adding-a-command
Creates a new CLI command following the Commander.js pattern in src/commands/. Handles…
Adds a new LLM provider implementing LLMProvider interface with call() and stream() methods. Integrates with provider factory in src/llm/index.ts, config detection in src/llm/config.ts, and error handling via tracking and recovery. Use when adding a new model backend,
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Adds a new LLM provider implementing LLMProvider interface with call() and stream() methods. Integrates with provider factory in src/llm/index.ts, config detection in src/llm/config.ts, and error handling via tracking and recovery. Use when adding a new model backend,
name: llm-provider description: Adds a new LLM provider implementing LLMProvider interface with call() and stream() methods. Integrates with provider factory in src/llm/index.ts, config detection in src/llm/config.ts, and error handling via tracking and recovery. Use when adding a new model backend, integrating a third-party LLM API, or extending LLM platform support. Do NOT use for fixing bugs in existing providers, modifying existing provider behavior, or changing the LLMProvider interface. paths: - src/llm/**/*.ts - src/llm/__tests__/**/*.ts
1. **All providers MUST implement the LLMProvider interface** from src/llm/types.ts with three methods:
2. **Initialize client in constructor and store defaultModel from config**. Example: `this.client = new YourSDK({ apiKey: config.apiKey })`. Never lazy-initialize on first call — providers are instantiated once and cached in src/llm/index.ts.
3. **For EVERY response in call() and stream(), invoke trackUsage(model, usage)** from src/llm/usage.js before returning/ending. This is mandatory — it captures token metrics for CLI telemetry and cost analysis. If the API doesn't return usage data, estimate via estimateTokens(text), which assumes ~4 chars per token.
4. **Both call() and stream() must respect the model parameter** using pattern: `options.model || this.defaultModel`. Never hardcode model names. Callers supply model overrides via LLMCallOptions.model.
5. **Error handling: catch all errors, preserve error messages unchanged**. The retry logic in src/llm/index.ts handles transient errors (ECONNRESET, socket hang up, 529 overload). For seat-based providers (Cursor, Claude CLI), wrap stderr via parseSeatBasedError() for user-friendly messages.
6. **Always update ProviderType union** (Step 2), DEFAULT_MODELS (Step 4), and createProvider() switch case (Step 5) in lock-step. Missing any one breaks the build or causes runtime Unknown provider error.
Verify directory exists: `ls -la src/llm/`. Create `src/llm/your-provider.ts`. Match existing provider patterns (src/llm/anthropic.ts, src/llm/openai-compat.ts).
Minimal structure:
import type { LLMProvider, LLMCallOptions, LLMStreamOptions, LLMStreamCallbacks, LLMConfig, TokenUsage } from './types.js';
import { trackUsage } from './usage.js';
import { estimateTokens } from './utils.js';
export class YourProviderProvider implements LLMProvider {
private client: YourSDKType;
private defaultModel: string;
constructor(config: LLMConfig) {
if (!config.apiKey) throw new Error('API key required');
this.client = new YourSDK({ apiKey: config.apiKey, ...(config.baseUrl && { baseURL: config.baseUrl }) });
this.defaultModel = config.model;
}
async call(options: LLMCallOptions): Promise<string> {
const model = options.model || this.defaultModel;
const response = await this.client.messages.create({ model, max_tokens: options.maxTokens || 4096, system: options.system, messages: [{ role: 'user', content: options.prompt }] });
trackUsage(model, { inputTokens: response.usage?.input_tokens || 0, outputTokens: response.usage?.output_tokens || 0 });
return response.content?.[0]?.text || '';
}
async stream(options: LLMStreamOptions, callbacks: LLMStreamCallbacks): Promise<void> {
const model = options.model || this.defaultModel;
const messages = [...(options.messages || []), { role: 'user' as const, content: options.prompt }];
try {
const stream = await this.client.stream({ model, max_tokens: options.maxTokens || 10240, system: options.system, messages });
let stopReason: string | undefined, usage: TokenUsage | undefined;
for await (const chunk of stream) {
if (chunk.delta?.text) callbacks.onText(chunk.delta.text);
if (chunk.delta?.stop_reason) stopReason = chunk.delta.stop_reason;
if (chunk.usage) usage = { inputTokens: chunk.usage.input_tokens, outputTokens: chunk.usage.output_tokens };
}
if (usage) trackUsage(model, usage);
callbacks.onEnd({ stopReason, usage });
} catch (error) { callbacks.onError(error instanceof Error ? error : new Error(String(error))); }
}
}Verify: File exports the class; imports match existing providers.
Edit `src/llm/types.ts` line 1. Add your provider in kebab-case:
export type ProviderType = 'anthropic' | 'vertex' | 'openai' | 'cursor' | 'claude-cli' | 'your-provider';
Verify: `npx tsc --noEmit` shows no ProviderType errors.
If your provider needs fields beyond apiKey, model, baseUrl, extend LLMConfig in src/llm/types.ts:
export interface LLMConfig {
provider: ProviderType;
model: string;
fastModel?: string;
apiKey?: string;
baseUrl?: string;
yourProviderSecret?: string;
}Edit `src/llm/config.ts`:
**Line 9:** Add to DEFAULT_MODELS:
export const DEFAULT_MODELS: Record<ProviderType, string> = {
anthropic: 'claude-sonnet-4-6',
vertex: 'claude-sonnet-4-6',
openai: 'gpt-5.4-mini',
cursor: 'sonnet-4.6',
'claude-cli': 'default',
'your-provider': 'your-provider/default-model',
};**Line 17:** Add to MODEL_CONTEXT_WINDOWS if known:
export const MODEL_CONTEXT_WINDOWS: Record<string, number> = {
'your-provider/model-name': 128_000,
};**Line 59:** In resolveFromEnv(), add env detection before final return null:
if (process.env.YOUR_PROVIDER_API_KEY) {
return {
provider: 'your-provider',
apiKey: process.env.YOUR_PROVIDER_API_KEY,
model: process.Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex.
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