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Found 9,304 Skills
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.**
When the user wants to build or improve a sales bot's ability to create handoff summaries and conversation notes. Also use when the user mentions "conversation summary," "handoff notes," "call notes," "CRM updates," or "conversation documentation."
When the user wants to build or improve a sales bot's ability to pull in firmographic or contact data mid-conversation. Also use when the user mentions "data enrichment," "lead enrichment," "pulling company data," "contact data lookup," or "real-time data."
Usar al pedir implementar, desarrollar o ejecutar trabajo referenciado por una historia de usuario o tarea. Solo debe usarse si la historia o tarea se encuentra en estado `Ready`. Activar tambien cuando el usuario mencione "ejecutar tareas", "codificar", "desarrollar la US", "trabajar en el TK" o cualquier variante que implique escribir codigo para una historia o tarea ya especificada.
Fetch official brand/product/tool logos (Stripe, GitHub, Notion, AWS, Figma, etc.) as clean SVGs from SVGL (svgl.app) — as saved .svg files, inline markup, or installed React components. Check this whenever logos or SVGs come up, e.g. adding brand marks to integration/partner rows, footers, pricing tables, or slides; replacing a blurry logo with a vector; getting light/dark variants; or finding an official logo. Prefer it over hand-writing SVG markup or grabbing random files. Skip for generic UI icons, illustrations, charts, favicons from an existing logo, or designing a brand-new custom logo.
Resolve `/flag` style requests into the right LaunchDarkly flag lookup flow. Use when the user types `/flag`, asks to quickly find a flag by name/key, wants a direct flag detail summary, or needs fast disambiguation between similar flags.
Review generated or changed WordPress code — plugins, themes, and blocks — before it ships. Best used reactively after an agent writes, edits, or reviews code touching WordPress APIs: add_action/add_filter, shortcodes, meta boxes, AJAX handlers, REST routes, WP_Query or $wpdb, widgets, or WP-CLI commands. Use on 'review this plugin', 'is this safe to ship', 'make this translatable', 'speed up this query', or after tasks like 'write a plugin' or 'add an endpoint/shortcode/meta box'. Enforces escaping and sanitization, nonces plus capability checks, prepared database queries, core-API-first development, translation-ready strings, and query/caching discipline. DO NOT USE for WooCommerce-specific order, product, or checkout logic (use woo-guard), non-WordPress PHP, generic code quality review (use clean-code-guard), test code review (use test-guard), server or hosting configuration, or conceptual WordPress questions.
Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.
Create a self-contained HTML file for whatever the user is describing, in the effective HTML style. Use when the user wants an HTML artifact that isn't specifically a diagram or a plan — a report, explainer, comparison, deck, prototype, or anything else best delivered as one HTML file.
Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/AlloyDB Omni/MySQL/Oracle/MSSQL/CockroachDB/Yugabyte/MongoDB/GizmoSQL/Redis/Spanner/BigQuery/Azurite/MinIO tests. Not for mocked DBs.
Provides technical specifications and implementation details for uploading audience members to Google products using the Data Manager API /v1/audienceMembers/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload audience members for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).
Generates correct, deployable Salesforce permission set metadata (PermissionSet XML) with object, field, user, and app permissions. Use this skill when creating or editing permission set metadata, object permissions, field-level security (FLS), tab visibility, or deploying permission sets.