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chore: stabilize universal refiner local updates
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‎.gitignore‎

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# Runtime state and local databases
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.refiner/
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**/.refiner/
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.claude/worktrees/
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**/.claude/worktrees/
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.gemini/memory.json
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**/.gemini/memory.json
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**/.gemini/blackboard.json
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.env
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.env.*
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!.env.example
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.gemini-refiner.json
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.universal-refiner.json
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**/.gemini-refiner.json
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**/.universal-refiner.json
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# Editor and OS artifacts
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.vscode/

‎README.md‎

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```mermaid
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flowchart LR
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CLI["AI CLI\n(Claude / Cursor)"] -->|"stdio"| PI["PromptImprover\n(gemini-prompt-refiner)"]
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CLI["AI CLI\n(Claude / Cursor)"] -->|"stdio"| PI["PromptImprover\n(universal-refiner)"]
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subgraph internal["PromptImprover Engine"]
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RAG["RAG Snippets\n(FlexSearch)"]
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Memory["SQLite Memory\n(LocalBrain)"]
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./build_and_install.sh
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```
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Both installers perform a deterministic dependency install, run the full test suite, build the package, install it globally, and verify the `gemini-prompt-refiner` command. Add that command to your MCP client configuration. See the [Setup Guide](https://github.com/Coding-Autopilot-System/Promptimprover/wiki/Setup-Guide) for full configuration instructions.
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Both installers perform a deterministic dependency install, run the full test suite, build the package, install it globally, and verify the `universal-refiner` command. Add that command to your MCP client configuration. See the [Setup Guide](https://github.com/Coding-Autopilot-System/Promptimprover/wiki/Setup-Guide) for full configuration instructions.
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For optional automatic pre-prompt linting and post-execution recording, see the [cross-CLI automation guide](./docs/cross-cli-automation.md). Claude Code and Gemini CLI expose the required lifecycle hooks. Codex currently requires MCP-first instructions or explicit helper invocation because its hook lifecycle does not transparently intercept each prompt.
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## Local Semantic Model
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PromptImprover uses a local OpenAI-compatible endpoint before optional MCP sampling. The safe defaults target `http://localhost:9000/v1`, use `gemma3:12b` first, and fall back to `gemma3:1b`. If neither local model nor MCP sampling is available, rule-based refinement continues without semantic output.
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Override the defaults per repository with `.gemini-refiner.json`:
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Override the defaults per repository with `.universal-refiner.json`:
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```json
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{

‎build_and_install.ps1‎

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npm install --global . --no-fund
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$package = Get-Content .\package.json -Raw | ConvertFrom-Json
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$command = Get-Command gemini-prompt-refiner -ErrorAction Stop
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$command = Get-Command universal-refiner -ErrorAction Stop
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Write-Host "Prompt Refiner v$($package.version) installed: $($command.Source)" -ForegroundColor Green
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}
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finally {
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Pop-Location
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}
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}

‎build_and_install.sh‎

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npm run build
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npm install --global . --no-fund
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command -v gemini-prompt-refiner >/dev/null 2>&1
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command -v universal-refiner >/dev/null 2>&1
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VERSION=$(node -p "require('./package.json').version")
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printf 'Prompt Refiner v%s installed: %s\n' "$VERSION" "$(command -v gemini-prompt-refiner)"
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printf 'Prompt Refiner v%s installed: %s\n' "$VERSION" "$(command -v universal-refiner)"

‎docs/cross-cli-automation.md‎

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- `promptimprover-hook-pre` makes one latency-safe rule-based `lint_prompt` call, creates a trackable prompt ID, and injects advisory context. Interactive MCP linting continues to use semantic providers by default.
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- `promptimprover-hook-post` records privacy-safe completion metadata with `record_agent_output`.
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Both commands read hook JSON from stdin, write JSON only to stdout, report failures to stderr, and always allow the client to continue. They start the same built MCP server used by `gemini-prompt-refiner`. Set `PROMPTIMPROVER_SERVER_PATH` only when testing a nonstandard build.
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Both commands read hook JSON from stdin, write JSON only to stdout, report failures to stderr, and always allow the client to continue. They start the same built MCP server used by `universal-refiner`. Set `PROMPTIMPROVER_SERVER_PATH` only when testing a nonstandard build.
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The helpers store only prompt ID, client name, and creation time in the OS temporary directory. They do not persist prompt or response bodies. Completion records contain output length rather than response text.
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‎docs/operator-testing.md‎

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Expected result:
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```text
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Package runtime smoke passed: installed gemini-prompt-refiner-8.0.0 and served /api/health on <port>.
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Package runtime smoke passed: installed universal-refiner-8.0.0 and served /api/health on <port>.
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```
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This catches missing production dependencies that are hidden by the local workspace.

‎docs/portfolio-proof.md‎

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## Present-State Evidence
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- `universal-refiner/package.json` defines the active package as `gemini-prompt-refiner` and describes it as cross-CLI prompt refinement using an MCP server.
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- `universal-refiner/package.json` defines the active package as `universal-refiner` and describes it as cross-CLI prompt refinement using an MCP server.
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- `universal-refiner/tests/` contains targeted Vitest coverage for detectors, history, lessons, memory, snippets, predictive refinement, timeline behavior, and server behavior.
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- `build_and_install.ps1` installs the `universal-refiner` package globally as `prompt-refiner`, showing the intended operator entry point.
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‎universal-refiner/.gemini/settings.json‎

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{
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"mcpServers": {
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"prompt-refiner": {
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"command": "gemini-prompt-refiner",
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"command": "universal-refiner",
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"args": []
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}
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}

‎universal-refiner/.universal-refiner.example.json‎

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{
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"_comment_usage": "Copy this file to .universal-refiner.json in the universal-refiner directory. ConfigManager reads .universal-refiner.json at server startup; restart the MCP server after changes.",
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"_comment_fields": {
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"semantic.localEnabled": "true = LocalOpenAiProvider is tried first. false = skip local model entirely.",
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"semantic.baseUrl": "OpenAI-compatible /v1 base URL. Ollama default: http://localhost:11434/v1. LM Studio default: http://localhost:1234/v1.",
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"semantic.models": "Model names to try in order. First reachable model wins. Use exact Ollama model tags.",
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"semantic.mcpSamplingEnabled": "true = fall back to MCP sampling if local model fails or is unreachable.",
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"semantic.allowNonLoopback": "Set to true only if baseUrl is a non-loopback host. Leave false for localhost."
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},
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"semantic": {
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"localEnabled": true,
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"baseUrl": "http://localhost:11434/v1",
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"models": ["gemma3:12b", "gemma3"],
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"mcpSamplingEnabled": true,
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"timeoutMs": 120000,
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"temperature": 0.2
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}
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}

‎universal-refiner/README.md‎

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# Universal Refiner
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MCP server for prompt governance. Provides the `refine_prompt` tool that enriches
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prompts with project mandates, agentic context, and semantic refinement via a local
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model or MCP sampling fallback.
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## Quick Start
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```powershell
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# From the universal-refiner directory
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npm run build
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node dist/src/index.js
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```
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The server registers as an MCP stdio transport. Global registration is managed by
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`scripts/operations/register-global.ps1`.
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## Local Model Configuration
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`refine_prompt` routes semantic refinement through `LocalOpenAiProvider` first (tier-0),
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then falls back to `McpSamplingProvider` if the local model is unreachable or returns an error.
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Configuration is read from `.universal-refiner.json` in the working directory at **server startup**.
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After changing this file you must **restart the MCP server process** for the change to take effect.
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### Wiring Ollama / Gemma
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1. Start Ollama with the Gemma model:
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```powershell
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ollama pull gemma3:12b
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ollama serve
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```
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Ollama listens at `http://localhost:11434` by default.
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2. Create `.universal-refiner.json` in `universal-refiner/`:
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```json
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{
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"semantic": {
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"localEnabled": true,
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"baseUrl": "http://localhost:11434/v1",
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"models": ["gemma3:12b", "gemma3"],
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"mcpSamplingEnabled": true
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}
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}
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```
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Copy `.universal-refiner.example.json` as a starting point.
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3. Restart the MCP server.
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### Config Fields
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| Field | Type | Default | Description |
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|-------|------|---------|-------------|
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| `semantic.localEnabled` | boolean | `true` | Enable `LocalOpenAiProvider` as tier-0 |
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| `semantic.baseUrl` | string | `http://localhost:9000/v1` | OpenAI-compatible `/v1` base URL. **Ollama default is port 11434, not 9000.** |
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| `semantic.models` | string[] | `["gemma3:12b", "gemma3:1b"]` | Model names tried in order. First reachable model wins. |
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| `semantic.mcpSamplingEnabled` | boolean | `true` | Fall back to MCP sampling if local model fails |
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| `semantic.timeoutMs` | number | `120000` | Request timeout in milliseconds |
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| `semantic.temperature` | number | `0.2` | Sampling temperature (0–2) |
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| `semantic.allowNonLoopback` | boolean | `false` | Must be `true` for non-loopback base URLs (e.g., remote server). Leave `false` for localhost. |
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> **Important:** The hardcoded default `baseUrl` is port 9000, not 11434. Ollama serves on
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> port 11434. Without a `.universal-refiner.json` overriding `baseUrl`, `LocalOpenAiProvider`
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> will silently fail to connect and fall through to MCP sampling.
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### LM Studio
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LM Studio exposes the same OpenAI-compatible `/v1` API. Use:
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```json
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{
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"semantic": {
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"localEnabled": true,
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"baseUrl": "http://localhost:1234/v1",
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"models": ["gemma-3-12b-it"]
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}
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}
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```
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### Provider Chain
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When a `refine_prompt` call arrives:
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1. `LocalOpenAiProvider` is tried first (if `localEnabled: true`).
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- Iterates `models` in order. On model failure, moves to the next model.
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- Returns `null` if all models fail (triggers fallback).
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2. `McpSamplingProvider` is tried next (if `mcpSamplingEnabled: true`).
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3. If both fail, `refine_prompt` returns the original prompt unchanged.
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## Configuration File Reference
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See `.universal-refiner.example.json` for an annotated template.
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## Release Gate
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```powershell
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npm run release:verify
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```
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Runs build, 100% test coverage, MCP acceptance, semantic fallback, stress/soak, and audit checks.
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## Security
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- Never commit `.universal-refiner.json` if it contains sensitive values.
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Add it to `.gitignore` if you customise it beyond the example defaults.
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- `allowNonLoopback: false` (default) prevents the local provider from contacting
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non-loopback hosts, limiting the blast radius of a misconfigured `baseUrl`.

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