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ai-resources
ai-resources contiene 10 skills recopiladas de LucasBuetje, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Peripheral vision audit for empirical output. Finds what the author cannot see — problems hiding in plain sight (vices) and opportunities being overlooked (virtues). Inspired by Viktor Shklovsky's defamiliarization and conversations with Jason Fletcher. Use when output exists and interpretation is about to happen.
Convene a multi-model "council" to answer a hard question — four different model families answer in parallel, anonymously peer-rank each other, and a Claude Opus chairman synthesizes the final answer. Adaptation of karpathy/llm-council for local CLIs (agy, codex, vibe) plus Claude subagents. Can judge attached material: point it at project files (R scripts, .tex/.md drafts, data dictionaries) or paste content inline, and every member sees it (PDFs are extracted to text by a subagent first). Use when the user wants cross-model consensus, a second/third opinion, or maximum-confidence judgment on open-ended, high-stakes, or contested questions — invoke on "/council", "ask the council", "convene the council", "council on …". Not for quick factual lookups (the fan-out costs ~9 model calls).
Create or edit a Beamer slide deck. Use when the user asks to build, write, or edit a presentation or slide deck.
Expert economics paper writing assistant synthesizing advice from 50+ top guides by Cochrane, McCloskey, Shapiro, Head, Bellemare, Goldin, Glaeser, Kremer, and other leading economists. USE THIS SKILL whenever the user writes, edits, reviews, rewrites, or structures any economics paper, thesis, job market paper, abstract, introduction, conclusion, results section, literature review, or referee response. Also handles LaTeX formatting, presentations, and paper audits. Covers all paper types (applied, theory, structural, mixed) and all sections. Do NOT invoke for tasks that are purely code, data manipulation, or tool configuration with no prose-writing component.
Decision premortem — imagine the plan has already failed, then work backward to find out why. Use before committing to high-stakes, still-reversible decisions (job market strategy, dissertation chapter direction, research design, grant framing). Generates failure scenarios and investigates the most significant ones in depth (parallel subagents in Claude Code; sequential inline in OpenCode/Antigravity), then synthesizes the three most critical risks with concrete revisions.
Systematic audit and cross-language replication of empirical research projects. Performs five audits (code, cross-language replication, directory structure, output automation, econometrics) and files a formal referee report with a Beamer deck.
Structure a response to referee or reviewer comments interactively, one comment at a time. Classifies each point, drafts responses, and waits for user approval before proceeding. Use when responding to referee2 reports, journal referee reports, or co-author feedback. Do not auto-invoke (e.g. at the end of a referee2 run) — always wait for the user to explicitly ask.
Check adapted skills against their upstream GitHub sources for substantial updates. Reads origin fields, fetches upstream versions, compares, flags changes worth adopting. Use when you want to sync adapted skills with their sources.
Locate (via Zotero) and deeply read/summarize an academic research paper — journal article, working paper, preprint — with ONE Claude Opus subagent that reads the WHOLE PDF in a single pass (no chunking). The subagent gets the rendered pages (images) plus the extracted text layer as a grounding anchor and returns a structured extract; page images stay inside the subagent and are discarded on return, so no image tokens reach the main thread. Auto-invoke for a whole-paper summary of a research paper; other paper reads (a targeted section or table) go through a Claude subagent per the PDF Reading rule.
Quick visual-collision check for figures — TikZ inside .tex files OR rendered .png/.jpg/.pdf figures from R/Python. Three checks only. (1) Bezier-curve label collisions via gap math. (2) Label-to-object whitespace. (3) Labels touching or running off the figure edge. These are the visual errors that compile cleanly — pdflatex never warns about them, ggsave never refuses to write them, so the agent that produced them does not know they are wrong. Use after generating any figure where visual correctness matters and you cannot eyeball it yourself.