platform/claude-api/claude-opus-migration/skills/claude-opus-4-5-migration/SKILL.md
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library claude-opus-4-5-migrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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One-shot migration from Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5.
"high" (see references/effort.md)Identify which platform the codebase uses, then replace model strings accordingly.
Remove the context-1m-2025-08-07 beta header if present—it is not yet supported with Opus 4.5. Leave a comment noting this:
# Note: 1M context beta (context-1m-2025-08-07) not yet supported with Opus 4.5
| Platform | Opus 4.5 Model String |
|----------|----------------------|
| Anthropic API (1P) | claude-opus-4-5-20251101 |
| AWS Bedrock | anthropic.claude-opus-4-5-20251101-v1:0 |
| Google Vertex AI | claude-opus-4-5@20251101 |
| Azure AI Foundry | claude-opus-4-5-20251101 |
| Source Model | Anthropic API (1P) | AWS Bedrock | Google Vertex AI |
|--------------|-------------------|-------------|------------------|
| Sonnet 4.0 | claude-sonnet-4-20250514 | anthropic.claude-sonnet-4-20250514-v1:0 | claude-sonnet-4@20250514 |
| Sonnet 4.5 | claude-sonnet-4-5-20250929 | anthropic.claude-sonnet-4-5-20250929-v1:0 | claude-sonnet-4-5@20250929 |
| Opus 4.1 | claude-opus-4-1-20250422 | anthropic.claude-opus-4-1-20250422-v1:0 | claude-opus-4-1@20250422 |
Do NOT migrate: Any Haiku models (e.g., claude-haiku-4-5-20251001).
Opus 4.5 has known behavioral differences from previous models. Only apply these fixes if the user explicitly requests them or reports a specific issue. By default, just update model strings.
Integration guidelines: When adding snippets, don't just append them to prompts. Integrate them thoughtfully:
<code_guidelines>, <tool_usage>) to organize additionsOpus 4.5 is more responsive to system prompts. Aggressive language that prevented undertriggering on previous models may now cause overtriggering.
Apply if: User reports tools being called too frequently or unnecessarily.
Find and soften:
CRITICAL: → remove or softenYou MUST... → You should...ALWAYS do X → Do XNEVER skip... → Don't skip...REQUIRED → remove or softenOnly apply to tool-triggering instructions. Leave other uses of emphasis alone.
Opus 4.5 tends to create extra files, add unnecessary abstractions, or build unrequested flexibility.
Apply if: User reports unwanted files, excessive abstraction, or unrequested features. Add the snippet from references/prompt-snippets.md.
Opus 4.5 can be overly conservative about exploring code, proposing solutions without reading files.
Apply if: User reports the model proposing fixes without inspecting relevant code. Add the snippet from references/prompt-snippets.md.
Apply if: User requests improved frontend design quality or reports generic-looking outputs.
Add the frontend aesthetics snippet from references/prompt-snippets.md.
When extended thinking is not enabled (the default), Opus 4.5 is particularly sensitive to the word "think" and its variants. Extended thinking is enabled only if the API request contains a thinking parameter.
Apply if: User reports issues related to "thinking" while extended thinking is not enabled (no thinking parameter in request).
Replace "think" with alternatives like "consider," "believe," or "evaluate."
See references/prompt-snippets.md for the full text of each snippet to add.
See references/effort.md for configuring the effort parameter (only if user requests it).
testing
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
development
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.