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Found 982 Skills
Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
Produce a comprehensive, evidence-grounded prioritized action plan from any PM input (notes, transcripts, drafts, executive asks, Slack threads, or a raw situation). Outputs one saveable document with an executive summary, input mirror, situation classification (Cynefin), the binding constraint (Theory of Constraints), prioritized questions and open decisions, a ranked action plan with the critical effort plus follow-ons, risks and pre-mortem, copy/paste prompts for downstream pm-skills, and an evidence map. Builds a source ledger and cites exact input quotes; refuses High-confidence plans for Complex or Chaotic situations. Use when you want the critical next effort and how to execute it.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Interactive prompt studio for HappyHorse 1.0 video generation. Guides users through scenario discovery with vivid examples, then assembles production-ready prompts in JP/CN/EN. Use when someone wants to create AI video content with HappyHorse but doesn't know where to start, or when they have a specific scenario and need a polished prompt. Covers manga drama, character PV, manga motion, virtual idol MV, and free-form scenarios.
Interview the user through their OWN investment screening checklist - you ASK the questions one at a time and the USER answers and clears each gate; the tool never answers, never invents questions, and never checks a gate off. The questions are James's own and live in his Obsidian vault (the source of truth); ask exactly what is there. A decision-support thinking tool, not financial advice. Complements /munger. Use when the user invokes /investment-checklist, says "run this through my investment checklist", "screen this idea/stock/ticker", or pastes a thesis/ticker to be screened.
PDF Editor edits and organizes PDF pages with merge, insert, reorder, exchange, and crop operations, built on ComPDF page management capabilities for fast PDF cleanup and document restructuring. It is a strong fit for requests such as “edit pdf,” “organize pdf pages,” “merge pdf,” “insert pages,” “reorder pdf pages,” “crop pdf pages,” and “rearrange pdf.” Example queries include “Merge these three PDFs into one file,” “Insert this appendix after page 8,” and “Reorder the pages and crop the white margins.”
Quick reference for all Supabase security audit skills with usage examples and command overview.
Write viral, persuasive, engaging tweets and threads. Uses web research to find viral examples in your niche, then models writing based on proven formulas and X algorithm optimization. Use when creating tweets, threads, or X content strategy.
Talk to Alex Hormozi about their expertise. Alex Hormozi provides authentic advice using their mental models, core beliefs, and real-world examples.
Practical async patterns using TaskEither - clean pipelines instead of try/catch hell, with real API examples
Self-contained design transformer — invoke directly, do not decompose. Transforms a design reference HTML file into a Vibes app. Use when user provides a design.html, mockup, or static prototype to match exactly.