skills/cross-poster-blurb/SKILL.md
--- name: cross-poster-blurb description: Writes a 60-140 word third-person blurb for the Substack cross-post feature, positioned so another newsletter writer can paste it into their cross-post popup without editing. Third-person throughout ("In this piece, Kushal argues…"). No subscriber CTAs. Use as the cross-post arm of the Distribution Translator. Trigger keywords: cross-post, cross-poster, blurb, Substack cross-post, third person, positioning. --- # Cross-Poster Blurb ## Workflow ``` Wri
npx skillsauth add lyndonkl/claude skills/cross-poster-blurbInstall 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.
Write blurb for cross-post:
- [ ] Step 1: Load spine + voice-profile + audience-notes
- [ ] Step 2: One-line positioning: "In this piece, Kushal argues…"
- [ ] Step 3: 2-3 sentences summarizing argument + one concrete anchor
- [ ] Step 4: One sentence on why a tech-adjacent audience should care
- [ ] Step 5: Sign-off minimal: "Read the full piece." or a period — no subscribe CTA
- [ ] Step 6: Enforce 60-140 words
---
source_post: {slug}.md
platform: substack-crosspost
target_length: 60-140 words
actual_length: {N}
section: {section-slug}
---
In this piece, Kushal argues {thesis}. {2-3 sentences of argument + concrete anchor}. For anyone building with {domain}, the {specific-claim} is the move that reframes the problem. Read the full piece.
---
source_post: architecture-not-prompting.md
platform: substack-crosspost
target_length: 60-140 words
actual_length: 98
section: agent-workshop
---
In this piece, Kushal argues that most prompt-engineering advice mistakes the unit of analysis — the lever that moves behaviour in long-running AI systems is organisational, not linguistic. Drawing on Wang et al., Anthropic (2024), which found multi-agent decompositions outperformed long-prompted single agents by roughly 40% on sustained tasks, he lays out four recurring patterns: supervisor-worker, pipeline, jury, and debate. For anyone building with agents — or watching teams ship agents that don't quite work — the reframing from "better prompt" to "better architecture" is worth the ten-minute read.
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.