Arcads External API

Arcads external API

Configuration

• Base URL: https://external-api.arcads.ai (or ARCADS_BASE_URL).
• Auth: HTTP Basic — use ARCADS_API_KEY as the username and an empty password unless Arcads documentation for your key specifies otherwise. Example curl: curl -u "$ARCADS_API_KEY:" "$ARCADS_BASE_URL/v1/products".
• Never print API keys, commit .env, or paste keys into MASTER_CONTEXT.md.

If the key is missing or the API returns 401/403

1. Editor-first (default): Ensure .env exists (copy from .env.example in the repo root). Ask the user to paste ARCADS_API_KEY only inside .env and save. Do not ask them to paste the key in chat unless they insist.
2. Chat-assisted: If they paste the key in chat, write .env for them, confirm "saved to .env" without repeating the key, and remind them that chat history may retain secrets—rotate the key in Arcads if the chat could be shared.

Before the first call, confirm .gitignore excludes .env.

Signup link (affiliate)

If the user doesn't have an Arcads account yet — at any point during a session, in any context — link them to https://arcads.ai/?via=claude-code (NOT the bare arcads.ai URL). This is the repo owner's referral / affiliate link. Use this exact URL in any "sign up" / "create an account" prompt.

Read order

1. Repo root MASTER_CONTEXT.md when present (brand voice, decisions, quirks).
2. This skill's reference.md for routes, bodies, polling.
3. prompting/guide.md then the right prompting/prompt-library/ file for the model (see table below).

Decision tree: which flow?

All video models use POST /v2/videos/generate with the appropriate model value (see reference.md for the full CreateVideoDto schema).

Prefer the shortest path: if the user only needs a single model, do not create scripts unless they ask for actors/lip-sync workflows.

Creative layer

• MANDATORY: Before composing any prompt for the API, read the relevant prompting/prompt-library/*.md file for the chosen model/workflow. Do NOT skip this step — every prompt must align with the vendor guide's formula and best practices.
• Build one clear prompt paragraph; avoid keyword soup.
• For Seedance 2.0 / Sora2 / Veo3.1 / Kling / Grok Video / Nano Banana, align with the official vendor guides linked in each prompting/prompt-library/*.md file (do not paste full vendor docs into chat—summarize checks).
• Merge slot values from the user and from MASTER_CONTEXT.md when it conflicts with defaults.

Session setup: auto-create a dated folder

At the start of each session that will generate assets, create a folder and project for the day so everything is organized in the Arcads dashboard:

1. Get today's date as YYYY-MM-DD.
2. GET /v1/products → pick the target product (default to whichever MASTER_CONTEXT.md specifies under "My workspace"). If no default is set: if only one product exists, auto-populate MASTER_CONTEXT.md with its ID and name; if multiple, ask the user to pick and save their choice to MASTER_CONTEXT.md.
3. Check existing folders (GET /v1/products/{productId}/folders) — if "Arcads API - {today}" already exists, reuse it. Otherwise:
   • POST /v1/folders with {"productId": "...", "name": "Arcads API - YYYY-MM-DD"}.
   • POST /v1/projects with {"productId": "...", "folderId": "...", "name": "Arcads API - YYYY-MM-DD"}.
4. Store the projectId for the session and pass it in every generation call (projectId field on Sora2/Veo31/b-roll/scene/image DTOs) and use POST /v1/assets/add-to-project after generation for asset types that do not accept projectId directly.

This ensures every generated asset is findable in the Arcads dashboard under Product → "Arcads API - {date}".

Credit cost estimation (MANDATORY — show before generating)

Before firing any generation calls, calculate and present the total credit cost to the user as an estimate. Do not generate until the user confirms.

ALWAYS label credit totals as estimates and tell the user to confirm the exact cost in the Arcads platform before generating if precision matters. The Arcads API does not expose billing endpoints; pricing varies by duration, resolution, and reference inputs.

Cost data sources (in priority order)

1. logs/arcads-api.jsonl — historical record of actual creditsCharged values for every previous call. Read this first. Grep for entries with the same model and similar config (same duration, resolution, referenceImagesCount, audioEnabled) and use the recorded creditsCharged as the estimate. This is the most accurate source.
2. MASTER_CONTEXT.md → Credit costs — user-provided pricing rules (e.g. "Seedance 2.0 image-to-video ≈ 0.06/sec"). Use when no matching log entry exists.
3. Ask the user — if neither source has data for the config, ask the user and write the answer into MASTER_CONTEXT.md.

Never invent numbers. Always cite the source of the estimate ("based on log entry from YYYY-MM-DD" or "from MASTER_CONTEXT.md rate table").

How to calculate

total_credits ≈ sum(credits_per_model × variations_requested) for each model

Example output to user

Estimated credit cost:
  Seedance 2.0 (15s i2v) × 1 = ~0.9 credits   (from logs/arcads-api.jsonl 2026-04-09)
  Veo 3.1                × 2 = ~8 credits     (from MASTER_CONTEXT.md)
  ─────────────────────────────
  Estimated total: ~8.9 credits

⚠️ Estimate only — confirm exact cost in the Arcads platform before proceeding.
Proceed? (yes/no)

Always wait for confirmation before firing. If the user has a credit balance visible in MASTER_CONTEXT.md, warn them if the total would exceed it. If neither the logs nor MASTER_CONTEXT.md have data for the config, ask the user before the first generation and save the answer.

Exception — QA-fix retries (still images only): After the user has confirmed the initial batch, automatic regeneration to fix visible defects (see Generated image QA below) does not require asking again for credit confirmation. Each retry is still billed — note the extra creditsCharged when summarizing the session.

Generation count: multiple variations per prompt

Before firing any generation call, ask the user how many variations they want for this prompt. Default is 1 if they don't specify.

When the count is greater than 1, send N separate API calls with the identical payload. Do NOT batch them into a single request — the API has no batch parameter. Fire them in parallel where possible, then poll all asset IDs concurrently.

Present results as a numbered list so the user can compare and pick favorites.

Nano Banana image: model choice (nano-banana-2 vs Nano Banana Pro)

For POST /V2/images/generate when using a Nano Banana engine:

• Default: "model": "nano-banana-2" (Nano Banana 2).
• Optional: "model": "nano-banana" when the user asks for Nano Banana Pro (the API has no nano-banana-pro enum — Pro maps to nano-banana; see nano-banana.md).

Before the first Nano Banana image call in a workflow, ask: "Use default Nano Banana 2, or Nano Banana Pro?" If they have no preference, use nano-banana-2. Include the chosen model in the credit estimate (separate rows in MASTER_CONTEXT.md if pricing differs).

Script and dialogue

For any video that features a person speaking, ask the user for the script (the exact words the AI person should say). This is separate from the visual prompt — it's the dialogue.

MANDATORY — dialogue confirmation gate

Before generating any video that contains spoken dialogue, the agent MUST:

1. Extract the dialogue lines from the full prompt and show them to the user in a dedicated block, separate from the visual/cinematography description.
2. Present them as a clean, numbered list with beat labels (hook / show / demo / verdict, or similar) and any silent beats clearly marked as (silent beat — no dialogue).
3. Read the dialogue out loud in your head at a natural pace, time it against the target duration, and flag the total spoken word count plus whether it comfortably fits.
4. Explicitly ask for dialogue approval before moving on — e.g. "Approve this dialogue? (yes / edit / rewrite)". Never assume approval from earlier confirmations (tone, template, credit cost). Dialogue approval is its own gate.
5. Only after the user types yes (or equivalent) may you proceed to the credit cost confirmation and then generation. If the user says "edit" or proposes changes, revise and re-present the numbered dialogue block until they approve.

Presentation format (use this exact structure):

📝 Dialogue script (please confirm before I generate)

  1. [HOOK]   "Bro. BRO. Look what just showed up."
  2. [SHOW]   "The PAID SOCIAL stripe? Insane. Like, who greenlit this?"
  3. [DEMO]   (silent beat — thumb brushing the suede, small nod)
  4. [VERDICT] "I'm literally wearing these to the gym tomorrow. You guys have to see these in person."

Total spoken words: ~28  |  Target duration: 15s  |  Fits at natural pace: ✅

Approve this dialogue? (yes / edit / rewrite)

This gate applies to Seedance 2.0, Veo 3.1, Sora 2, and Scene — any flow where the model speaks. Skip for silent flows (B-roll, pure product-hero, premium-reveal with no voiceover, Nano Banana images).

Model-specific notes

• For Seedance 2.0, Veo 3.1, and Sora 2: embed the dialogue in the prompt field using a Dialogue: "..." or She speaks: "..." pattern (these models generate speech from the text prompt).
• For Seedance 2.0 specifically: before generating, always ask the user whether to enable audio output (audioEnabled: true). Also ask whether they want to supply referenceAudios (e.g. background music or a specific voice clip). Upload audio files via presigned URL if provided.
• For Scene (CreateSceneDto): use the dedicated script field for dialogue and prompt for visuals.
• For B-roll: no speech — b-roll is silent/ambient by nature. If the user wants speech, redirect to Seedance 2.0, Veo 3.1, Sora 2, or Scene.
• For Nano Banana images: no speech — these are still images. Speech is handled in the subsequent video generation step.

Script length → video duration (auto-select)

Use the script's word count to automatically pick the best duration value. Average speaking pace: ~2.5 words per second (~150 WPM). Round up to the next available duration to give breathing room.

Sora 2 — duration enum: [4, 8, 12, 16, 20] seconds

Veo 3.1 — no duration field

Veo 3.1 auto-determines video length (~8s typical). If the script exceeds ~20 words, warn the user that Veo may truncate dialogue and offer to split or switch to Sora 2 which has longer duration options.

Seedance 2.0 — duration: 4–15 seconds (continuous)

Seedance 2.0 supports any integer from 4 to 15. Use ~2.5 words/second, round up to the nearest second.

For no-dialogue styles (product hero, premium reveal), default to 15s.

Resolution: Default to 720p. Only use 480p if the user asks for a faster/cheaper test generation.

Aspect ratio: 9:16 (vertical, default for UGC/social) or 16:9 (landscape). No 1:1 support.

B-roll (Kling 3.0) — duration enum: [5, 10] seconds

B-roll is typically wordless. If the user insists on a timed clip with context:

Scene — no duration field

Scene auto-determines length. Use the script field for dialogue.

Splitting long scripts into multiple videos

If the script exceeds the maximum duration for the chosen model:

1. Tell the user the script is too long for a single video and show the word/duration math.
2. Offer two options:
   • Split into segments — the agent breaks the script at natural sentence boundaries into chunks that each fit within the model's max duration. Each chunk becomes a separate generation call.
   • Switch models — if they're on Kling (10s max), suggest Sora 2 (up to 20s).
3. If the user chooses to split, generate each segment as a separate video (respecting the generation count — if they asked for 3 variations, generate 3 of each segment).
4. Offer to stitch the final segments together using ffmpeg:
   • Download all segment videos locally.
   • Concatenate using ffmpeg -f concat -safe 0 -i list.txt -c copy output.mp4 (re-encode if codecs differ).
   • Present the stitched file alongside the individual segments so the user has both.

Veo 3.1: startFrame vs referenceImages — pick one

Veo 3.1 has two mutually exclusive image input modes. Never use both on the same call.

Default rule: When the user provides a single reference photo of a person, always use startFrame unless they explicitly say they want it as a style reference.

Image handling: auto-upscale small inputs

Before sending any reference image, start frame, or base64 image to the API:

1. Check dimensions. If the image's longest side is below 1024 px, upscale it using Lanczos resampling so the longest side reaches 1080 px (preserve aspect ratio).
2. Convert to RGB JPEG (quality 90–95) to strip alpha channels and keep payload size reasonable.
3. Re-encode as base64 (for refImageAsBase64) or upload the resized file (for startFrame via presigned URL).

Several Arcads endpoints (notably POST /v1/b-roll) reject images below a minimum resolution with 422 — The provided image is too small. Auto-upscaling prevents this silently so the user never hits the error.

Generated image QA (mandatory)

Applies to still images from Arcads, especially POST /V2/images/generate (Nano Banana and other image models). After each image asset reaches status: generated, visually inspect the output (download or open the image URL / use the agent's image-reading capability).

Look for: extra or missing hands or fingers; wrong limb count; distorted, duplicated, or merged facial features; melted or fused objects; impossible anatomy; stray limbs; obvious texture or boundary artifacts; unreadable or garbled text if text was requested.

If something looks wrong: Do not hand off the bad frame as the final deliverable without trying again. Regenerate with a revised prompt that explicitly corrects the issue (e.g. "exactly two hands, five fingers each, anatomically correct arms," "single face, no duplicate features"). Do not resend the identical payload and expect a different outcome.

Retry cap: Up to 2 regeneration attempts per originally requested image (3 attempts total including the first). If defects remain after the cap, stop auto-retries, tell the user what still looks wrong, show the best attempt or URLs for all attempts, and ask how they want to proceed.

Credits: Each attempt is a separate generation and is billed. Summarize total credits used for that image after the QA loop ends. See Exception — QA-fix retries under Credit cost estimation.

Video (optional quick check): Before spending heavily on downstream video, you may spot-check scene/b-roll thumbnails or extracted frames for the same kinds of defects; scope is lighter than for hero stills.

Details and checklist items: prompting/prompt-library/nano-banana.md.

Execution checklist (agent)

1. Session folder: Ensure today's dated folder + project exist (see above).
2. Resolve productId (and projectId from session folder): GET /v1/products or ask the user.
3. Ask for script/dialogue: If the output is a video with a person speaking, ask the user for the exact words. Count words to auto-select duration (see "Script length → video duration" above). If too long, offer to split. (Skip for Nano Banana image-only requests.)
   • MANDATORY dialogue confirmation gate (before credit cost / before generation): Extract the dialogue lines from the drafted prompt and present them to the user as a dedicated, numbered block separate from the visual description. Follow the format in Script and dialogue → MANDATORY dialogue confirmation gate. Wait for explicit yes before moving on. This gate is separate from the credit cost confirmation — both must be satisfied.
4. Nano Banana image model: For POST /V2/images/generate, confirm Nano Banana 2 (default) vs Nano Banana Pro (nano-banana) per the section above. Skip if not an image call.
5. Ask for generation count: Ask how many variations the user wants for this prompt. Default to 1.
6. Show credit cost and get confirmation: Calculate total credits using the cost table above. Present the breakdown to the user. Do NOT proceed until they confirm.
7. Check references/ folder: Before composing the prompt, check the repo-root references/ folder for relevant images: references/influencers/ for person recreation, references/products/ for product showcase, references/aesthetics/ for style/mood. If the user hasn't provided an image but a relevant one exists in references/, offer to use it. Auto-upscale any reference image if needed. For Veo 3.1, determine whether to use startFrame or referenceImages (see section above — default to startFrame for person photos).
8. Compose JSON per OpenAPI / reference.md. Primary video endpoint: POST /v2/videos/generate with the appropriate model value (see CreateVideoDto in reference.md). Include projectId when the DTO supports it. Set duration based on script length for models that require it. For Nano Banana images, use POST /v2/images/generate with model set per the Nano Banana section (nano-banana-2 unless the user chose Pro).
   • Seedance 2.0 extras: Set resolution to 720p (default). Set aspectRatio to 9:16 (UGC/social) or 16:9 (landscape). Include audioEnabled per user confirmation. If the user provided reference images, upload via presigned URL and pass filePath strings in referenceImages (max 3). Same for referenceVideos and referenceAudios if provided. Keep @(img1) tokens in the prompt text alongside the referenceImages array.
   • ⚠️ Seedance 2.0 mutually exclusive input modes (confirmed 2026-04-09): referenceVideos and referenceImages cannot be combined in the same request — the API returns HTTP 500 UNKNOWN_ERROR. Pick one: image-to-video OR video-to-video. referenceAudios may be combined with either. See reference.md for details.
   • Seedance 2.0 v2v + human faces — RESOLVED 2026-04-14: v2v with people/faces in reference videos now works. Previously blocked by content checker (April 9). See reference.md.
   • Seedance 2.0 audio+image 500 regression — RESOLVED 2026-04-14: audioEnabled: true + referenceImages now works. Previously returned HTTP 500 (April 9). Always use freshly obtained presigned URLs. See reference.md.
9. POST the correct endpoint N times (once per requested variation) with the same payload. Fire in parallel where possible. Immediately after the POST succeeds, append a log entry to logs/arcads-api.jsonl with the request config (model, duration, resolution, aspectRatio, audioEnabled, reference counts, promptWordCount, assetId). Do NOT log the full prompt text, API keys, or Authorization headers.
10. Poll: GET /v1/videos/{videoId} for video IDs; GET /v1/assets/{id} for asset IDs (including Nano Banana images) until status is generated or failed (see reference.md). Poll all asset IDs concurrently. When polling completes, update the log entry with response.status, response.creditsCharged, response.generationTimeSec, response.videoUrl, response.thumbnailUrl, and response.error (if failed). See logs/README.md for the schema.
11. Generated image QA: For each still image produced in this turn (e.g. POST /V2/images/generate), follow Generated image QA: inspect the image; if defective, regenerate with a refined prompt until pass or 2 retries are exhausted. Skip this step for video-only outputs with no still to review.
12. Assign ALL assets to session project: After generation (and QA retries), check each asset's projects array. If it does not include the session projectId, call POST /v1/assets/add-to-project. This applies to every generated asset — including failed QA attempts and intermediate assets like Nano Banana stills used as starting frames for subsequent video generations. All assets from the session must end up in the same dated project folder.
13. Present results: Return watch URLs, image URLs, or download URLs for QA-passed stills (or the best attempt after max retries, with a clear note). If multiple variations, present as a numbered list for comparison. Explain failed with moderation/validation hints if 422 occurred. For Nano Banana images used as starting frames, show the image and wait for user approval before proceeding to video generation.

• ALWAYS open the output folder on the user's machine after saving generated files so they can immediately review. macOS: open "<output_directory>". Linux: xdg-open "<output_directory>". Windows: explorer "<output_directory>". (The agent should detect the OS or just try open first and silently fall back.) Save videos to outputs/ with a descriptive subfolder (e.g. outputs/seedance-tests/, outputs/clone-ad-tests/).

14. Stitch if split: If the script was split into segments, offer to stitch the final videos together with ffmpeg and provide both the stitched file and individual segments.

Errors (user-facing)

• 401/403: Fix API key / workspace access (setup flow above).
• 404: Wrong UUID; re-fetch lists.
• 422: Validation or moderation — tighten prompt, remove disallowed content, check required enums (aspect ratio, duration).
• 500: Retry later; if repeated, stop and report.

Supporting files

• reference.md — endpoints, auth detail, polling, model mapping notes, CreateVideoDto schema.
• prompting/guide.md — marketing brief → API.
• Seedance 2.0:
  • prompting/prompt-library/seedance-2.md — main Seedance 2.0 model guide (platform rules, API parameters, style template directory).
  • prompting/prompt-library/seedance-2-ugc.md — 9-layer UGC selfie-style formula for Seedance 2.0.
  • prompting/prompt-library/seedance-2-premium-reveal.md — dark-void premium product reveal (no person).
  • prompting/prompt-library/seedance-2-product-hero.md — elemental product hero with splash/effects (no person).
  • prompting/prompt-library/seedance-2-studio-lookbook.md — studio lookbook with voiceover.
  • prompting/prompt-library/seedance-2-feature-walkthrough.md — fast-paced feature walkthrough demo.
  • prompting/analyze-video/SKILL.md — reverse-engineer a reference video into a reusable Seedance 2.0 prompting template.
  • prompting/clone-ad/SKILL.md — clone a reference video ad for a different product (end-to-end: analyze → adapt → generate).
• Other models:
  • prompting/prompt-library/influencer-recreation.md — analyze a reference photo and recreate the influencer.
  • prompting/prompt-library/ugc-selfie-style.md — cross-model UGC guide (iPhone aesthetic, negative prompts, per-model formulas).
  • prompting/prompt-library/product-showcase.md — product-in-hand video workflow (Nano Banana image → approve → video).
  • prompting/prompt-library/nano-banana.md — Nano Banana image prompting guide.
  • prompting/prompt-library/character-sheet.md — generate a 10-image character sheet for a new AI influencer (Nano Banana, default).
  • prompting/prompt-library/character-sheet-gpt-image-2.md — same workflow on ChatGPT Image 2 (gpt-image-2); 5-ref cap + stronger text anchoring for identity continuity.
  • prompting/prompt-library/ugc-product-selfie.md — UGC selfie-style still image: character + product + style references.
• prompting/brand-voice-starter.md — template to copy into MASTER_CONTEXT.md.