memi

Return one shadcn registry-item.json-compatible item from the workspace. Returns: files, targets, dependencies, cssVars, and Atomic Design metadata. Errors: isError if the item is unknown (discover names via get_shadcn_registry).

Load and return the project's user research V2 store — observations, findings, personas, themes, quantitative metrics, and quality metadata. Prerequisites: None — reads from the local .memoire/research/ directory. Research data is populated by running `memi research from-file`, `memi research from-stickies`, `memi research from-transcript`, `memi research web`, or `memi research synthesize`. Returns an empty V2 store if no research has been imported yet. Returns on success: Research store object with shape { version, sources, observations, findings, themes, personas, quantitativeMetrics, opp…

Get the nodes currently selected in Figma with layout/style details. Prereq: bridge + plugin connected; returns [] if nothing is selected. Returns: [{ id, name, type, width, height, x, y, layoutMode?, padding/sizing/itemSpacing?, fills?, strokes?, effects?, styles?, variantProperties? }]. Use for node IDs (capture_screenshot, analyze_design) or reading layout/variants before writing a spec. Errors: isError if not connected.

Build a shadcn registry.json-compatible index from the workspace (component specs; tokens map to a registry:theme item when present). Returns: { $schema, name, homepage, items[] } with file targets, registryDependencies, cssVars. For a single item use get_registry_item.

Fetch the full body of one spec by name. Returns: full spec JSON — ComponentSpec: atomicLevel, props, variants, composesSpecs, codeConnect, WCAG fields; PageSpec: sections, meta; DataVizSpec: chartType, dataShape. Errors: isError if the name is not found (list names via get_specs).

List all saved specs (cheap summary operation). Returns: [{ name, type: "component"|"page"|"dataviz"|"design"|"ia", purpose? }]; [] when none exist. Use before create_spec (overwrite check) or generate_code; fetch a full body with get_spec.

Get design tokens from the local registry, optionally filtered. Prereq: none — local read; run pull_design_system if empty. Returns: [{ name, type: "color"|"spacing"|"typography"|"radius"|"shadow"|"other", values (keyed by mode), cssVariable? }]; [] when none. format "dtcg" returns the same tokens as a W3C Design Tokens (DTCG) document instead — nested groups, $type/$value, lossless via $extensions. Filter by type/name to keep payloads small on large token sets. For a Tailwind-ready mapping use sync_design_tokens.

Predict text layout — height, line count, overflow, breakpoint behavior — via Node canvas. No browser, Figma, or AI needed. Returns: { height, lineCount, lines[] }; plus { overflow } when containerHeight is given; plus { breakpoints: { mobile, tablet, desktop } } when checkBreakpoints=true. Never throws (0 height / 1 line for unparseable fonts). Use to verify labels or body copy fit fixed containers or maxLines constraints before generating designs or code.

Write Mermaid Jam-ready FigJam source artifacts from research or a simulation run. This is source + open friendly: it writes .mmd/.md files under .memoire/mermaid-jam and returns next steps. It does not attempt clipboard or direct paste automation.

Build a dry-run UI fix plan from diagnosis evidence and app graph data. Returns on success: { patches[], summary, caveats[] } where every patch includes risk, confidence, affected files, operations, and writeSafe. This tool never modifies source files. Use this tool: to decide what a human or coding agent should patch before calling memi fix apply or making manual edits.

Prepare a local Apple design brief without running Xcode or writing files.

Prepare a bounded local design brief without executing commands, integrations, or models.

Inspect existing components, props, tokens and stories before editing frontend code. Optionally resolve supplied Figma/Paper evidence; no network, model, config execution or writes. Unknown behavior remains unassessed.

Pull the full design system from Figma (tokens, components, styles) into the local registry. Prereq: Figma bridge running + plugin connected — verify with check_bridge_health; start via `memi connect`. Returns: { tokens, components, styles, lastSync }. Errors: isError "Figma not connected" if no plugin. Run at session start or after designer changes; inspect results with get_tokens.

Pull the design system from Figma via REST API — no plugin or bridge required. Prereq: FIGMA_TOKEN and FIGMA_FILE_KEY env vars. Returns: { tokens, components, styles, lastSync }. Errors: missing env vars, or Figma API errors (403 = bad token, 404 = bad file key). Use in CI/headless; equivalent to `memi pull --rest`.

Preview a research-backed vibe design package from ResearchStore V2 plus an optional simulation run. Returns on success: { package } with brief, Atomic Design specs, evidence ids, Mermaid Jam-ready source artifacts, and warnings. This tool is non-mutating; call research_generate_specs to write specs or mermaid_jam_export to write FigJam source files.

Write research-backed Atomic Design specs generated from ResearchStore V2. Prerequisites: Call research_design_package first to preview. This tool requires approved=true to make the write explicit. Writes DesignSpec, IASpec, PageSpec, ComponentSpec, and DataVizSpec objects through the Memoire registry.

Run a deterministic design-system audit (WCAG contrast, token completeness, spec accessibility) and return structured findings. Prereq: none — token/spec level, no Figma, no AI. Returns: { success, results: issues[], score, level, summary }. focus="contrast" narrows to token contrast pairs; focus="skill-compliance" checks real source files against ATOMIC_DESIGN.md/MOTION_VIDEO_DESIGN.md's checkable rules. vs analyze_design: run_audit = systematic spec/token compliance; analyze_design = AI vision review of a live Figma frame.

Preview or write a spec-first Atomic Design scaffold for agent-created files. Default is non-mutating: approved=false returns a dry-run JSON plan with the exact spec path, guardrails, and next generation commands. To write the spec, pass approved=true. This writes only the registry spec; generated code still requires generate_code or memi generate, so quality gates stay intact.

Preview or write a deterministic SwiftUI feature scaffold. Default approved=false is non-mutating and returns every planned path and file body: Memi iOS spec, SwiftUI view, optional screen model, preview, and Swift Testing file. Set approved=true only after reviewing the plan. Writes never alter Xcode project files and refuse to overwrite existing files.

Compare completed simulation runs by adoption, confidence, evidence coverage, risk, and cost.

Summarize token and cost usage for a simulation run.

Convert a simulation report into a product-spec impact artifact that agents can paste into specs or handoff docs.

Generate a 20-60 agent model-swarm cohort from Memoire research evidence without starting a run.

Interview a simulated product stakeholder from a completed local or model-swarm run.

List persisted simulation runs with lightweight summaries. Use this to discover runIds for simulation_status, simulation_stream, simulation_transcript, simulation_costs, simulation_report, and simulation_compare.

List Codex-first model profiles available to Memoire model-swarm simulations. Live model execution is opt-in; unavailable providers automatically fall back to deterministic clean-room simulation.

Create a clean-room product simulation scenario from Memoire research evidence. Prereq: research/store.v2.json or a ResearchStore JSON string. Local TypeScript simulation core only; adapter=model-swarm plans Codex-first profiles with deterministic fallback. Returns: { scenario (agents, variables, graph, evidenceFindingIds), warnings }.

Export a simulation report with recommendations, risks, assumptions, events, interviews, and evidenceFindingIds.

Run a prepared local or model-swarm product simulation scenario. Prerequisites: Call simulation_plan first and pass the returned scenario.id. Returns on success: SimulationRun with status, events, eventCount, and persisted run id.

Plan and run multiple model-swarm hypotheses, then compare outcomes for product-spec decision work.

Read a local simulation run status from .memoire/simulations/runs.

Read persisted simulation events in stream order. Paginated — use offset/limit to page through long runs instead of materializing the full event log.

Read model-swarm transcript memory for a run.

Map registry tokens to a Tailwind theme.extend object; optionally import a W3C DTCG token file first. Prereq: tokens in the registry (pull_design_system first) — or pass dtcgFile to import them here. Never throws — returns {} when empty. Returns: partial theme (colors, spacing, fontSize, borderRadius, boxShadow) using var(--token) references; keys from the last token-name segment; "other" tokens skipped. With dtcgFile also { imported, warnings }. vs get_tokens: get_tokens = raw data for inspection (format "dtcg" exports the DTCG document); this = paste-ready Tailwind patch.

Update a design token value in the local registry, optionally pushing to Figma. Prereq: token must exist (names via get_tokens); Figma connection only for pushToFigma. Returns: { updated: true, name, pushedToFigma, reason? } — a requested-but-skipped or failed push is reported in reason, never silently dropped. Errors: isError if the token name is not found. For bulk Tailwind mapping use sync_design_tokens.