Generate support, recommendation, appreciation, acceptance, and dispense (short-stay invitation) letters for students and candidates of Prof. Martin Otis / LAR.i, in LaTeX compiled to PDF. Ingests the candidate's own files (CV, transcript, project description, motivation letter) and highlights both the candidate's dossier and the professor's own experience with the candidate. Trigger on: recommendation letter, support letter, appreciation letter, acceptance letter, invitation letter, dispense / work permit exemption letter, lettre d'appui, lettre de recommandation, lettre d'appreciation, lettre d'acceptation, lettre d'invitation, lettre de dispense, PFLA, ELAP, CRSNG, FRQNT, MITACS, or /recommendation-letter.
Use this skill to search Scopus for academic references, validate existing references, or produce literature reviews. Trigger on: /scopus, mentions of Scopus search, reference validation requests, finding papers on a topic.
Budget-bounded develop-and-improve loop: design, plan, code, comment, test, review, score, and correct in a loop until a composite quality gate is met or a hard budget/iteration/no-progress stop is hit. Keeps the best cloud model (Fable 5) as orchestrator and judge, uses cheaper cloud tiers (Opus to plan, Sonnet to execute and review) for the actions, and delegates code and comments to the local-coder / local-writer agents over the Bash bridge so the heavy generation is free. Trigger on: /loopdev, 'loop engineering', 'run the improve loop', 'develop this until the score/budget', requests to iterate on code under a cost cap with an automatic quality gate.
Two-round multi-model deliberation panel: Gemini and GitHub Copilot debate a near-final draft (via deliberate.py), enriched with Consensus and optional Scopus.AI evidence, after which Claude arbitrates per the canonical protocol, validates new references against Scopus, and writes a Deliberation Log. Used as the standardized Deliberation step inside the academic auditor and researcher agents (scopus-auditor, paper-auditor, thesis-auditor, thesis-proposal-auditor, scopus-researcher, reviewer-response) before the final plan, review, or response is written. Trigger when an agent reaches its deliberation/cross-review step.
Build a spatial map of a literature-review corpus from a BibTeX file. Extracts each paper's study / case-study location automatically (per-DOI Scopus abstract + title + keywords, matched against an offline Natural Earth gazetteer), writes a reviewable draft table with a confidence column, lets a human curate or override it, then renders the corpus as CSV, KML (Google My Maps), GeoJSON (QGIS/Leaflet), a static world map PNG for the review figure, an interactive HTML map, and a per-country count table. Use this skill whenever the user wants to map, geolocate, or geographically situate the papers of a corpus or review; turn a .bib into study_locations.csv or a study-location map; produce a KML/GeoJSON/world-map figure of where studies were conducted; or asks about the geographic coverage, spatial distribution, or country breakdown of a reference set. Triggers on: /geolocalisation, 'map the corpus', 'geolocate the studies', 'carte des études', 'où ont eu lieu les études', 'bib to map', 'study locations map'.
Future-works analysis skill with two modes. Mode audit: review one manuscript's own future-works / conclusion / limitations section (.tex/.md) — presence, testability, link to a stated limitation, and novelty against the literature — and emit flagged findings for the host agent's plan. Mode mine: extract the stated future works / open problems of a corpus from full text (download via download_pdf.py any-format, parse via extract_text.py --section-scan) and synthesize a corpus future-works table (with fit to the review and a Pareto 80/20 effort-vs-impact ranking) plus a research-opportunity list. Used inside the four auditors (audit) and scopus-researcher (mine). Trigger when an agent reaches its future-works / hypothesis step.
Statistical analysis skill with two modes. Mode audit: review the statistics of one manuscript (.tex/.md) — test selection, assumptions, effect size, presentation, and cross-validation between text, tables, and figures — and emit flagged findings for the host agent's plan. Mode mine: extract the reported statistics from the full-text PDFs of a corpus (download via download_pdf.py, parse via extract_text.py) and synthesize a corpus statistics table plus a statistical-improvement opportunity list. Used inside paper-auditor and thesis-auditor (audit) and scopus-researcher (mine). Engineering-default domain profiles with a selectable cosmetic profile. Trigger when an agent reaches its statistical-audit or corpus-statistics step.
Convert a Word .docx template (Mitacs, CRSNG, FRQNT, UQAC, partner forms) into a faithful LaTeX source. Use when the user asks to translate a Word gabarit to LaTeX, when a generated .tex must match a .docx visually, or for /word2latex. Drives pandoc + applies the standard patch sequence (Arial, full-grid tables, centered sections, landscape geometry, first-page banner). Delegates the actual patch work to the word-to-latex agent.