Raven

Render a bound Taste Engine surface as a self-contained designed HTML portrait. Pass project to render one binding, or omit project to render every binding plus a gallery index.html. Portraits are generated from the local taste store and should be verified with audit_taste against their own surface/project before sharing — pass document_kind:'portrait' on that audit: a portrait is a document ABOUT the surface, so design_notes (three.js scenes, branded loaders…) are not acceptance criteria for it; profile rules still bind in full.

Get brand and visual-design principles — logo usage (clear space, min sizes, variants, placement, restraint), gradient usage (hierarchy, palette, contrast, trend vs signature), imagery (consistency, representation, purpose), visual hierarchy, and brand-as-system thinking. Use when the user asks about branding, logos, gradients, imagery, visual consistency, or how to treat a brand across surfaces.

Read a local Raven creative brand profile by ID.

Get a complete design system for building an app with branding like a specific company. Say 'Make me an app with branding like Spotify' and get the full token set, style guide, and implementation instructions. Matches against 12 known design systems and provides closest match with ready-to-use CSS.

Get current brand and visual-design trends — what's working in 2026 and where each trend fits or fails. Includes bento grids, monospace type, neon-on-dark-glass, generative patterns, brutalism rebound, AI-generated imagery, lowercase/mixed case. Each trend is time-stamped — treat as a calibration signal, not a prescription.

Get business and monetization strategies for digital products. Covers monetization models, retention strategies, onboarding optimization, growth mechanics, and product metrics frameworks.

Get a pre-publish checklist for a specific UI type. Returns actionable yes/no items to verify before shipping.

Get content design patterns — copy recipes for error messages, empty-state copy, notifications, and form validation. Returns do's, don'ts, good/bad examples, evidence, and a checklist.

Get UX-writing principles — clarity over cleverness, active voice, error-message anatomy, inclusive language, voice vs tone, and more. Filter by the writing context (e.g. 'error messages', 'notifications', 'form labels').

Get a brand's content design system — voice attributes, tone shifts by context, vocabulary (use/avoid/never), grammar rules, content patterns for errors/empty-states/buttons/etc., and inclusive language guidance.

Get the Design for Delight (D4D) framework templates. Returns customer problem statement, ideal state, hypothesis, LOFA, and experiment templates for structured product thinking.

Get design tokens for a specific design system. Returns colors, typography, spacing, radii, elevation, and motion tokens in W3C DTCG, CSS custom properties, or flat format.

Read a Raven creative generation job by ID.

Read the latest non-mutating layer-tree snapshot captured by the active local grab session. Any fixed/flexible permissions are cooperative advisory metadata: display labels only, not enforced.

Read or update one durable grab change, list legacy reorder operations, or request the unified style+reorder batch. Applied/rejected/superseded changes leave the pending set.

Read newly sent grab selections without deleting their durable change records, optionally waiting up to timeout_ms. A batchCommit marker is the deterministic signal to implement the unified pending batch.

Get a product-metrics framework — HEART (Google), AARRR/Pirate (Dave McClure), North Star Metric, Conversion Funnel, RICE Scoring, or OKRs. Returns structure, when-to-use, pitfalls, and examples. Use when the user asks 'how should we measure success?' or 'what metrics should we track?'

Read the page-scoped template slots from the active grab session's DESIGN.md and merge the overlay's latest selector validation. fixed/flexible roles and allowedTokens are cooperative advisory metadata: display labels only, not enforced.

Get proven UI/UX patterns for a specific design type. Returns do's, don'ts, evidence, and checklists for signup flows, pricing pages, navigation, forms, landing pages, dashboards, modals, empty states, error states, loading states, CTAs, social proof, and mobile conversion.

Get design principles relevant to a UI context. Returns usability heuristics, laws of UX, Gestalt principles, accessibility requirements, typography rules, and color theory — matched to what you're designing.

Get research method details — qualitative (interviews, contextual inquiry, diary, field, intercept), quantitative (surveys, analytics, A/B tests, benchmarking, clickstream), or usability (moderated, unmoderated, 5-second, card sort, tree test, heuristic eval). Returns specific protocols, do/don't guidance, evidence, and a checklist. Use when the user is designing a study or asking how to measure something.

Get a service design pattern — service blueprinting, human handoff, signup-as-service, omnichannel continuity, or moments of truth / recovery. Returns patterns, do/don't guidance, evidence, and a checklist. Use when the user is designing a service flow, escalation, cross-channel experience, or moment of truth.

Get the GOV.UK Service Standard — 14 points the UK government uses to assess whether a public service is ready to launch. Widely applicable as a rigorous service-quality checklist beyond government. Use when the user asks how to evaluate a whole service.

START HERE on a NEW project: returns a deterministic calibration interview. By default (depth:'first_run') it is COMPRESSED to just 5 core questions — identity, genesis (what already exists for the project — a brand, a design system, brand assets — routing whatever is missing to generation: mood board approval stop, then generate_design_system save:true, then init_design_md), aesthetic, voice, matchers — so a first-time kickoff is fast; every other question (references, the remaining ten design dimensions, any learned dimensions, rule exceptions, and the open-ended special question) is still returned under `more_questions` for the agent to offer as optional deeper calibration. Pass depth:'full' to get everything at once as a single flat `questions` list (the eleven design dimensions — typography, spacing, color, layout, motion, imagery, entrance, loading, navigation, aesthetic, libraries — each grounded in what the profile already enforces and most carrying multiple-choice options; the libraries question names specialty tech in plain outcome language and states the default build target for sites: a Next.js app, unless the user prefers otherwise). The voice question always renders the same message in three registers (formal-technical / warm-conversational / punchy-editorial) so the user picks by ear, not by adjective — asked even when the profile has zero voice rules. Every question carries skippable + priority ('core'|'extended'); only identity is required. A 'references' question (offered in more_questions on first_run) invites example URLs/screenshots/files, each interviewed with follow-ups about what specifically draws the person, folded into the matching design_notes. The full interview closes with an open-ended 'special' question (any texture, signature detail, motif, or easter egg nothing else asked about); once the person has other bound surfaces, it carries `suggestions` — the special touches they chose elsewhere. Ask the user the returned questions convers…

Load a locally stored taste profile by name — returns its full rule catalog, precedent corpus, and per-project surface bindings. NOT a calibration step: bindings are per-surface and do not transfer — for design work on a project without a binding, call get_taste_interview and ask the user its questions before committing any direction.

Store a transcript source and return an extraction prompt for the calling agent's model. Raven makes no model or network call.

Parse model-produced extraction JSON into reviewable Decision Graph candidates linked to their source. Nothing is auto-confirmed.

Initialize a DESIGN.md file from a stored Raven token system, a getdesign.md starter slug, or a blank template.

Read component declarations and tokens from a local DESIGN.md file.

Append a labeled precedent to a taste profile's corpus — the growth loop. Use when a human accepts/revises/rejects an audit_taste finding or labels a new wrong→right example. Append-only: existing records are never rewritten. accept-verdict precedents suppress matching findings in future audit_taste runs.

List local Raven creative brand profiles.

Browse available content design systems — brand voice and tone guides (Conversational Product Voice, GOV.UK, Shopify Polaris, Atlassian). Filter by category or search by name.

Browse Raven's provider-agnostic creative model catalog. These are capability slots for image, video, 3D, audio, character consistency, and creative analysis. Each entry lists typical inputs and best-for guidance.

Browse Raven creative presets for product photoshoots, marketplace cards, UGC ads, TV spots, cinematic reveals, social launch packs, storyboards, and infographics.

List the components and provenance in the Raven canonical baseline.

Browse available design systems for tokens. Filter by category (component-library, consumer, developer, fintech, framework, platform, productivity) or search by name.

List local Raven creative generation jobs.

List the taste/direction/design decisions recorded for a profile (see record_taste_decision), optionally filtered by project or dimension — the ledger that evolves the kickoff interview.

List locally stored taste profiles with rule/corpus counts and last-updated timestamps.

List templates and their registered page pathnames from the active grab session. Template permissions and allowedTokens are cooperative advisory metadata: display labels only, not enforced.

Translate a captured pattern's raw literals (font-size: 64px, color: rgb(247,248,248)) onto the project's own design tokens, so generated code uses the user's type ramp and palette instead of another site's values. Pure and deterministic — no model, no network. Colours compare by RGBA distance (exact at 0, near under 12; alpha counts), lengths normalize to px at root 16 (near under 2px), unitless numbers near within 1%, font families compare on the first family. A property with a known token family (font-size, line-height, gap, radius, …) only ever binds inside that family: if the closest token by value belongs to another ramp, the result is a gap naming it, not a binding. Winners are ordered by distance, then family fit, then shortest and lexicographic token path. Percent and viewport units, unparseable colour syntaxes, shadows, gradients, and other unmatchable classes become gaps with a stated reason — never a forced match. Broken token chains are reported in diagnostics whether or not anything else matched. A project with no tokens is not an error: every property returns as a gap at coverage 0. It does not invent tokens and does not write code.

Queue a same-page layer reorder or reparent intent (previewed when measuredRects are supplied, otherwise proposed) without mutating the live page. Reparent moves a node to a different parent (toParentSelector); reorder keeps a single parentSelector. Permissions and fixed/flexible roles are cooperative advisory metadata: display labels only, not enforced; caller-supplied roles are rejected. Shadow-root and iframe boundaries are out of scope.

Plan a multi-asset creative campaign and optionally create draft generation jobs. Covers Higgsfield-like workflows: product photos, UGC/video ads, marketplace cards, launch/social packs, storyboards, and channel cutdowns.

Review added UI-code lines and propose deterministic DESIGN.md token substitutions without writing files. The returned unified patch applies on top of the reviewed diff's post-image; applying it is an explicit, separate step by the caller. Re-verifies the hypothetical polished lines and leaves judgment-heavy findings in manual.

Summarize how Raven has been used on this machine over the last N days. Reports which tools are called most, which audit warnings fire repeatedly (→ likely gaps in Raven's knowledge), which patterns and design systems you look up, and which companies you ask for brand styles. Call this when the user asks 'what have I been building with Raven' or 'what's Raven missing'. All data is read from a local log ($RAVEN_USAGE_LOG or ~/.raven/usage.jsonl) — nothing is fetched over the network.

Register your email to receive design updates and provide feedback to the Raven creator. Call this when a user wants to register, give feedback, or connect with the Raven team.

Parse a DESIGN.md file and return its frontmatter, Markdown body, and flattened token index.

The Taste Engine's learning loop — record a taste, direction, or design decision the MOMENT it is made during real work (an accent chosen, a nav pattern rejected, a name direction picked, a type pairing approved), not just at interview time. Each record carries the project, a short dimension name (a standard one like color/navigation or a new category like iconography/sound), what was chosen in the user's words, the alternatives rejected, why, and a source: 'user-directed' (the user asked for it), 'user-approved' (the user accepted a proposal), or 'user-corrected' (the user overrode a generated choice — the highest-signal record). Recorded decisions evolve every future get_taste_interview kickoff: recurring choices return as suggested defaults on their dimension's question, and decision categories no standard question covers become NEW interview questions. Record liberally — every committed decision is calibration data.

Register a local or remote creative asset for Raven jobs. This is the local-first analog of upload: Raven stores metadata and a URI/path, not the file bytes.

Review added UI-code lines in a unified diff against the project's own DESIGN.md tokens and active recorded design decisions. Returns a structured CI verdict with file/line findings and nearest-token suggestions. Agents should call this on every PR or diff that touches UI code before merge.