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Found 516 Skills
Resolve implementation ambiguities before planning begins. Two modes: Discussion mode surfaces gray areas with concrete options for greenfield work. Assumptions mode reads the codebase, forms evidence-based opinions, and asks the user to correct only what's wrong (brownfield work). Use for "discuss ambiguities", "resolve gray areas", "clarify before planning", "assumptions mode", "what are the gray areas", "before we plan". Do NOT use for broad design exploration (use feature-design) or for planning itself (use feature-plan).
Facilitate structured idea exploration and product/design specification. Use when a user wants to talk through an idea, refine it via iterative questions, and converge on a clear design/spec (and later an implementation plan), especially after inspecting the current project state.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
PR-backed and current-main optimization manual for `moonshotai/Kimi-K2*` and `moonshotai/Kimi-K2.5*` in SGLang. Use when Codex needs to recover, extend, or audit Kimi optimizations, including K2 router/MoE fast paths, K2 thinking Marlin paths, K2.5 wrapper/multimodal/runtime plumbing, W4AFP8/W4A16 quant tracks, parser contracts, LoRA coverage, and backend-specific validation.
Build high-quality visual Web artifacts using HTML/CSS/JavaScript/React — web pages, landing pages, dashboards, interactive prototypes, HTML slide decks, animated demos, UI mockups, data visualizations, and more. Use this skill whenever the user's request involves a visual, interactive, or front-end deliverable, including: - Creating web pages, landing pages, dashboards, marketing pages - Building interactive prototypes or UI mockups (with device frames) - Building HTML slide decks / presentations - Creating CSS/JS animations or timeline-driven animated demos - Turning design mockups, screenshots, or PRDs into interactive implementations - Data visualization (Chart.js / D3, etc.) - Design system / UI Kit exploration Even if the user doesn't explicitly say "HTML" or "web page," this skill applies whenever the intent is to produce something visual, interactive, or presentational. Not applicable: pure back-end logic, CLI tools, data-processing scripts, non-visual code tasks, command-line debugging.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Use when adding or updating Go CLI E2E coverage for one `tests/cli_e2e/{domain}` domain of the compiled `lark-cli`, especially when the work requires live `--help` or `schema` exploration, scenario-based `clie2e.RunCmd` workflows, and per-domain `coverage.md` maintenance.
Lightweight workflow for straightforward changes — plan → implement → optional PR. Direct-commit by default; synthesize is opt-in via synthesisPolicy or a runtime request_synthesize event. Use for trivial fixes, config tweaks, single-file changes, or exploratory work that doesn't warrant subagent dispatch or two-stage review. Triggers: 'oneshot', 'quick fix', 'small change', or /oneshot.
Render an ad-hoc interactive map inline in the chat from a deck.gl declarative spec via the CARTO MCP server's view_map tool. Use whenever the user asks to map, visualize, or show the geographic distribution of points, polygons, hexagons, quadbins, clusters, density (heatmaps), or raster — and the map is exploratory or throwaway, not meant to be saved as a permanent CARTO Builder map. Triggers on "show me X on a map", "visualize Y", "make a heatmap of Z", "render the points/clusters/raster of W". Distinct from carto-create-builder-maps (CLI authoring of permanent maps), carto-preview-builder-map (loading an existing saved Builder map), and carto-develop-app (writing a from-scratch deck.gl app in TypeScript / JavaScript).
Research prediction-market events, venues, underliers, liquidity, and news context for Itô basket workflows. Use for read-only market intelligence, API-gated Itô exploration, and source-grounded prediction-market briefings without investment advice or live trading.
Guided game concept ideation — from zero idea to a structured game concept document. Uses professional studio ideation techniques, player psychology frameworks, and structured creative exploration.
Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".