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* feat(ai): scaffold pen-ai-skills package
* feat(ai): add pen-ai-skills core types
* feat(ai): add Vite plugin for skill file compilation
* feat(ai): add budget module with token estimation and category-priority trimming
* feat(ai): add skill loader with phase filtering and test injection
* feat(ai): add skill resolver with phase filter, intent match, and dynamic injection
* feat(ai): migrate all prompt content to skill files
Extract prompt content from 7 TypeScript source files into 27 Markdown
skill files with proper frontmatter. Each file contains phase, trigger,
priority, budget, and category metadata for the skill resolution engine.
Planning: decomposition, design-type
Generation: jsonl-format, jsonl-format-simplified, schema, layout,
text-rules, overflow, style-defaults, variables, design-system,
design-code, design-md
Validation: vision-feedback
Maintenance: local-edit, incremental-add, style-consistency
Domains: landing-page, dashboard, mobile-app, form-ui, cjk-typography
Knowledge: role-definitions, design-principles, icon-catalog,
copywriting, examples
* feat(ai): add document context memory module
* feat(ai): add generation history memory module
* feat(ai): wire memory loading into skill resolver
Add memory field to ResolveOptions and load documentContext/generationHistory
into AgentContext with per-phase limits (planning:5, maintenance:3, others:0).
Export memory utility functions from package index.
* refactor(ai): use resolveSkills('planning') in orchestrator
Replace ORCHESTRATOR_PROMPT import with resolveSkills() from pen-ai-skills.
The planning phase system prompt is now resolved dynamically from skill
files instead of a static constant.
* refactor(ai): use resolveSkills('generation') in sub-agent
Replace SUB_AGENT_PROMPT + designPrinciples concatenation with
resolveSkills() from pen-ai-skills. The generation phase system prompt
is now resolved dynamically with flag-based skill filtering for
variables and design.md context.
* refactor(ai): use resolveSkills('validation') in design validation
Replace the 70-line inline VALIDATION_SYSTEM_PROMPT constant with a
lazy resolver function that loads the validation prompt from pen-ai-skills
skill files at call time.
* refactor(mcp): use skill registry for design prompt sections
* refactor(ai): remove old prompt files replaced by pen-ai-skills
Delete prompt files whose content has been migrated to the
pen-ai-skills package:
- ai-prompt-sections.ts (section registry, triggers, builders)
- orchestrator-prompts.ts (orchestrator + sub-agent prompts)
- design-system-prompts.ts (design token generation prompt)
- design-code-prompts.ts (HTML/CSS code-gen prompt)
- design-principles/ directory (5 principle files + index)
Consolidate role-definitions/ 8 sub-files into index.ts
(registerRole calls are runtime behavior, must be preserved).
Move buildDesignMdStylePolicy into ai-prompts.ts. Strip migrated
constants (PEN_NODE_SCHEMA, DESIGN_EXAMPLES, ADAPTIVE_STYLE_POLICY,
CHAT_SYSTEM_PROMPT, DESIGN_GENERATOR_PROMPT, etc.) from ai-prompts.ts.
Add pen-ai-skills alias to mcp:compile esbuild script.
* docs: add pen-ai-skills to architecture documentation
* fix(mcp): use h3 v2 createEventStream API for SSE endpoint
event.node.res was removed in h3 v2. Migrate to createEventStream
which handles SSE headers, streaming, and cleanup automatically.
* chore: update package versions and add pen-ai-skills to Dockerfile
- Bump version for multiple packages to 0.5.2, including pen-ai-skills, pen-codegen, pen-core, pen-figma, pen-renderer, pen-sdk, and pen-types.
- Add pen-ai-skills package to Dockerfile for inclusion in the build process.
- Update TypeScript configuration to ensure proper file inclusion.
- Modify GitHub Actions workflow to trigger on version tags.
- Enhance AI-related functionality by integrating resolveAgentModel for dynamic model resolution in chat and generation APIs.
* refactor: clean up unused imports and improve dynamic content handling
- Removed unused imports from various files, including SkillMeta and ResolvedSkill from types.test.ts, and estimateTokens from resolve-skills.ts.
- Updated the dynamic content injection function to use a more concise parameter in the regex replacement callback.
* chore: enhance build and CI workflows with skill generation
- Added a new script to generate the skill registry during post-installation in package.json.
- Updated the GitHub Actions workflows to include steps for compiling the CLI and generating the skill registry, ensuring all necessary components are built and ready for deployment.
---------
Co-authored-by: Fini <fini.yang@gmail.com>
280 lines
9.3 KiB
TypeScript
280 lines
9.3 KiB
TypeScript
import { defineEventHandler, readBody, setResponseHeaders } from 'h3'
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import { resolveClaudeCli } from '../../utils/resolve-claude-cli'
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import { runCodexExec } from '../../utils/codex-client'
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import {
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buildClaudeAgentEnv,
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buildSpawnClaudeCodeProcess,
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getClaudeAgentDebugFilePath,
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resolveAgentModel,
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} from '../../utils/resolve-claude-agent-env'
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import { formatOpenCodeError } from './chat'
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interface GenerateBody {
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system: string
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message: string
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model?: string
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provider?: 'anthropic' | 'openai' | 'opencode' | 'gemini'
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thinkingMode?: 'adaptive' | 'disabled' | 'enabled'
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thinkingBudgetTokens?: number
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effort?: 'low' | 'medium' | 'high' | 'max'
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}
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/**
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* Non-streaming AI generation endpoint.
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* Routes to the appropriate provider SDK based on the `provider` field.
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* Requires explicit provider and model; no fallback routing.
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*/
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export default defineEventHandler(async (event) => {
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const body = await readBody<GenerateBody>(event)
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if (!body?.message || body?.system == null) {
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setResponseHeaders(event, { 'Content-Type': 'application/json' })
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return { error: 'Missing required fields: system, message' }
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}
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if (!body.provider) {
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setResponseHeaders(event, { 'Content-Type': 'application/json' })
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return { error: 'Missing provider. Provider fallback is disabled.' }
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}
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if (!body.model?.trim()) {
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setResponseHeaders(event, { 'Content-Type': 'application/json' })
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return { error: 'Missing model. Model fallback is disabled.' }
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}
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if (body.provider === 'anthropic') {
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return generateViaAgentSDK(body, body.model)
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}
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if (body.provider === 'opencode') {
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return generateViaOpenCode(body, body.model)
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}
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if (body.provider === 'openai') {
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return generateViaCodex(body, body.model)
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}
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if (body.provider === 'gemini') {
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return generateViaGemini(body, body.model)
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}
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return { error: 'Missing or unsupported provider. Provider fallback is disabled.' }
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})
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/** Generate via Claude Agent SDK (uses local Claude Code OAuth login, no API key needed) */
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async function generateViaAgentSDK(body: GenerateBody, requestedModel?: string): Promise<{ text?: string; error?: string }> {
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const runQuery = async (): Promise<{ text?: string; error?: string }> => {
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const { query } = await import('@anthropic-ai/claude-agent-sdk')
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// Remove CLAUDECODE env to allow running from within a CC terminal
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const env = buildClaudeAgentEnv()
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const debugFile = getClaudeAgentDebugFilePath()
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const model = resolveAgentModel(requestedModel, env)
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const claudePath = resolveClaudeCli()
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const q = query({
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prompt: body.message,
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options: {
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systemPrompt: body.system,
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...(model ? { model } : {}),
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maxTurns: 1,
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tools: [],
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plugins: [],
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permissionMode: 'plan',
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persistSession: false,
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env,
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...(debugFile ? { debugFile } : {}),
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...(claudePath ? { pathToClaudeCodeExecutable: claudePath } : {}),
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...(buildSpawnClaudeCodeProcess() ? { spawnClaudeCodeProcess: buildSpawnClaudeCodeProcess() } : {}),
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},
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})
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try {
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for await (const message of q) {
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if (message.type === 'result') {
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const isErrorResult = 'is_error' in message && Boolean((message as { is_error?: boolean }).is_error)
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if (message.subtype === 'success' && !isErrorResult) {
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return { text: message.result }
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}
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const errors = 'errors' in message ? (message.errors as string[]) : []
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const resultText = 'result' in message ? String(message.result ?? '') : ''
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return { error: errors.join('; ') || resultText || `Query ended with: ${message.subtype}` }
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}
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}
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} finally {
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q.close()
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}
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return { error: 'No result received from Claude Agent SDK' }
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}
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try {
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return await runQuery()
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} catch (error) {
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const message = error instanceof Error ? error.message : String(error)
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return { error: message }
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}
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}
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async function generateViaCodex(body: GenerateBody, model?: string): Promise<{ text?: string; error?: string }> {
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const result = await runCodexExec(body.message, {
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model,
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systemPrompt: body.system,
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thinkingMode: body.thinkingMode,
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thinkingBudgetTokens: body.thinkingBudgetTokens,
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effort: body.effort,
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})
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return result.error ? { error: result.error } : { text: result.text ?? '' }
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}
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function mapOpenCodeEffort(
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effort?: 'low' | 'medium' | 'high' | 'max',
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): 'low' | 'medium' | 'high' | undefined {
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if (!effort) return undefined
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if (effort === 'max') return 'high'
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return effort
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}
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function buildOpenCodeReasoning(
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body: GenerateBody,
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): Record<string, unknown> | undefined {
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const reasoning: Record<string, unknown> = {}
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const effort = mapOpenCodeEffort(body.effort)
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if (effort) {
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reasoning.effort = effort
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}
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if (body.thinkingMode === 'enabled') {
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reasoning.enabled = true
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} else if (body.thinkingMode === 'disabled') {
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reasoning.enabled = false
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}
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if (typeof body.thinkingBudgetTokens === 'number' && body.thinkingBudgetTokens > 0) {
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reasoning.budgetTokens = body.thinkingBudgetTokens
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}
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return Object.keys(reasoning).length > 0 ? reasoning : undefined
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}
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/** Timeout for OpenCode prompt calls (3 minutes) */
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const OPENCODE_PROMPT_TIMEOUT_MS = 180_000
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async function promptWithTimeout(
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ocClient: any,
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payload: Record<string, unknown>,
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timeoutMs = OPENCODE_PROMPT_TIMEOUT_MS,
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): Promise<{ data: any; error: any }> {
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const result = await Promise.race([
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ocClient.session.prompt(payload),
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new Promise<{ data: null; error: string }>((resolve) =>
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setTimeout(
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() => resolve({ data: null, error: `OpenCode prompt timed out after ${timeoutMs / 1000}s` }),
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timeoutMs,
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),
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),
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])
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return result
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}
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async function promptOpenCodeWithThinking(
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ocClient: any,
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basePayload: Record<string, unknown>,
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body: GenerateBody,
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): Promise<{ data: any; error: any }> {
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const reasoning = buildOpenCodeReasoning(body)
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if (!reasoning) {
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return await promptWithTimeout(ocClient, basePayload)
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}
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const enhanced = { ...basePayload, reasoning }
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const firstTry = await promptWithTimeout(ocClient, enhanced)
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if (!firstTry.error) {
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return firstTry
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}
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console.warn('[AI] OpenCode reasoning options rejected, retrying without reasoning.')
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return await promptWithTimeout(ocClient, basePayload)
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}
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/** Generate via OpenCode SDK (connects to a running OpenCode server) */
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async function generateViaOpenCode(body: GenerateBody, model?: string): Promise<{ text?: string; error?: string }> {
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let ocServer: { close(): void } | undefined
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try {
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const { getOpencodeClient } = await import('../../utils/opencode-client')
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const oc = await getOpencodeClient()
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const ocClient = oc.client
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ocServer = oc.server
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const { data: session, error: sessionError } = await ocClient.session.create({
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title: 'OpenPencil Generate',
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})
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if (sessionError || !session) {
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const detail = formatOpenCodeError(sessionError)
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return { error: `Failed to create OpenCode session: ${detail}` }
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}
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// Inject system prompt as context (no AI reply)
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await ocClient.session.prompt({
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sessionID: session.id,
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noReply: true,
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parts: [{ type: 'text', text: body.system }],
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})
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// Parse model string ("providerID/modelID")
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let modelOption: { providerID: string; modelID: string } | undefined
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if (model && model.includes('/')) {
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const idx = model.indexOf('/')
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modelOption = { providerID: model.slice(0, idx), modelID: model.slice(idx + 1) }
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} else if (model) {
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console.warn(`[AI] OpenCode generate: could not parse model string "${model}", sending without model override`)
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}
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// Send main prompt and await full response
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const promptPayload: Record<string, unknown> = {
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sessionID: session.id,
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...(modelOption ? { model: modelOption } : {}),
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parts: [{ type: 'text', text: body.message }],
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}
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console.log(`[AI] OpenCode generate: model=${model}, parsed=${JSON.stringify(modelOption)}`)
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const { data: result, error: promptError } = await promptOpenCodeWithThinking(
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ocClient,
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promptPayload,
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body,
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)
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if (promptError) {
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const errorDetail = formatOpenCodeError(promptError)
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console.error('[AI] OpenCode generate error:', errorDetail)
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return { error: errorDetail }
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}
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// Extract text from response parts
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const texts: string[] = []
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if (result?.parts) {
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for (const part of result.parts) {
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if (part.type === 'text' && part.text) {
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texts.push(part.text)
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}
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}
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}
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if (texts.length === 0) {
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return { error: 'OpenCode returned an empty response. The model may not have generated any output.' }
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}
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return { text: texts.join('') }
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} catch (error) {
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const message = error instanceof Error ? error.message : 'Unknown error'
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return { error: message }
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} finally {
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const { releaseOpencodeServer } = await import('../../utils/opencode-client')
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releaseOpencodeServer(ocServer)
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}
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}
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/** Generate via Gemini CLI (`gemini -p -o json`) — CLI handles its own auth */
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async function generateViaGemini(body: GenerateBody, model?: string): Promise<{ text?: string; error?: string }> {
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const { runGeminiExec } = await import('../../utils/gemini-client')
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return runGeminiExec(body.message, {
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model,
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systemPrompt: body.system,
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thinkingMode: body.thinkingMode,
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thinkingBudgetTokens: body.thinkingBudgetTokens,
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effort: body.effort,
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})
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}
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