skills/propose-section/SKILL.md
--- name: propose-section description: Converts one candidate cluster from cluster-corpus-by-theme into a named, promised section proposal ready for writer review. Calls write-section-promise for the one-sentence promise. Rates fit confidence (high / medium / low / provisional) and flags borderline posts. Use once per cluster that passes ≥3-post threshold. Trigger keywords: propose section, section proposal, new section candidate. --- # Propose Section ## Workflow ``` Per qualifying cluster:
npx skillsauth add lyndonkl/claude skills/propose-sectionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Per qualifying cluster:
- [ ] Step 1: Name the cluster (working name from centroid codes)
- [ ] Step 2: Call write-section-promise for the one-sentence promise
- [ ] Step 3: Score each member post: tight | fair | borderline
- [ ] Step 4: Check non-overlap against existing section-map.md and other proposals this run
- [ ] Step 5: Assign fit_confidence:
- high: ≥5 tight-fit posts + strong cohesion + unambiguous non-overlap
- medium: 3-4 posts with mixed fit + narrowing promise
- low: borderline throughout; defer
- provisional: confident enough to name, uncertain enough to need probation
- [ ] Step 6: Write proposal block with reasons_to_reject (steelman the case against)
proposal:
name: "{Human name}"
slug: {kebab-case}
promise: "{one sentence}"
fit_confidence: high | medium | low | provisional
supporting_posts: [{slug, fit}]
borderline_posts: [{slug, reason}]
non_overlap_check: "Distinct from {other section} because..."
reasons_to_reject: "Two of these posts also fit {other section}. If those migrate, cluster drops to 3 posts and becomes marginal."
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.