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modelport-skill

@forkadarshp⭐ 4 stars

Use when the user asks to migrate, upgrade, or port prompts, agents, tools, or code to a newer LLM or a different provider, including Claude Opus 5.5 / Sonnet 5.5 / Haiku 5.5 / Fable 5.1, GPT-6, and Gemini 3.8, or replace a deprecated or retired model. Also use when a model swap returns 400s (thinking disabled, budget_tokens, forced tool_choice, temperature/top_p, prefill, computer_20251124), truncates replies, or breaks parsers and tool calls, and when auditing migration work another agent started. Fixes API calls, prompts, tool definitions, tests, and configs, then proves it with tests, a live smoke check, and an optional old-vs-new benchmark.

Choose how to use this skill

You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.

1. Native installer

This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.

Do not guess an installer command or replace an existing version without reviewing the diff.

2. Complete package recommended

Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.

No complete ProSkills package is published for this listing yet.

3. Prompt-only

Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.

Need only the instruction file?

Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.

No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.

—/10

// RATINGS

⭐GitHub Stars
⭐ 4 on GitHubGitHub ↗

New / niche

🟢ProSkills Score
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📍

Not yet listed on ClawHub or SkillsMP

// README

ModelPort

CI License: MIT Agent Skill Models PRs Welcome

ModelPort social preview

Upgrade to Claude Opus 5.5, GPT-6, or Gemini 3.8 without breaking prod.

ModelPort is an agent skill for Claude Code, Codex, and Cursor. It migrates the prompts, tool definitions, API calls, and tests around a model swap, using per-model guides that track each provider's breaking changes. Then it proves the upgrade worked with tests, a live smoke check, and optional old-vs-new evals.

Install: npx skills add forkadarshp/modelport, then tell your agent "migrate this repo to claude-opus-5-5". See Quickstart.

Seen one of these after a model swap?

"thinking.type.disabled" is not supported for this model.
tool_choice: type "tool" and "any" are not supported for this model.
'claude-opus-5-5' does not support tool types: computer_20251124.
temperature / top_p / logprobs rejected after moving to GPT-6
thinking_budget and thinking_level in the same request (Gemini 3.x) → 400
stop_reason: "refusal" and code that reads content[0]

Changing the model ID is one line. The 400s, the truncated replies (thinking now counts toward max_tokens), the quiet UI between tool calls, and the parsers that stop matching are everything else. The lookup for all of them is in references/breaking-changes.md. ModelPort applies the fixes for you, in order, and shows its evidence.

What you get

  • Current-model knowledge. Dedicated guides for Claude Fable 5.1, Opus 5.5, Sonnet 5.5, Haiku 5.5, GPT-6, and Gemini 3.8 Flash. Older guides layer underneath for upgrades that start further back.
  • Guides that stay fresh. Every guide has a Last verified: date and a Source:. A weekly job flags stale guides, and the skill re-checks the provider's docs before trusting an old one.
  • A migration, not a find/replace. Scope lock, caller discovery, file classification, required fixes kept apart from optional tuning, then validation and rollback notes.
  • Proof. Tests, a live smoke call, and an optional three-arm benchmark (old model, naive swap, ModelPort) with a runnable harness.

Supported models

ProviderModels with a dedicated guide
AnthropicClaude Fable 5.1, Opus 5.5, Sonnet 5.5, Haiku 5.5 (legacy: Opus 4.8, Opus 4.6, Sonnet 4.5)
OpenAIGPT-6 Astra, GPT-6.1 Sol, GPT-6 Sol, GPT-6 Luna (legacy: GPT-5.5)
GoogleGemini 3.8 Flash
Anything elseGeneric playbook plus provider checklists, with research on the target's docs

Registry, upgrade paths, and freshness rules: references/models/INDEX.md.

Example

Use $modelport-skill to migrate this codebase to claude-opus-5-5.
Apply required compatibility fixes first, keep tool + output behavior stable,
and return validation evidence plus rollback notes.

You get back a report with: what changed and why, required fixes versus optional tuning, test and smoke results, risks, and how to roll back. A sample is in examples/example-output.md.

How it works

A strict, phase-gated pipeline. The skill will not edit before scope is locked and will not claim "done" without proof.

╭──────────────────────────────────────────────────────────────────────╮
│                                                                      │
│   [ old model ]  ═══>  ( ModelPort )  ═══>  [ new model ]            │
│   what you run        behavior-preserving   what you ship            │
│                                                                      │
│   » phase 0   lock scope, read current state ················· [ OK ]│
│   » phase 1   discover callers, prompts, agents, tools ······· [ OK ]│
│   » phase 2   classify every hit before editing ·············· [ OK ]│
│   » phase 3   design: required fixes vs optional tuning ······ [ OK ]│
│   » phase 4   surgical patches, no blind search/replace ······ [ OK ]│
│   » phase 5   validate: tests + live smoke + contract ········ [ OK ]│
│   » phase 6   report: proof evidence + rollback notes ········ [ OK ]│
│                                                                      │
╰──────────────────────────────────────────────────────────────────────╯

Beyond the model ID

ModelPort also adjusts prompts, tool descriptions, and guardrails to match how the target model behaves, including changes the changelog barely mentions. Two examples from the current generation: Claude Opus 5.5 defaults to medium effort, one level below Opus 5, so an untouched config quietly thinks less. And Claude Sonnet 5.5 can read a user message that arrives right after a tool result as a prompt injection, so mid-task user input must be delivered as a user turn. Each guide lists these as required fixes or optional tuning, never mixed.

Benchmark the upgrade

Opt in and ModelPort ends with measured evidence. It runs the same eval set against three configurations so the raw model delta and the skill's value are attributed separately:

  • Baseline: old model, old prompts (where you started)
  • Naive swap: new model, old prompts (what a find/replace gets you)
  • ModelPort-enhanced: new model, enhanced system (where you land)

Sample leaderboard from the bundled offline simulator. The simulator models only prompt explicitness and a per-model "literalness" knob, so these numbers show the report format, not real model performance:

╭──────────────────────┬────────────┬────────────┬────────────╮
│ metric               │ baseline   │ naive swap │ ModelPort  │
│                      │ (old/old)  │ (new/old)  │ (new/enh.) │
├──────────────────────┼────────────┼────────────┼────────────┤
│  task success        │        64% │        57% │        93% │
│  output contract     │        64% │        57% │        93% │
│  tool-call accuracy  │        64% │        57% │        93% │
│  p95 latency         │       4.1s │       2.5s │       2.5s │
│  cost / req          │    $0.0008 │    $0.0006 │    $0.0009 │
├──────────────────────┼────────────┼────────────┼────────────┤
│  composite (50/30/20)│       0.57 │       0.63 │       0.76 │
╰──────────────────────┴────────────┴────────────┴────────────╯

Get real numbers: python3 harness/run.py --provider anthropic (needs ANTHROPIC_API_KEY). The harness works with current Claude models: it never sends parameters they reject and leaves room for always-on thinking. Details in harness/README.md and references/benchmarking.md.

Quickstart

1. Install. Pick the line for your agent.

# Any agent (Claude Code, Codex, Cursor, and others)
npx skills add forkadarshp/modelport
# Claude Code, as a plugin
/plugin install modelport --marketplace forkadarshp/modelport

2. Ask. In your project, tell the agent where you are going:

Migrate this repo to claude-opus-5-5. Keep tool and output behavior the same.

3. Read the report. You get the changes, the test and smoke results, the risks, and the rollback steps. Nothing is called done without evidence.

Copy-paste prompts

GoalPrompt
Latest ClaudeMigrate this repo to claude-opus-5-5. Apply required fixes first, then list optional tuning separately.
Cheaper and faster Claude`Move the classification and routing calls in src/ to claude-haiku-5-5 and keep the output format ident

// HOW IT'S BUILT

KEY FILES

SKILL.mdREADME.md

// REPO STATS

4 stars