Create dark-themed, animated technical diagrams as self-contained HTML+SVG files — flowcharts whose connectors visibly flow, and architecture diagrams where requests travel as light dots through the system (Diagrid/Temporal landing-page style). Use this skill whenever the user asks for a flowchart, workflow, pipeline, process diagram, state machine, system architecture, infrastructure, cloud, microservices, or network topology diagram — and especially when they mention "animated", "flowing", "dynamic", "alive", "GIF-like", or want a diagram for a landing page, README, docs, or product demo. Also use it to convert Mermaid source (a mermaid code block or .mmd file) into an animated diagram — "animate this mermaid", "make this flowchart move". Prefer this over static diagram output whenever the diagram represents anything that moves: requests, events, data, jobs, messages, or control flow.
npx skills add https://github.com/csthink/dashmotion --skill dashmotion
Create professional animated technical diagrams as single self-contained HTML files. The name is the implementation: stroke-dashoffset animation + animateMotion — that's all there is. Output is vector, loops forever, weighs a few KB, and opens in any browser.
| User wants | Mode | Read |
|---|---|---|
| Steps, sequence, branching, parallel execution, state transitions ("what happens, in what order") | Flow | references/flow-mode.md + resources/template-flow.html |
| Components, services, infrastructure, containment, topology ("what the system is made of") | Architecture | references/architecture-mode.md + resources/template-architecture.html |
Mixed request ("show our microservices AND how an order flows through them") → Architecture mode; the animated request path *is* the flow. Only produce two separate files if the process has branching logic that the topology can't express.
Mermaid input — if the request contains Mermaid source (a mermaid block, a .mmd file, or pasted code), ALSO read references/mermaid-input.md before anything else. Supported: flowchart/graph and stateDiagram-v2`; other diagram types are unsupported — say so and offer alternatives. The mode routing above still applies (mermaid is syntax, not semantics), and layout is always recomputed top-down regardless of the source's declared direction.
Read the mode reference file before you start. Its layout arithmetic is what scripts/layout.py implements (Step 5) — read it to author a clean semantic graph and to apply the color/shape/animation style layer to the script's geometry (and to hand-compute the fallback). It encodes what prevents the common failures: overlaps, arrows through boxes, broken loops.
stroke-dashoffset.flow { stroke-dasharray: 5 5; animation: dashmove 0.75s linear infinite; }
@keyframes dashmove { to { stroke-dashoffset: -10; } }
stroke-dasharray period (here 5+5=10), or the loop visibly jumps.d from source to target.<animateMotion><circle r="3.5" class="dot" fill="#34d399">
<animateMotion dur="2s" repeatCount="indefinite"
path="M400 178 L400 204 L170 204 L170 222"/>
</circle>
path reuses the connector's d verbatim; the dot rides exactly on the line.cx/cy — animateMotion positions it.begin="0.7s" etc. 3–6 dots total per diagram; put them where direction is informative (fan-outs, merges, the main request path), never on every edge.#020617, 40px grid pattern (#0f1b33, 0.5px lines), JetBrains Mono when locally installed, else a system monospace stack (ui-monospace, 'SF Mono', 'Cascadia Code', Menlo, Consolas, monospace) — no web-font fetch, the file is fully self-contained.#e2e8f0 13px/500, sublabels #64748b 10px, legend 11px.rx="8"; START/END pills rx = height/2.context-stroke (inherits each line's color):<marker id="arrow" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="6" markerHeight="6" orient="auto-start-reverse">
<path d="M2 1L8 5L2 9" fill="none" stroke="context-stroke" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
</marker>
<path> MUST have fill="none" (or sit in a <g fill="none">) — SVG defaults to black fill and an L-shaped path renders as a giant black polygon without it.0 0 W H where H = lowest element bottom + 50. Never negative coordinates.@media (prefers-reduced-motion: no-preference)..dot elements under reduced motion and wires the visible ⏯ pause toggle (animation-play-state: paused + svg.pauseAnimations()).role="img" + <title> + <desc>.dashmotion ships a deterministic layout engine, scripts/layout.py (pure stdlib). It does the coordinate arithmetic the mode references describe — row packing, branch gaps, boundary padding, orthogonal rail/lane routing — and renders the finished HTML: geometry + the mode style layer + your copy. So you do not hand-compute coordinates *or* hand-transcribe 35 rects and 38 path ds into a template (both are slow). You decide the *semantics and the copy*; the script writes the *file*. Full contract in references/layout-script.md.
Script path — use it whenever python3 is available:
references/mermaid-input.md — into the semantic graph JSON of references/layout-script.md. This is your judgement layer, and it carries everything the diagram needs:type + tier for architecture — omit tier for ungrouped/single-group arch (engine auto-layers), write it for multi-group, see layout-script.md; per-node group for boundary membership; flow shape written only for pills & decisions — never "shape": "step", steps omit it), edges (kind), groups, journeys, any legendExtra, classDef retention;title, subtitle, and (architecture) a summary of exactly three cards (accent cyan/violet/rose, title, items[]) — the human-facing wording is yours to write, here, in the JSON."$TMPDIR/dashmotion-graph.json" (or any mktemp path) — then run python3 <this-skill-directory>/scripts/layout.py "$TMPDIR/dashmotion-graph.json" --render <topic>-dashmotion.html. The semantic JSON is a throwaway build intermediate; the delivered HTML does not depend on it, so never write it beside the .html — the user's folder should contain only the finished diagram. The script computes the geometry, applies the style layer (node fills/strokes by type, the opaque-base + styled-rect masking pair, flow/flow-async/flow-auth connector classes by edge kind, per-journey dot colors with staggered, chained begin), drops in your copy, and writes the complete, self-contained, ready-to-ship file. Edges flagged "loop": true are rendered as the ↻ label annotation, not a path.Do not author the JSON, then *also* hand-write the HTML — that re-incurs the exact transcription cost this path removes. Render, check, deliver.
Hand-computed fallback — only when python3 is unavailable: do the layout arithmetic from the mode reference explicitly before writing coordinates, copy the template, replace SVG content / title / header / legend / summary cards (keep CSS + pause toggle + reduced-motion script), pick 3–6 dot paths copying connector d values and staggering begin. This is the pre-2.2 path — slow, but it needs no Python.
Tell the user the file opens directly in any browser.
Never render frames by hand. Screen-record the open file (macOS ⌘⇧5), or headless:
npx timecut <file.html> --viewport=1200,900 --duration=3 --fps=30 --output=flow.mp4 then ffmpeg -i flow.mp4 flow.gif.
A 3s capture loops seamlessly when all durations divide 3s — prefer 0.75s / 1.5s / 3s when GIF export is the goal.
The file is not done when it's written — it's done when it passes this check. The --render output is structurally sound by construction, but the check is still mandatory (it's also your guard for the hand-computed fallback, whose coordinates fail in predictable ways — the connector layer far more often than the text layer). Verify; don't assume.
Mechanized path (use it whenever python3 is available): run the bundled checker against the file you just wrote —
python3 <this-skill-directory>/scripts/check_diagram.py <your-file>.html
It deterministically detects the failure classes below (overlaps, connectors through boxes, dash-loop seams, out-of-bounds, dots off their line, black-fill, endpoint pierce, dangling begin refs, malformed XML). Fix every reported violation and re-run until it prints 0 violations. Do NOT hand-walk the arithmetic when the script is available, do NOT write your own ad-hoc verification script, and never verify by opening a browser or taking screenshots — the script is the authority; items it can't see (label collisions, exact boundary padding, legend placement) you still check by reading the numbers.
If the input was Mermaid, also mechanize the fidelity recount (checklist item 6): save the source to a temp .mmd and run —
python3 <this-skill-directory>/scripts/check_fidelity.py <source>.mmd <your-file>.html
Fix until it prints PASS. It verifies every source node/edge/group label appears verbatim and the connector count matches the source's edge count. So keep labels and legend entries exactly as the source wrote them — do not reword, merge two source strings into one, or add parentheses (a legend entry v2 点线橙框 must stay v2 点线橙框, never v2 治理骨架(点线橙框)). This is the same low-recall trap as the structural check: prose "I kept it verbatim" misses real drift; the script doesn't.
Prose fallback (only if python3 is unavailable): verify each item below with arithmetic on the actual numbers (write the comparisons out), not by eyeballing the code. Fix every violation and re-check until the list is clean.
left.x + left.width + gap ≤ right.x (gap ≥ 20 flow / 40 architecture). For every stacked pair: top.y + top.height + gap ≤ bottom.y. A boundary must fully contain its children with ≥ 20px padding on all four sides; partial overlap between any two boxes is always a bug.y against the rects it passes (rect.y ≤ y ≤ rect.y + height means a collision); same for vertical drops' x. Fix by re-routing with the rail pattern, not by nudging boxes until something else breaks.|stroke-dashoffset delta| must be an exact multiple of the stroke-dasharray period sum (e.g. 5 5 → 10), including connectors that override the dasharray inline (an async 2 4 edge animated by a -10 keyframe seams every cycle — give it its own keyframes). For each animateMotion, name the single connector whose d it traces — a dot path that spans two connectors sails straight through the component between them; split it into chained per-hop dots instead. Every begin="X.end+…" must reference an id that exists.x+width/y+height and every path coordinate stays inside 0 0 W H; H ≥ lowest element bottom + 20; the legend sits below the lowest boundary (architecture).<path> resolves to fill="none"; endpoints stop ~4px short of the target border and never reach inside a box; no -- inside SVG comments (<!-- A -- B --> closes the comment early and leaks stray text into the document).check_fidelity.py above; run it and fix to PASS. It recounts against the source: node rects/pills == source node IDs (START/END pills added only for [*]); connector paths + ↻-rendered loops == source edges after expanding chains and &; every node, edge, group, and legend label appears verbatim (legend entries merged from a 图例 subgraph included — keep their exact text). Without python3, recount by hand. Details in references/mermaid-input.md.Deliver the file only after a pass where nothing needed fixing.
One self-contained .html: embedded CSS, inline SVG, no external assets, no JS dependencies — only the ~15-line inline pause/reduced-motion script. Renders correctly opened from the filesystem.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take csthink/dashmotion from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npx.
Without those the skill loads but fails at the first command.