reduction meets 10-25% target. Ensure every remaining word earns its place.
See resources/revision-guide.md - Pass 1 for detailed examples.
Goal: Make reading effortless. First reading should be correct reading.
Step 2.1 - Analysis: Read entire draft. Create analysis file writer-pass2-cognitive-load-analysis.md identifying all issues: garden-path sentences (temporarily mislead readers), buried topics, subject-verb-object separated by more than 7 words, ambiguous pronouns, broken topic chains, sentences requiring re-reading.
Step 2.2 - Improvement: Read analysis file. Work through entire draft: fix garden-path sentences, signal topic at start of each sentence, keep subject-verb-object close, clarify pronouns, repair topic chains, break overly complex sentences. Read aloud to verify no stumbles.
See resources/revision-guide.md - Pass 2 for detailed examples.
Goal: Create engaging flow through sentence variety and strong endings.
Step 3.1 - Analysis: Read entire draft. Create analysis file writer-pass3-rhythm-analysis.md analyzing: sentence lengths for each paragraph (list actual lengths), monotonous patterns (5+ similar-length sentences in a row), last word of each sentence (mark weak endings), gold-coin placement (identify gaps), opportunities for ladder of abstraction (concrete -> general -> concrete), sections lacking variety.
Step 3.2 - Improvement: Read analysis file. Work through entire draft: add short sentences for emphasis after longer ones, replace weak sentence endings with strong words, distribute gold-coin moments throughout (especially middle), apply ladder of abstraction, vary sentence lengths deliberately. Read aloud to verify flow. Confirm good mix of short, medium, and long sentences.
See resources/revision-guide.md - Pass 3 for detailed examples.
Validate using resources/evaluators/rubric_revision.json. Minimum standard: Average score >= 3.5.
| Pass | Focus | Method | Target | |------|-------|--------|--------| | Pass 1 | Clutter | Zinsser/King | Cut 10-25% word count | | Pass 2 | Cognitive Load | Pinker | No re-reading needed | | Pass 3 | Rhythm | Clark | Varied lengths, strong endings |
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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.