skills/opener-critique/SKILL.md
--- name: opener-critique description: Evaluates the first 1-3 sentences of a substacker draft against the writer's signature opener patterns — confession / "I hadn't done X" / reframe / small concrete admission. Classifies opener as confession | reframe | admission | news-hook | generic-opener and flags news-hook/generic as tier-1. The opener sets the voice contract for the essay. Use on every draft. Trigger keywords: opener, hook, first sentence, opening, confession opener, news hook, generic
npx skillsauth add lyndonkl/claude skills/opener-critiqueInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Related skills: Called by Editor in structural pass. Sets tone contract; downstream skills (voice-check, slop-detector S1) can reference its classification.
| Class | Markers | Voice verdict |
|---|---|---|
| confession | I hadn't, I used to think, I did not know, Until last week, I spent three hours, I was wrong about | PASS |
| reframe | X is commonly called Y — actually Z, Most people think X; actually Y | PASS |
| admission | I substitute learning for doing, I have been meaning to, I have opinions about X — the kind that feel like knowledge | PASS |
| puzzle (biology cold open) | Biological/systems question opened with a specific number or contradiction | PASS |
| epistolary | Dear friend, Dear reader, second-person address to a specific imagined reader | PASS (rare but valid) |
| news-hook | GPT-5 launched, Last week OpenAI, With the release of X, reacting to an external event | FLAG tier-1 |
| generic-opener | AI is transforming, In a world where, In today's fast-paced, As we enter a new era | FLAG tier-1 |
Evaluate opener:
- [ ] Step 1: Extract first 1-3 sentences
- [ ] Step 2: Match against classifier marker lists
- [ ] Step 3: Assign class
- [ ] Step 4: Write one-line justification
- [ ] Step 5: If news-hook or generic-opener, produce 2 rewrite options in the confession/admission register
Draft opener (bad): "In today's rapidly evolving AI landscape, teams face a critical choice between RAG and fine-tuning for domain knowledge."
Classification: generic-opener. Tier-1.
Rewrites:
Draft opener (good): "I spent a week re-prompting a customer-service agent before I realised the agent was the wrong unit of analysis."
Classification: confession (I spent, I realised). PASS.
Draft opener (puzzle — valid): "Your immune system can recognize roughly ten trillion distinct molecular threats. It does this with a genome that contains fewer than twenty thousand protein-coding genes. The math should not work."
Classification: puzzle. PASS.
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.