archetypeai

@archetypeai source repo

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Atai Design System · archetypeai
Build the front-end for a Newton demo with the Archetype AI Design System — the published, versioned packages (`@archetypeai/ds-lib-tokens`, `@archetypeai/ds-ui-svelte-console`, `@archetypeai/ds-ui-svelte-labs`) and the `ds` scaffolding CLI (`@archetypeai/ds-cli`) — instead of hand-rolling tokens, components, fonts, or brand styling. Use this skill when the user wants to scaffold a new dashboard/UI, add the design system to an existing SvelteKit app, or pull in branded primitives (menubar, logo, sensor/scatter charts, video player, card, badge, table, dialog…). The CLI installs the design system's OWN agent config — `CLAUDE.md` (or `AGENTS.md` for Cursor) plus `ds-manifest.json` at the project root — which is the source of truth for component usage, fonts, fallback behavior, and styling once scaffolded; this skill's only job is to get you there. Components currently ship for Svelte 5; a React port is in progress. Do NOT use for Newton API / backend work (see the `atai-newton-*` skills).

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Atai Newton Omega Model · archetypeai bundle
Get time-series embeddings from Archetype AI's Omega encoder (`OmegaEncoder::omega_embeddings_1_4`) over the prod `/query` endpoint — send a window of sensor readings, get back a fixed-size vector per channel, no batch job and no session. Use this skill when the user wants to embed multivariate sensor windows (vibration, flow, pressure, network, etc.) for lightweight downstream ML — KNN classification, anomaly scoring, similarity search, or 2D projection — done client-side over the embeddings. Covers the request shape (`data.numeric_array`, channel-first window), the per-channel 768-d output, the supported 16–1024 window-length range, `normalize_input`, and the "joint multi-channel state" + KNN pattern. For cleaning / splitting / windowing raw sensor CSVs first, see `atai-newton-omega-model-data-prep`. Do NOT use for text / image / video reasoning (that's the Newton fusion model on `/query`).

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Atai Newton Fusion Model · archetypeai bundle
Call Newton's C 2.6 fusion model (`Newton::c2_6_8b_fp8_260424d7a55d5e`) on the prod `/query` endpoint with text, image, and video inputs in a single stateless request — no session, no batch job, no SSE plumbing. Use this skill when the user wants per-call multimodal reasoning (describe an image, summarize a clip, classify a state from sensor + visual context) without managing session lifecycle, or when they need the Newton C checkpoint that reasons over video frames via `/query`. Covers the request shape per modality, the two image-attachment paths (file_ids vs base64), multi-image mode, both video paths (`.mp4` + `max_frames`, and client-sampled frames + `query_metadata`), generation parameters, JSON-output prompting, latency budgets, and the C 2.6 identifier gotcha. Do NOT use for streaming / session-based activity monitoring, large multi-file batch jobs, or time-series embedding (KNN / anomaly) classification.

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Atai Anomaly Discovery Agent · archetypeai bundle
Run Archetype AI's managed Anomaly Discovery (AD) agent over the Agents API — upload a prepared sensor CSV, resolve the pre-packaged "AD Quick Start" bundle by name (a fitted LOF detector and its threshold are already pinned), run it, poll, download a per-window anomaly score. Use when the user wants to flag "this no longer looks like normal operation" from **normal-only reference data** — no fault library, no labelled examples of the thing being detected, because none exist. Covers the resolve → run → poll → results lifecycle, the output schema, the per-asset framing, the validation settings that silently invalidate every window above 1 kHz, the 50 MiB checkpoint limit, and scoring by lead time and false-alarm rate. Do NOT use for labelled operating regimes (`atai-operational-state-monitoring-agent`), a *named* recurring fault (`atai-rare-event-detection-agent`), client-side embeddings over `/query` (`atai-newton-omega-model`), or raw-CSV prep (`atai-newton-omega-model-data-prep`).

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Atai Manual Generation Agent · archetypeai bundle
Run Archetype AI's managed Manual Generation (MGA) agent over the Agents API — upload a procedure video, create a bundle from the canonical `mga` blueprint, run it, poll, download an ordered, timestamped manual. Use when the user has a recording of a procedure (a repair, an assembly, a workflow) and wants the platform to turn it into step-by-step instructions traceable back to the video. Covers video suitability (the ~5-minute ceiling, the audio-track requirement), the bundle request shape (`max_frames`, `max_new_tokens`, and a `prompt` that must say what to cover and never how to format), the run/poll/results lifecycle, the output JSON schema (`step, instruction, frame_start/end, timestamp_start/end`), and scoring against reference annotations. Do NOT use for verifying a task was performed correctly (the `tva` blueprint), for one-shot multimodal questions over a clip (`atai-newton-fusion-model`), or for time-series state classification (`atai-operational-state-monitoring-agent`).

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Atai Task Verification Agent · archetypeai bundle
Run Archetype AI's managed Task Verification (TVA) agent over the Agents API — upload a recording AND a reference procedure (an SOP), create a bundle from the `tva` blueprint, run it, poll, download a per-step PASSED / FAILED / MISSING verdict per step. Use when the user has a recording of work that should have followed a known procedure and wants to know whether each step was performed — assembly QA, maintenance sign-off, training assessment, SOP compliance. Covers the runtime SOP input (one bundle serves every SOP), SOP authoring as the only tuning lever, the output schema, the measured reliability limit — a skipped step whose tool or part is visible comes back PASSED with an invented reason — and the empty-`results` failure that still reports success. Do NOT use for generating a procedure where none exists (`atai-manual-generation-agent`), for one-shot questions over a clip (`atai-newton-fusion-model`), or for time-series state classification (`atai-operational-state-monitoring-agent`).

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Atai Rare Event Detection Agent · archetypeai bundle
Run Archetype AI's managed Rare Event Detection (RED) agent over the Agents API — upload a sensor CSV, resolve the pre-packaged "RED Quick Start" bundle by name (its nearest-prototype classifier and windowing are already pinned), run it, poll status + audit events, download per-window event predictions. Use when the user wants fully-managed, server-side detection of a *named* rare fault recurring — equipment breakdowns, process excursions — from a handful of labelled examples. Covers name-based bundle resolution, the run/poll/results lifecycle, the output schema, the Embeddings variant, bring-your-own-classifier via the `red` blueprint, and incident-level vs window-level scoring. Do NOT use for classifying every operating regime (`atai-operational-state-monitoring-agent`), for unnamed anomalies with no labelled examples, for client-side embedding + KNN over `/query` (`atai-newton-omega-model`), or for cleaning and windowing raw CSVs (`atai-newton-omega-model-data-prep`).

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Atai Newton Omega Model Data Prep · archetypeai bundle
Clean, split, and featurize multivariate time-series data before embedding it with the Omega model. Bundles three reusable building blocks: `DataPreprocessor` (diagnose timestamp regularity, gaps, and nulls; build continuous gap-aware temporal blocks with imputation), `DataSplitter` (out-of-time or random train/test split that respects temporal order), and `FeaturePreparer` (pivot per-sensor embeddings into a "joint state" (X, y, metadata) matrix with optional L2 / standardization / PCA). Use this skill when the user is preparing raw sensor CSVs for the Omega model (`atai-newton-omega-model`), when training data has gaps and they're unsure whether to drop / impute / split, or when an n-shot CSV looks noisy and they want a principled cleanup pipeline. Do NOT use this skill to run inference or produce embeddings (use `atai-newton-omega-model`). Do NOT use for video / image / text data (use `atai-newton-fusion-model`) — time-series only.

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Atai Operational State Monitoring Agent · archetypeai bundle
Run Archetype AI's managed Operational State Monitoring (OSM) agent over the Agents API — upload a sensor CSV, resolve a maintained pre-packaged "OSM Quick Start" bundle (classifier + windowing already pinned), run it, poll status + audit events, download the per-window state predictions. Use when the user wants fully-managed, server-side classification of operational states over a CSV of sensor records — drilling states, machine modes, process phases — without fitting or hosting a classifier themselves. Covers resolving the bundle by name (portable across deployments), the run/poll/results lifecycle, the output CSV schema (`finish_timestamp, predicted_state, invalid, p_<state>…`), and scoring against a ground-truth sidecar. Do NOT use for client-side embedding + KNN over `/query` (that's `atai-newton-omega-model`), for cleaning / windowing raw CSVs (`atai-newton-omega-model-data-prep`), or for fitting a classifier artifact yourself (contact support@archetypeai.dev).

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9 published skills