@agentled/cli/dist/commands/init.js

CLI for Agentled — manage workflows, apps, and knowledge from the command line. Zero context-window cost for AI agents.

/**
 * `agentled init [slug]` — bootstrap a local workspace folder for building,
 * testing, and iterating on workflows without per-iteration credit cost.
 *
 * Creates agentled_<slug>/ in cwd with:
 *   - .agentled/workspace.json + cache/ (apps + models)
 *   - docs/ (SKILL.md + GOTCHAS.md)
 *   - examples/scaffolds/ (bundled JSON skeletons)
 *   - examples/live/, fixtures/, tests/, drafts/ (empty, agent fills these)
 *
 * See `agentled workspace sync` to refresh cached data.
 * See `agentled workflows pull <id>` to import a live workflow.
 * See `agentled fixture capture` + `agentled test` for the zero-credit
 *     iteration loop.
 */
import { existsSync } from 'node:fs';
import { resolve } from 'node:path';
import { AgentledClient, resolveAuthContext } from '../client.js';
import { printOutput, printError } from '../utils/output.js';
import { createWorkspaceFolder, resolveWorkspaceMeta } from '../utils/workspace-folder.js';
export function registerInitCommand(program) {
    program
        .command('init')
        .description('Scaffold a local agentled_<slug>/ workspace folder with docs, scaffolds, and cache. Foundation for the test/fixture iteration loop.')
        .argument('[slug]', 'Override the workspace slug used for the folder name (defaults to remote workspace slug)')
        .option('--force', 'Overwrite an existing folder', false)
        .option('--format <fmt>', 'Output format', 'json')
        .action(async (slugArg, opts) => {
        try {
            const client = new AgentledClient();
            const [workspace, appsList, modelsList] = await Promise.allSettled([
                client.getWorkspace(),
                client.listApps(),
                client.listModels(),
            ]);
            if (workspace.status !== 'fulfilled') {
                printError(`Failed to load workspace: ${workspace.reason?.message ?? 'unknown error'}`);
                return;
            }
            const authContext = resolveAuthContext();
            const meta = resolveWorkspaceMeta({
                response: workspace.value,
                authWorkspace: authContext.workspace,
                slugOverride: slugArg,
                apiBase: authContext.baseUrl,
            });
            const folderName = `agentled_${meta.slug}`;
            const folderPath = resolve(process.cwd(), folderName);
            if (existsSync(folderPath) && !opts.force) {
                printError(`Folder already exists: ${folderPath}. Use --force to reinitialize, or "agentled workspace sync" to refresh the cache.`);
                return;
            }
            createWorkspaceFolder(folderPath, meta, {
                appsList: appsList.status === 'fulfilled' ? appsList.value : undefined,
                modelsList: modelsList.status === 'fulfilled' ? modelsList.value : undefined,
            });
            printOutput({
                folderPath,
                folderName,
                meta,
                cached: {
                    apps: appsList.status === 'fulfilled',
                    models: modelsList.status === 'fulfilled',
                },
                nextSteps: [
                    `cd ${folderName}`,
                    'agentled workflows pull <workflowId>          # import a workflow',
                    `agentled clients init ${meta.slug} --name "${meta.name}" # start the DFE client dossier`,
                    'agentled workflows lint examples/scaffolds/lead-scoring-kg.json',
                    'cat docs/GOTCHAS.md                          # read before building',
                ],
            }, (opts.format ?? 'json'));
        }
        catch (e) {
            printError(e instanceof Error ? e.message : String(e));
        }
    });
}
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