Skills

Chapter 04 of the ResearchTools manual. Back to table of contents.

Skills bundle scripts and references the agents reuse. Fifteen ship in this repo, and every one of them is written here.

This repository vendors no skill it did not write. Two were vendored on 2026-08-30 and both were removed the same day, because each already had its own delivery path and the copy only created a second one that would drift:

• tech-debt is delivered by the engineering@knowledge-work-plugins plugin, which both .claude/settings.template.json and .claude/settings.json declare in enabledPlugins. The vendored file was byte-identical to the plugin's own, which Claude Code serves from ~/.claude/plugins/cache/. A plugin declaration is the delivery mechanism; a copy is not.
• graphify ships with the graphify tool, installed on the machine with uv tool install graphifyy. The skill is a property of that installation, not of this repository.

The consequence is stated rather than hidden: a clone whose machine has neither the plugin enabled nor the graphify tool installed gets local-writer referring to a code graph it cannot reach. That is a machine-setup gap, answered by the Prerequisites table in 01-installation.md, and not something a copied SKILL.md fixes - a vendored copy of a CLI's skill without the CLI is instructions for a tool that is still absent. test_settings_template_distribution.py asserts that neither name reappears under .claude/skills/.

In this chapter: scopus · geolocalisation · recommendation-letter · narrative-cv · paper2talk · the two memories · obsidian-cli · latex-hygiene · opt-local-vram-llm

Skill	Purpose	Entry point
scopus	Search Scopus (citation-ordered, title/abstract/keyword scoped), validate references (ambiguity-flagged), fetch PDFs via the Elsevier REST API (with a Semantic Scholar fallback).	/scopus, .claude/skills/scopus/SKILL.md
scientific-writing	Core writing skill: scientific manuscripts in flowing IMRAD prose with verified citations (IEEE/APA/AMA/Vancouver) and reporting guidelines (CONSORT/STROBE/PRISMA).	.claude/skills/scientific-writing/SKILL.md
scholar-evaluation	ScholarEval framework — scores research work across problem formulation, literature review, methodology, data, analysis, results, writing, citations.	.claude/skills/scholar-evaluation/SKILL.md
deliberation	Two-round Gemini ↔ GitHub Copilot debate over a near-final draft; Claude arbitrates and validates any new references against Scopus. Used inside the auditor/researcher agents.	.claude/skills/deliberation/SKILL.md
extract-statistic	Statistical analysis. Mode audit: review a manuscript's own statistics (test selection, assumptions, effect size, presentation, cross-validation). Mode mine: extract the reported statistics of a corpus's full-text PDFs and synthesize a corpus statistics table plus an improvement-opportunity list. Engineering-default domain profiles. Used inside paper-auditor / thesis-auditor (audit) and scopus-researcher (mine). Also carries parse_cache.py (content-addressed PDF-parse cache, additive to every consumer) and the opt-in corpus_index.py (deterministic chunker, injected embedder, pgvector store for ad-hoc cross-corpus retrieval; every hit is provenance, never a citation).	.claude/skills/extract-statistic/SKILL.md
extract-futureworks	Future-works analysis (reuses extract_text.py --section-scan). Mode audit: review a work's own future works (presence, testability, link-to-limitation, novelty) and validate its hypotheses against the cited-corpus future works, proposing stronger ones. Mode mine: extract every corpus paper's stated future works, build a review-fit table, Pareto 80/20-rank it (low effort, high impact first), and emit a research-opportunity list. Used inside the four auditors (audit) and scopus-researcher (mine), where it is a hard gate: no hypothesis/project without it.	.claude/skills/extract-futureworks/SKILL.md
extract-contributions	What a cited paper says it CONTRIBUTES, out of its full text (reuses extract_text.py; ships no reader). Mode validate: pair every \cite{} of a manuscript with the paper's own contribution sentences and flag the citations the paper does not support. Mode mine: tabulate a refs/ corpus by kind. Surfaces evidence verbatim and decides nothing; ok / no-contribution / empty / unreadable are kept apart so a retrieval failure is never read as a property of the paper. Markers are data in contribution_markers.json.	.claude/skills/extract-contributions/SKILL.md
extract-paper-idea	Extract a paper's OWN content into one JSON, the basis for drafting or refreshing its abstract (or, for a UQAC thesis, its Résumé + Abstract pair). Ships no reader of its own: reuses paper2talk's \input/\include flattening and section map, extract-statistic's future-works-cue section scan, and extract-contributions' marker scan (pointed at the paper's own text instead of a cited one). Detects document type (uqac.cls on the flattened text) and the document's own language; never a forced bilingual pair for a paper, always both languages for a thesis. Delegates the drafting to the abstract-writer agent.	/abstract, .claude/skills/extract-paper-idea/SKILL.md
paper2talk	Accepted paper -> conference talk. Asks six questions before reading the paper (audience, duration, output target, aspect ratio, PDF format, deck ending), echoes a build contract, then builds ONE talk_model.json and renders it to PowerPoint (pptxgenjs), LaTeX Beamer on the lab gabarit, or a self-contained web page. Speaker notes budgeted at 130 wpm aiming under the slot, figures re-exported through the draw.io CLI at scale 3, and a visual QA loop (PowerPoint COM -> pdftoppm) with a legibility gate and a no-text-only-slide rule. Delegates the loop to the talk-builder agent.	/talk, .claude/skills/paper2talk/SKILL.md
word2latex	Convert a Word .docx template (Mitacs, CRSNG, FRQNT, UQAC, partner forms) into a faithful LaTeX source. Delegates the patch work to the word-to-latex agent.	/word2latex, .claude/skills/word2latex/SKILL.md
drawio2tikz	Convert one .drawio sheet into a coordinate-exact TikZ fragment (absolute coordinates, edge anchoring, braces, rotation, FR→EN --translate). The sanctioned absolute-coordinate exception to the hand-authored TiKZ rules.	/drawio2tikz, .claude/skills/drawio2tikz/SKILL.md
geolocalisation	Map a review corpus in space from its .bib: resolve each paper's study/case-study site (per-DOI Scopus abstract + title + keywords, optional --full-text PDF scan via download_pdf.py, matched against an offline Natural Earth gazetteer), write a reviewable draft table with a confidence column and a per-paper provenance note, then render CSV, KML (Google My Maps), GeoJSON (QGIS/Leaflet), a world-map PNG, an interactive HTML map, and a per-country count table. Human-reviewed; an override CSV always wins.	/geolocalisation, .claude/skills/geolocalisation/SKILL.md
loop-engineer	Budget-bounded develop-and-improve loop (Agent SDK driver): design → plan → code → comment → test → review → score → correct, looping until a composite gate (tests green, no CRITICAL/HIGH, score >= min) or a hard budget/max-iters/no-progress stop. Fable 5 orchestrates; Opus/Sonnet act; local-coder/local-writer do local generation. loop_audit.py aggregates the installed reviewers into a 0-100 score with a security hard floor; merge to a protected branch is human-gated.	/loopdev, .claude/skills/loop-engineer/SKILL.md
recommendation-letter	Generate support, recommendation, appreciation, acceptance, and dispense (short-stay invitation) letters in LaTeX → PDF from a candidate's files. Two tracks: Claude authors the four persuasive types (fr/en); a stdlib-only Python script fills the fixed French acceptance/dispense forms (candidate status, funding provider, 120-day work-permit exemption, paired output). Sample data is synthetic.	/recommendation-letter, .claude/skills/recommendation-letter/SKILL.md
narrative-cv	Draft, refresh, or tailor the narrative "CV descriptif" (FRQ CV-FRQ, structurally identical to the tri-agency CIHR/NSERC/SSHRC CV commun des trois organismes) to one grant competition: three sections, up to 10 items in section 2, 6-page FR / 5-page EN cap. Built around a durable master contributions inventory in the researcher's own external project folder, refreshed via Scopus (AU-ID two-step) + extract-contributions and re-ranked per competition by keyword overlap against that program's own objectives/evaluation criteria rather than rebuilt from scratch each time. Renders LaTeX/PDF and a plain-text companion for the new-FRQnet-portal paste-in channel from one JSON model (paper2talk's one-model-many-renderers pattern).	/cv, .claude/skills/narrative-cv/SKILL.md
obsidian-cli	Read and search the Obsidian vault through the allowed command surface only (read, search, list, property:get/property:set, tasks, links, tags, move, rename); a captured learning is deposited to the outbox, the single write path, instead of calling a write command directly. The direct CLI write commands (create, append, prepend, plus eval, dev:*, plugin:install, theme:install, sync*) are forbidden for measured reasons: the failure sits in the whole JSON header, not the content (a 3850-byte header passes, 4343 does not, and 4096, a Windows named-pipe buffer, falls between); the CLI exits 0 on that failure too; and create on an existing file writes a numbered duplicate instead of failing.	.claude/skills/obsidian-cli/SKILL.md
latex-hygiene	Measure LaTeX manuscript hygiene mechanically: forbidden characters, an AI-usage risk score, prose and track-changed word counts, abstract length, brace/\begin-\end balance, changes-macro paragraph-crossing corruption, and label/citation coverage (citecov against a .bib, refcov for uncited labels, dangling refs, and duplicate labels). Backs the aiscan/wc checks that paper-auditor and submit-checker already describe in prose, so the same signal table and score formula are computed the same way every time. The write side applies a machine-readable audit plan (patch), scans for post-write corruption (scan), and resolves and builds the tracked or accepted PDF (accept, build).	/texcheck, .claude/skills/latex-hygiene/SKILL.md
form-service (was uqac-forms, renamed 2026-09-25)	Stateless mechanics for official PDF forms from any institution. RT-1 ships the validated ingest contract: https only re-checked on every redirect hop, at most 5 redirects followed manually, a 25 MiB cap enforced during the stream, %PDF magic bytes, a 30 s timeout, and an atomic write. The form catalogue, field maps and profile live in ThesisTracker, not here. Filling (RT-3), PAdES signing (RT-4) and signature validation follow. RT-5 exposes all of it as a stateless HTTP API in deploy/form-service/ (/pdf/widgets, /pdf/fill, /pdf/sign, /pdf/validate), shared-secret gated, no CORS, nothing persisted. See chapter 09 for the boundary with ThesisTracker.	Yes
graphify (external: uv tool install graphifyy, not shipped here)	The code-graph memory: turn a folder of files into a queryable knowledge graph, then ask it what calls what, how one node reaches another, and what a symbol is. query, path and explain are deterministic traversals of graphify-out/graph.json and cost no model at all, which is why one graph query beats grepping file by file. Reached only through local-writer, like the vault. The CLI itself is a separate install (uv tool install graphifyy); this directory is the SKILL, kept here so a clone is never told to consult a graph it has no way to reach. .graphify_version records the version it was generated from - refresh the copy after upgrading the CLI.	/graphify, .claude/skills/graphify/SKILL.md
opt-local-vram-llm	Tune a local Ollama model for this GPU: retain the largest num_ctx that keeps the model 100 percent resident in VRAM, among configurations whose decode throughput clears a floor (default 0.90 of the best admissible run). Reads the manifest and daemon facts read-only, renders a tuned Modelfile, sweeps num_ctx against OLLAMA_KV_CACHE_TYPE (restarting the daemon per value and proving the restart took effect from server.log, restoring the original value on failure), then declares the tuned tag as a role candidate in local-models.json. Stops before qualification, which stays with model_resolver.py --qualify.	/opt-local-vram-llm, .claude/skills/opt-local-vram-llm/SKILL.md
aider-setup	Set up, tune and run the aider nightly local-coding pipeline — a second, independent local-coding harness that needs no Claude Code: two local Ollama models (a writer that codes and tests, a reviewer that never edits, gated by measured token budgets) run one aider process per plan overnight, driven by aider-plan.ps1/aider-night.ps1. Owns the packaging pipeline (scripts/build/) that assembles the student-facing aider-kit.zip from this skill's own canonical sources, with a leak scan and an install-and-dry-run gate. config/rules.md is generated at build time from this repository's own .claude/rules/ (R26 fixes the plan-file shape every harness reads; R27 the function-header convention). See chapter 06 for the full picture.	.claude/skills/aider-setup/SKILL.md

/scopus — Scopus academic search

Searches the Scopus database via the Elsevier REST API. Requires SCOPUS_API_KEY (see the API-keys table in 02-profiles.md) and an active institutional network connection (campus or VPN), or an --insttoken.

Command	What it does
/scopus <topic>	Search top papers on a topic — ordered by citation count, and bare keywords are scoped to title/abstract/keywords. Add --sort recent when you want the newest papers instead
/scopus review <topic>	Structured literature review with inline citations
/scopus validate <DOI or title>	Confirm a reference exists. Flags ambiguous when several records share the title, so results[0] is never taken on faith
/scopus cite <DOI>	Citation count + full metadata for one paper
/scopus author AU-ID(<digits>)	Author profile: document count, affiliation, computed h-index, top papers. Works on a Search-only key
/scopus author <name>	Same, when the key is entitled for the Author Search API; otherwise degrades to Semantic Scholar candidates and says so (no Scopus AU-ID can be resolved from a name without that entitlement)
/scopus journal <name or ISSN>	Journal SJR quartile, CiteScore, subject areas

Files: - .claude/skills/scopus/SKILL.md - .claude/skills/scopus/scripts/scopus_api.py — Scopus REST client. search orders by -citedby-count (--sort recent for date order; the alias is required because argparse refuses a value starting with a dash) and wraps a query naming no Scopus field in TITLE-ABS-KEY(); without both, a search answered only this year's least-cited papers, and citation ordering on an unqualified query answered the most-cited papers of all science. validate queries the title as a quoted phrase and publishes title_similarity plus an ambiguous flag. journal --issn is the reliable venue lookup: it resolves print and electronic ISSNs in turn and returns the best CiteScore subject percentile with the quartile derived from it (a lookup by title, or an ISSN lookup combined with field=, answers a stub). publications returns one author's document list, most recent first, each carrying its own DOI and an approved_publisher flag against the CLAUDE.md list (flagged, never dropped); deploy/form-service/'s GET /publications wraps it in a disk cache and a rate limiter so the Scopus key, the throttling, and the publisher policy never cross the service boundary to ThesisTracker - .claude/skills/scopus/scripts/doi_publisher.py — DOI prefix → publisher. search, validate and cite all carry doi_prefix and publisher_by_prefix; the prefix is assigned by the registration agency, whereas Scopus prism:publisher was measured naming a learned society for a Springer DOI and an imprint for an Elsevier one - .claude/skills/scopus/scripts/bib_batch.py — candidates → .bib: strict TITLE() title-to-DOI resolution, cite enrichment, venue grading, BibTeX generation - .claude/skills/scopus/scripts/bib_audit.py — .bib → audit, the opposite direction: required fields, duplicates, DOI validation against Scopus, venue metrics by ISSN, publisher approval, then an annotated pass-through copy (<base>_clean.bib) and a measured report (<base>_bib_report.md). Drives the bib-cleaner agent; --no-network replays from its cache - .claude/skills/scopus/scripts/semantic_scholar_api.py — Semantic Scholar fallback - .claude/skills/scopus/scripts/download_pdf.py — any-format full-text retrieval (PDF, else HTML/Markdown via Unpaywall, arXiv, PMC, validated DOI landing), plus an opt-in --browser tier - .claude/skills/scopus/scripts/browser_fetch.py — tier 8: a real Playwright Chromium for challenge-gated publishers (Akamai/Cloudflare), with a per-paper refs/_sources.json override URL (e.g. ResearchGate) for papers with no institutional access; optional (needs playwright + playwright install chromium) - .claude/skills/scopus/scripts/litreview_update.py — incremental-update bookkeeping for /litupdate (baseline fingerprint, delta dedup via bib_batch.title_match, dated output paths, CHANGELOG scaffold) - .claude/skills/scopus/scripts/gemini_reviewer.py · github_reviewer.py · gemini_table.py — cross-review cores

geolocalisation — corpus study-location mapping

Turns a corpus .bib into a spatial map of where each paper's empirical study was conducted. A case-study site is not a bibliographic field (no API returns it — it lives in the text), so the skill is deliberately human-in-the-loop: it emits a draft with a confidence column and a per-paper provenance note, a human reviews it, and a manual override CSV always wins before rendering. Requires SCOPUS_API_KEY (campus network or VPN), or run --no-scopus for a manual-entry template.

Stage	Command	Output
1 — extract	extract_locations.py --bib <corpus.bib> --out <dir> [--full-text] [--override <curated.csv>]	study_locations.csv (with confidence, evidence_field, evidence, provenance) + provenance/<citekey>.md audit notes
2 — review	(human)	curate / confirm; correct every low/none row via the override CSV
3 — render	generate_geomap.py --csv <dir>/study_locations.csv --out <dir> [--formats …] [--min-confidence …]	CSV, KML (Google My Maps), GeoJSON (QGIS/Leaflet), world-map PNG, interactive HTML, country_counts.csv

• Extraction: per-DOI Scopus abstract + title + keywords; place names matched against an offline Natural Earth gazetteer, with capitalization + a population floor + a common-word stoplist for precision (separates the city Mobile from the word mobile).
• --full-text: for none/low results, downloads the PDF via the scopus skill's download_pdf.py, reads it with PyMuPDF, and scans only study-cue sentences with affiliation lines rejected — an unfiltered full-text scan maps author affiliations, not study sites. Adopted only when it beats the abstract; PDFs cached in refs/.
• Auditability: every mapped point carries evidence_field + the verbatim evidence sentence in the CSV, a provenance/<citekey>.md note, and the same surfaced in the HTML popup, GeoJSON properties, and KML description.
• No geopandas/GDAL — the basemap is drawn from raw GeoJSON. Deps: matplotlib (PNG), optional folium (HTML) and PyMuPDF (--full-text).

Files: - .claude/skills/geolocalisation/SKILL.md - .claude/skills/geolocalisation/scripts/extract_locations.py — bib + Scopus/full-text → draft CSV + provenance notes - .claude/skills/geolocalisation/scripts/generate_geomap.py — reviewed CSV → CSV/KML/GeoJSON/PNG/HTML + per-country table (no geopandas) - .claude/skills/geolocalisation/references/geocoding-protocol.md — extraction method, confidence rubric, override format, full-text pipeline - .claude/skills/geolocalisation/scripts/Test/test_extract_locations.py — offline unit tests (bib parse, matcher, evidence, full-text)

recommendation-letter — support / recommendation / acceptance / dispense letters

Generates supervisor letters in LaTeX → PDF from a candidate's own files, signed by the active profile's author (profiles/<active>.yaml, author.letter; a profile with no such block is a stated refusal, never a letter signed with someone else's name). Two tracks: Claude authors the four persuasive types (scholarship, academic_position, industry_position, appreciation; French or English), while a Python script fills the fixed French acceptance and dispense forms. candidate_status (applicant / current_student / graduated) sets how the candidate is named; funding_provider (supervisor / candidate / combination) branches the funding paragraph; the dispense letter carries the < 120-day work-permit exemption paragraph and warns when a stay exceeds it; invitation_pair=both emits the acceptance and dispense letters together. A style-hygiene linter enforces the AI-usage rules. All shipped sample data is synthetic.

Files: - .claude/skills/recommendation-letter/SKILL.md - .claude/skills/recommendation-letter/scripts/generate_letter.py — two-track assembler, validator, pdflatex compile - .claude/skills/recommendation-letter/scripts/letter_templates.py — preamble, letterhead/signature, acceptance/dispense French templates - .claude/skills/recommendation-letter/scripts/Test/test_generate_letter.py — offline unit tests (53 cases) - .claude/skills/recommendation-letter/references/quality-patterns.md — authored-track quality patterns - .claude/skills/recommendation-letter/evals/ — synthetic sample configs

narrative-cv — FRQ / tri-agency narrative CV

FRQ's CV-FRQ and the tri-agency (CIHR/NSERC/SSHRC) "CV commun des trois organismes" are the same format (verified against both official pages 2026-09-25): three sections (career/skills, up to ten contributions and experiences, supervision and mentoring), 6 pages French or 5 English. Built around a durable master inventory rather than a stateless per-run extraction, so a grant CV is a re-selection over a growing corpus instead of a from-scratch rebuild each time.

Stage	Script	Job
1 — inventory	cv_inventory.py	CRUD over the master inventory YAML (init/add/list/stats/mark-used), validated and deduplicated by id/DOI. Never calls Scopus itself.
2 — select	cv_select.py	Deterministic keyword-overlap ranking against a competition's own objectives/evaluation criteria — a signal for the drafting agent's judgment, not a verdict.
3 — build	cv_build.py	Renders ONE cv_model.json to LaTeX and a plain-text companion, builds the mandatory old-portal filename (NOM_XXXXX1234_Titre.pdf, normes_presentation.pdf), and checks the compiled page count against the 6/5-page cap.

The candidate's identity (CV header/footer, filename surname) and the external project folder the inventory and drafts live in (cv.project_dir) both come from the active profile (profiles/<active>.yaml), never from a hardcoded path — a profile carrying neither block is a stop, not a guess. Drives the narrative-cv-writer agent, reached via /cv.

Files: - .claude/skills/narrative-cv/SKILL.md - .claude/skills/narrative-cv/scripts/cv_common.py — shared helpers: contribution-types loader, active-profile identity/project-dir resolution (no fallback), FRQ filename builder - .claude/skills/narrative-cv/scripts/cv_inventory.py — the master inventory CRUD - .claude/skills/narrative-cv/scripts/cv_select.py — keyword-overlap ranking - .claude/skills/narrative-cv/scripts/cv_build.py — LaTeX/text rendering, filename, page-budget check - .claude/skills/narrative-cv/scripts/contribution_types.json — section titles, clientele/category vocabularies, page budget, portal/font variants (data, not code) - .claude/skills/narrative-cv/scripts/Test/test_cv_common.py, test_cv_inventory.py, test_cv_select.py, test_cv_build.py — offline unit tests (61 cases; no network, no LaTeX install, no machine-local profile dependency)

paper2talk — accepted paper to conference talk

Starts where submit-checker and cover-paper stop: the paper is accepted, the talk is the next deliverable. The skill asks six questions before reading the paper — audience, duration and conference, output target, aspect ratio, PDF format, and how the deck ends — because every one of them is an input to a number the build cannot invent, then echoes a build contract (n_content, word budget, font floor, preferred form, deliverables) before a single slide is authored.

Stage	Command	Output
0 — preflight	talk_doctor.py --target pptx	per-dependency state, which targets are buildable here, what degrades without each missing tool
0b — read	paper_extract.py main.tex --out inventory.json	\input-flattened sections, floats with labels/captions/assets, equations, citation keys, and the number inventory the deck is checked against
1 — brand	talk_template.py <gabarit.pptx> --out brand.json --extract-media assets/	canvas, layouts, master background, every object in inches with srcRect as a keep-fraction
2 — figures	fig_export.py <fig.svg> --out <fig.png> --scale 3 [--fix-text map.json]	projector-grade PNG + implied-DPI report (warns below 150)
3 — model	talk_model.py <talk_model.json>	block-vocabulary check, no-text-only-slide rule, exhibit coverage, budget aggregation
4 — render	talk_pptx.py --template <gabarit.pptx> · talk_model.py --render …tex.j2 (Beamer) · …html.j2 (web)	.pptx built on the gabarit's own layouts, .tex, or one self-contained .html, all from the same model
5 — gates	talk_validate.py · talk_notes.py · talk_render.py [--paper a4]	package + legibility checks, spoken budget and cadence, PDF + page images, A4/Letter reflow

• Cadence. One slide per minute counts content slides only: n_content = floor(minutes - 0.5*(title+thanks) - 0.33*dividers). A 13-minute talk is 12 content slides and 15 in all, or 10 and 18 with five dividers.
• Rate. 130 words per minute for a technical talk (not the 150 wpm of conversational speech), aimed at (minutes - 1.5) x 130 so the talk lands under the slot.
• Audience-parameterised typography. 16 pt body floor in the field (14 pt for captions and references), 20 pt for a general-public talk; no bullet cap for a scientific audience, since a slide may legitimately carry seven or eight equations. The cap is replaced by a mechanical legibility gate.
• Content hierarchy. figure > table > equation > prose. Prose, bullets included, is the last resort; a content slide with no exhibit is a defect, and every exhibit must be discussed in its own speaker notes (matched on subject keywords, never filenames).
• Gabarits. ../gabarit_these_maitrise_DSA_UQAC/src/slides/: Gabarit169.pptx (16:9, 18 named layouts), Gabarit43.pptx (4:3, preferred when an A4 handout is the deliverable), main.tex for Beamer. The deck is built on the gabarit with python-pptx rather than imitated, so the lab branding, layouts and placeholders are used as designed.
• Nothing invented. talk_model.py --check-numbers inventory.json compares every number on every slide against the numbers the paper states, on normalised values (a French decimal comma and an English point are one number), so a figure that entered the deck from nowhere is caught before the room catches it.
• Windows render path. PowerPoint COM SaveAs(..., 32) then pdftoppm; the document-skills soffice.py wrapper fails here with AF_UNIX. soffice is used when it is on PATH. A legacy .ppt gabarit is converted with talk_template.py --convert (SaveAs(..., 24)), leaving the original untouched.

Files: - .claude/skills/paper2talk/SKILL.md - .claude/skills/paper2talk/scripts/talk_rules.py — audience profiles, tier costs, cadence, budget, build contract - .claude/skills/paper2talk/scripts/talk_model.py — deck-as-data validation + Jinja render of the Beamer/web targets - .claude/skills/paper2talk/scripts/talk_doctor.py — preflight: buildable targets, per-tool degradation - .claude/skills/paper2talk/scripts/paper_extract.py — paper → inventory (\input flattening, floats, equations, numbers) - .claude/skills/paper2talk/scripts/talk_pptx.py — PowerPoint renderer: opens the lab gabarit, drops its sample slides, builds on its own layouts and placeholders - .claude/skills/paper2talk/scripts/talk_template.py · fig_export.py · talk_render.py · talk_notes.py · to_a4.py · talk_validate.py - .claude/skills/paper2talk/assets/deck_skeleton.js (no-gabarit fallback) · beamer_skeleton.tex.j2 · web_skeleton.html.j2 - .claude/skills/paper2talk/references/renderer-contracts.md · qa-loop.md - .claude/skills/paper2talk/scripts/Test/ — thirteen offline suites, 182 tests (fixtures built in the test; no Office, no network) - Harvest and licence findings: docs/superpowers/notes/2026-08-11-reference-skill-harvest.md

The two memories - the vault and the code graph

Two memories back this toolkit, and they do not overlap. The Obsidian vault holds what was LEARNED, across every project: a failure and its root cause, a decision and why, a tool that misbehaves. The graphify knowledge graph in graphify-out/ holds what this repository's code IS right now: which function calls which, how one module reaches another, where a symbol lives. The vault is permanent and hand-curated; the graph is derived from the files and therefore rebuildable and disposable.

The routing rule follows from that. A question about this code goes to the graph first, and query, path and explain are deterministic traversals that cost no model at all, so one graph query beats grepping file by file. A question about a failure mode, a misbehaving tool or a past decision goes to the vault first. Many tasks want both, in that order.

Both are reached the same way and only that way: dispatch the local-writer agent. It is the single reader and the single writer of both memories. Consulting or refreshing the graph by hand is the same breach as reading the vault by hand, and since 2026-08-30 the vault-access-guard.py hook enforces BOTH at the tool boundary rather than only the vault. For the vault it matches by PATH rather than by command; for the graph it matches the graphify-out/ path, the graphify CLI, and the two graph audit scripts by name, because running a read-only health check to learn the graph's state is a consultation in which the graph's path never appears. The graph is refreshed by writing a file and then pointing graphify update <path> at it, never by editing graph.json.

What the graph does not answer. Measured 2026-08-30 through a local-writer consultation: every node carries _origin: ast, so the graph holds the code and the structure of each .md file - headings, names, where things live - and no layer that read what those files say. Asked "why is the Obsidian CLI write path forbidden", it returned 109 nodes of file names, command names and test-class names, and none of the three measured reasons (the header-size threshold, the CLI exiting 0 on a failed write, create making a numbered duplicate). Those reasons live in .claude/CLAUDE.md and in the vault. So the graph is asked what calls what, and the vault is asked why. Asking the graph for intent is the failure that hurts, because it returns names that read like an answer. Adding the missing layer is possible and needs no API key - the graphify skill's own flow has the host agent read the documents - but it is a deliberate, token-costing run, so check-graph-health.ps1 reports the state as a note and does not fail on it.

The graph's own state is the one thing a session may read directly, because it is metadata rather than content: scripts/audit/check-graph-health.ps1 is read-only and reports what is in the graph, which files it claims to cover and never produced a node for, and whether any covered file is newer than the graph itself. It exits 0 where there is no graphify-out/ at all, so it is harmless in a project that has no graph.

obsidian-cli - Obsidian vault operations

Gives Claude the vault's allowed read/search command surface (read, search, list, property:get/property:set, tasks, links, tags, move, rename) and the single sanctioned write path: a captured learning is drafted, dropped in ~/.claude/obsidian-outbox/ with a create|append path="..." directive on its first line, and the obsidian-outbox-flush.py hook (SessionStart/SessionEnd) writes it into the vault through the filesystem, verifying the effect by file size before and after rather than trusting the CLI's return code.

create, append, and prepend (plus eval, dev:*, plugin:install, theme:install, and every sync* except read-only sync:history) are forbidden commands. The measured reasons: the write fails on the whole JSON header size (content, path, tty/cwd), not the content alone - a 3850-byte header passes, a 4343-byte header does not, and 4096 bytes, a Windows named-pipe buffer, falls in between; the CLI exits 0 even when the write failed, so a script checking the return code archives notes that were never written; and create on an existing file silently writes a numbered duplicate (Decisions 1.md) instead of failing.

Files: - .claude/skills/obsidian-cli/SKILL.md - .claude/skills/obsidian-cli/references/command-reference.md - full command syntax - .claude/skills/obsidian-cli/scripts/vault_consolidate.py - deterministic half of consolidation: measures shared tags/domaine/term overlap and proposes links, decides nothing; --mode links reports dead wiki-links read-only, and --apply <map.json> --yes is the one guarded, map-validated, single-pass rewrite of existing links exempted from the outbox-only write rule - .claude/skills/obsidian-cli/scripts/vault_daemon.py - the unattended path. A raw drop (unrouted text in ~/.claude/obsidian-outbox/raw/, three frontmatter keys, no directive) is classified, drafted, filed, journalled and queued for consolidation without a session: the cloud wrapper pushes, the local model decides. --once handles what is pending, --drain runs the deferred work by hand, --dry-run lists and touches nothing. Anything it is not confident about is parked in needs-review/ with its reason, for local-writer to file with the whole reusable layer in context - .claude/skills/obsidian-cli/scripts/daemon_outbox.py - the outbox layout that IS the queue (raw, working, raw/sent, needs-review, state, queue), plus the write lock, the one-daemon-per-machine singleton lock, the atomic claim by rename, and the sweep that recovers a drop stranded by a crash - .claude/skills/obsidian-cli/scripts/daemon_states.py - the per-state handlers; both model calls are constrained by a JSON schema, and every refusal parks the event - .claude/skills/obsidian-cli/scripts/daemon_drains.py - the deferred half: candidate pairs judged one per call on the strict mechanism test, accepted edges appended reciprocally with their sentence and journalled. Phantom repair stays human-gated - .claude/skills/obsidian-cli/scripts/local_capability_probe.py - the gate that measured, before any of this was written, that the daemon honours a JSON schema and re-uses a prompt prefix - .claude/skills/obsidian-cli/scripts/vault_lock.py, vault_journal.py, outbox_io.py - the shared write path: one lock across sessions and processes, an append-only record of every vault write with an --undo, and the single implementation the flush hook and the daemon both call - .claude/skills/obsidian-cli/daemon-config.json - every timeout, ceiling and threshold, with its provenance; no such value is written in the code that uses it

latex-hygiene - mechanical LaTeX manuscript hygiene

Turns the hygiene checks paper-auditor, submit-checker, and thesis-auditor already describe in prose into one script with subcommands, so the AI-usage score, the word count, and the brace balance are computed the same way every session instead of by hand. One script, tex_check.py; every subcommand takes paths or globs, never a hardcoded manuscript path, and accepts --json.

Subcommand	Input	Output
chars	.tex files/globs	per-file forbidden-character hits (line + name) and a total count
aiscan	.tex files/globs	risk_score, weighted count per signal, lowest-deviation sentence window, a 15-word excerpt per hit
wc	.tex files/globs	prose word count per file (floats and comments excluded), float count, total, page estimate
wc --accepted	.tex files/globs, optional --before <dir>	word count of the accepted text (changes macros resolved); with --before, a before/after/delta/percent table
abstract	main .tex	abstract word count, keyword count
braces	.tex files/globs	final brace depth per file, the line of the first negative dip, and \begin/\end environment balance
par	.tex files/globs	occurrences of \added/\deleted/\replaced whose argument crosses a blank line (the macros are not \long, so this breaks a build)
citecov	--tex <globs> --bib <file>	cited keys absent from the .bib (dangling), and .bib entries never cited
refcov	.tex files/globs	uncited labels, dangling \ref/\eqref/\cref targets, and duplicate labels
patch	--plan <audit_plan.md> --target <file.tex>, optional --author <id>, --dry-run, --init	applies an audit plan by exact-match substitution, one occurrence required per edit; a FAILS: list and non-zero exit on any 0-match or 2+-match edit
scan	.tex files/globs, optional --bib <file>, --fail-on-markers	post-write guard for control characters, damaged control-sequence residue, changes macros crossing a table/float boundary, a % comment that swallowed a row-terminating \\, a stale \cite inside a deleted span, and live \hl{}/\todo{} markers
accept	--target <file.tex>, optional --out <path>, --resolve	the accepted source, [final]{changes}/[disable]{todonotes}, generated from the tracked source
build	--target <file>, optional --outdir out, --both	pdflatex/bibtex/pdflatex/pdflatex with mandatory BIBINPUTS="..", refusing a .bib inside the output directory
all	files/globs	the aggregate of the read-side subcommands above

aiscan reproduces the High/Medium signal weights and the risk_score formula stated in paper-auditor.md Step 7.5 (a High signal counts 2, a Medium signal counts 1, toward raw_count). The script is pure Python standard library, so it needs no requirements.txt and adds no pip-audit surface.

Files: - .claude/skills/latex-hygiene/SKILL.md - .claude/skills/latex-hygiene/scripts/tex_check.py - thin CLI dispatching to the subcommand modules - .claude/skills/latex-hygiene/scripts/tex_common.py, tex_chars.py, tex_braces.py, tex_par.py, tex_citecov.py, tex_abstract.py, tex_wc.py, tex_aiscan.py, tex_aiscan_text.py - .claude/skills/latex-hygiene/scripts/tex_patch.py, tex_scan.py, tex_build.py - the four write-side subcommands (patch, scan, accept, build) - .claude/skills/latex-hygiene/scripts/Test/test_tex_check.py - offline synthetic-string tests - .claude/skills/latex-hygiene/scripts/Test/test_tex_patch.py, test_tex_build.py - offline tests for the write side (10 + 7 tests); test_tex_build.py patches subprocess and shutil.which, no LaTeX installation needed

opt-local-vram-llm - measured VRAM tuning for the local agents

Replaces six manual steps with one command when a newer model arrives for local-writer or local-coder: read the manifest, write a Modelfile, create the tag, sweep, declare the candidate, qualify. Every number it writes is measured on this card, none copied from a model card or inferred from a parameter count. The objective, in order: admissible (`size_vram / size

= 0.999from/api/ps, 300 MiB free, the rung not clamped by the model's own context maximum), fast enough (decode throughput at or above--throughput-floor, default 0.90, of the best admissible throughput), then largest window wins, ties broken on throughput.num_ctxclimbs the existing ladder;kv_cache_type(f16,q8_0,q4_0) is a daemon-wide variable read only at start, so each value costs a restart throughrestart-ollama.ps1, verified againstserver.logbefore anything is measured.num_gpuis pinned at 99, not swept. The rung measurement itself isoptimize_ollama.evaluate_rung, imported fromloop-engineerrather than duplicated. It stops at declaration: it writeslocal-model-config.json, declares the tag as a role candidate inlocal-models.json, and prints themodel_resolver.py --qualify` command without running it.

Files: - .claude/skills/opt-local-vram-llm/SKILL.md - .claude/skills/opt-local-vram-llm/scripts/vram_probe.py - read-only manifest and daemon facts - .claude/skills/opt-local-vram-llm/scripts/vram_modelfile.py - pure Modelfile render - .claude/skills/opt-local-vram-llm/scripts/vram_daemon.py - KV cache axis: write, restart, verify, restore - .claude/skills/opt-local-vram-llm/scripts/vram_optimizer.py - the driver: search, objective function, report, declaration - .claude/skills/opt-local-vram-llm/scripts/Test/test_vram_probe.py, test_vram_modelfile.py, test_vram_daemon.py, test_vram_optimizer.py - four offline suites (11 + 9 + 5 + 13 tests), no network, no GPU, no Ollama daemon

aider-setup and rt-observe are big enough to get their own chapters rather than a subsection here: 06-aider-pipeline.md and 07-rt-observe-dashboard.md.

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