ideogram-migration-deep-dive
Provides strangler fig migration strategies to move image generation workloads to Ideogram.
Install
mkdir -p .claude/skills/ideogram-migration-deep-dive && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7584" && unzip -o skill.zip -d .claude/skills/ideogram-migration-deep-dive && rm skill.zipInstalls to .claude/skills/ideogram-migration-deep-dive
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Migrate from other image generation APIs to Ideogram, or re-architectKey capabilities
- →Map parameters from DALL-E/Midjourney to Ideogram
- →Implement adapter patterns for multi-provider support
- →Audit existing image generation API usage
- →Validate migration with test prompts
- →Re-architect pipelines for Ideogram features
How it works
It uses the strangler fig pattern to gradually replace legacy image generation providers with an adapter that maps parameters to the Ideogram REST API.
Inputs & outputs
When to use ideogram-migration-deep-dive
- →Migrate from DALL-E to Ideogram
- →Re-architect image generation pipelines
- →Audit current image generation API usage
- →Replace local Stable Diffusion with cloud API
About this skill
Ideogram Production Migration
Overview
Modernize a complete Ideogram image workflow without assuming endpoint names imply equivalent semantics. Inventory legacy and V3 behavior, select V4, P-Image, or outcome-focused tools by requirement, compare safe durable results, and preserve in-flight state and rollback.
Prerequisites
- Source routes, payloads, traffic, consumers, stored assets, async state, quality rubric, and owners.
- Target use cases, model-control needs, latency, safety, rights, cost, and retention constraints.
- Feature flags, shadow destination, reconciliation plan, canary budget, and rollback window.
Current Contract
First-party navigation labels /generate, /edit, /remix, /reframe, and /describe as Legacy Endpoints. Current surfaces include V3, V4 sync/async/transparent, P-Image, edit, remix, structured describe, magic prompt, outcome-focused tools, and custom training. Payload and rendering semantics differ.
Authentication
Preserve the server-side Api-Key boundary and environment isolation throughout migration. Dual-run fixtures and receipts must exclude keys, prompts, uploaded images, generated assets, and temporary URLs.
Instructions
- Inventory each source route, field, enum, output, error, retry, safety, storage, consumer, and in-flight state.
- Classify every use case by required model control, transparency, edit semantics, structured prompting, specialized outcome, latency, and cost.
- Select a documented target route for each use case; record non-equivalence and retire unsupported assumptions.
- Build owned translation at the application boundary instead of leaking mixed legacy and target schemas to consumers.
- Compare sanitized fixtures, then run approved shadow or dual generation into isolated storage without double publication.
- Evaluate transport, safety, useful quality, latency, cost, URL persistence, async reconciliation, and deletion.
- Canary by tenant or use case, reconcile every known generation and asset, then remove legacy traffic only after the rollback window.
Tool Discipline
Use Read, Glob, and Grep for routes, consumers, schemas, state, and evidence. Use Write and Edit for approved migration code, tests, and records. Do not dual-run paid customer traffic or delete legacy assets by invocation alone.
Approval Boundaries
Require owners for target selection, model or quality changes, dual-run spend, customer-derived content, safety differences, cutover, and destructive retirement. A migration with no state and asset reconciliation plan is not ready.
Error Handling
- Do not map legacy enums or rendering speed by string similarity.
- Stop on unexplained safety, output-count, aspect, storage, or quality divergence.
- On rollback, halt target admission first and reconcile accepted target work before restoring source traffic.
Output
Return source inventory, target mapping, semantic gaps, translated contracts, fixture and shadow results, safety, quality, latency, spend, canary state, reconciled identifiers, retirement evidence, and rollback receipt.
Examples
- Move legacy generation to V4 multipart while keeping consumer response compatibility behind an adapter.
- Replace a broad edit flow with a specific background or reframe tool only when direct model control is unnecessary.
Validation
Run source and target contracts against the same approved synthetic cases, review visual quality separately from transport, verify durable storage and deletion, and exercise rollback under in-flight load.
Resources
- Current first-party evidence map — use the dated endpoint, webhook, billing, team, and training links as the contract index for this workflow.
- Recheck the endpoint-specific page and current OpenAPI description before relying on an enum, limit, beta feature, or lifecycle claim.
- Record live observations as environment-specific evidence, not as universal vendor guarantees.
When not to use it
- →Using Authorization: Bearer headers for Ideogram
- →Using pixel dimensions instead of aspect ratio enums
- →Failing to wrap parameters in image_request
Prerequisites
Limitations
- →Ideogram API requires Api-Key header, not Authorization
- →Image URLs expire after ~1 hour
- →Size format requires enums like ASPECT_1_1
How it compares
This migration guide provides a structured parameter mapping and adapter pattern to minimize disruption during provider replacement.
Compared to similar skills
ideogram-migration-deep-dive side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| ideogram-migration-deep-dive (this skill) | 1 | 2mo | Review | Intermediate |
| ideogram-hello-world | 1 | 2mo | Caution | Beginner |
| mcp-builder | 136 | 5mo | Review | Advanced |
| supabase-developer | 95 | 9mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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