Expert knowledge of FoBiS.py (Fortran Building System for poor men) — an automatic Fortran build tool that resolves module dependency hierarchies without manual makefiles. Use this skill whenever the user asks about: writing or editing a fobos file; running any fobis subcommand (build, clean, fetch, install, rule, doctests, scaffold, check, test, coverage, tree, introspect, run, cache, commit); Fortran project build configuration; diagnosing FoBiS build errors; adding GitHub dependencies to a Fortran project; the --json output flag; multi-mode builds; templates; variables and varsets; library builds (static/shared); MPI/OpenMP/coarray/OpenMP-offload builds; feature flags and conditional compilation; build profiles; the build cache; external-library auto-detection and pkg-config (.pc) generation; multi-target builds; convention-based source auto-discovery; the lock file and semver dependency constraints; the first-class test runner and coverage reports; LLM-assisted commit messages; the cflags-heritage feature
Consult Stefano's compiled OKF knowledge bundles at ~/llm-wiki (the "second brain"). TRIGGER whenever a question falls in an existing bundle's domain — currently div-free-amr; numerical divergence constraints (∇·B=0, incompressibility), constrained transport and flux-CT, projection/divergence-cleaning, eight-wave, staggered vs collocated grids, discrete divergence/curl operators, SBP, mimetic/compatible/structure-preserving discretizations, DEC, block-structured AMR refinement interfaces, prolongation/restriction commutation. Also trigger on explicit mentions: "llm-wiki", "second brain", "check the wiki", "what does the wiki say". Use for CONSULTATION from any directory; ingestion/compilation work happens in ~/llm-wiki under its own CLAUDE.md.
Write and review technical developer blog posts as version-controlled markdown. Use this skill when the user wants to draft, write, or review a blog post / dev write-up / release note / tutorial about their code — typically HPC, Fortran, Python, or scientific-computing work. Orchestrates a draft pass and two verification agents: blog-code-checker (verifies every code block, command, and API claim against the real repos under ~/fortran and ~/python) and blog-prose-reviewer (strips AI tells and enforces technical voice). Output is a single markdown file under ./posts/ with minimal YAML frontmatter. Trigger on "/blog", "write a blog post", "draft a post about <repo/feature>", or "review this post". Do NOT trigger for academic manuscripts — use scientific-writing for those.
Operator workflow for post-processing a RAW shoot in darktable with Claude as the operator. Use when the user wants to develop/process/batch their RAW photos (NEF/CR2/ARW), cull a shoot, apply a consistent look across a series, control edit values (exposure etc.) from the prompt, or run darktable-cli batches. Drives the four-stage pipeline (ingest/diagnose -> cull -> look -> export) and the edit-and-look QA loop. References the darktable-raw skill for module specifics. Architecture: the user builds a base LOOK once in the GUI; Claude captures it as a reference XMP and then controls precise per-module values from the prompt by patching the XMP (dt-xmp-patch.py), applying it headlessly via darktable-cli's positional-xmp arg.
Knowledge base from the darktable 4.6 user manual. Use when developing RAW photos (NEF/CR2/etc.) in darktable, driving darktable-cli for headless/batch export, reasoning about the scene-referred pixelpipe, modules (exposure, filmic rgb, color calibration, highlight reconstruction, tone equalizer), masking/blending, styles & XMP sidecars, or color management. Version-matched to darktable 4.6.
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
Generate or edit images using AI models (FLUX, Nano Banana 2). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.
Router and disambiguation map for the HPC skill fleet — decides WHICH high-performance-computing skill to consult when a query plausibly matches several. Use ONLY for cross-cutting routing/navigation: when the user asks which HPC skill covers a topic, says 'what HPC skills do I have', wants the map/relationships between them, or poses a parallel/numerical/HPC question that genuinely spans the reference-vs-applied-vs-theory boundary and the right skill is ambiguous (e.g. 'CG solver not converging' = theory vs PETSc API; 'optimize my CUDA kernel' = design playbook vs CUDA reference; an MPI question that could be the standard vs the C++/Python practical layer). SKIP this skill when the query already names one technology unambiguously — a pure MPI-semantics question goes straight to mpi-5.0, a C++23-standard question to iso-cpp-2023, a Numba-CUDA-Python question to python-hpc — those route directly without this router. This is a thin navigation layer over twelve HPC skills, not a knowledge base itself.