skills/voice-check/SKILL.md
--- name: voice-check description: Scans a substacker draft line-by-line against the canonical voice-profile.md don't-list and signature moves. Emits phrase-level flags with location, quoted phrase, violation type, voice-profile citation, and up-to-2 suggested rewrites per flag. Use as pass-2 skill (voice) after structural-review completes, when a draft reads competent but not in the writer's voice, or when the writer asks "does this sound like me?" Trigger keywords: voice check, delve, unpack,
npx skillsauth add lyndonkl/claude skills/voice-checkInstall 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 the Editor after structural-review. Runs alongside hedge-detector, slop-detector, citation-form-check. Each skill owns a specific band of voice issues; this one handles explicit don't-list phrase matches.
Voice check draft D:
- [ ] Step 1: Load voice-profile.md (global) + voices/{section}.md (if applicable)
- [ ] Step 2: Extract the don't-list as a regex bank
- [ ] Step 3: Scan draft; match each regex with word boundaries
- [ ] Step 4: For each match: record {line, quoted phrase, violation, voice-profile citation, 2 rewrite options}
- [ ] Step 5: Emit phrase-flags table
From voice-profile.md §10 (Voice don'ts). Word-boundary enforced.
| Violation | Regex | Rewrite hint |
|---|---|---|
| delve | \bdelve(s|d|ing)?\b | replace with specific verb |
| unpack | \bunpack(s|ed|ing)?\b | replace with specific verb |
| dive into | \bdive\s+into\b | replace with specific verb |
| let's explore | \blet'?s\s+(explore|dive) | delete; open with confession |
| at the end of the day | \bat\s+the\s+end\s+of\s+the\s+day\b | delete clause |
| game-changer | \bgame[-\s]?changer\b | name the specific change |
| paradigm shift | \bparadigm\s+shift\b | name the shift explicitly |
| under the hood | \bunder\s+the\s+hood\b | replace with "mechanically" or delete |
| in today's fast-paced world | in\s+today'?s\s+(fast[-\s]?paced\s+)?world | delete; open with a dated concrete fact |
| AI is transforming | (?i)AI\s+is\s+transforming | delete generic framing; open with confession |
| emoji | [\u{1F300}-\u{1FAFF}\u{2600}-\u{27BF}] | delete |
| exclamation | ! (in body, not quotes) | convert to period |
| I think (as hedge) | \bI\s+think\b (when followed by a claim, not a question) | commit OR specific hedge |
| clearly / obviously / simply (as persuasion) | \b(clearly|obviously|simply)\b | delete or name the step |
| custom CTA | subscribe\b.*(resonated|more|stay\s+tuned) | delete; Substack boilerplate is fine |
Section overlays can add or override entries. If voices/kalshi-log.md bans "generalizing to other sports", add that rule.
Draft excerpt:
In today's rapidly evolving AI landscape, let's unpack why RAG beats fine-tuning. At the end of the day, RAG is a game-changer. Let's dive into the details!
Flags (all tier-1):
| loc | quote | violation | citation | rewrites | |---|---|---|---|---| | P1 S1 | "In today's rapidly evolving AI landscape" | generic opener | voice-profile §10 don't #2 | (a) delete; (b) replace with a dated concrete fact | | P1 S1 | "let's unpack" | don't-list | §10 don't #1 | (a) "Here is the mechanism" (b) delete | | P1 S2 | "At the end of the day" | don't-list | §10 don't #1 | (a) delete clause | | P1 S2 | "game-changer" | don't-list | §10 don't #1 | (a) "it cuts token budget by 40% on our traces" (b) "it avoids a retrain" | | P1 S3 | "Let's dive into" | don't-list | §10 don't #1 | (a) "Here is what actually happens" (b) delete | | P1 S3 | "!" (exclamation) | don't-list | §10 don't #8 | (a) period |
Six tier-1 flags in three sentences → triggers Editor must-not #13 (≥3 tier-1 voice violations = no-go).
delve).hedge-detector (hedging) and slop-detector (structural voice).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.