jiang
d58bd19039
Generate chat, task and learning models from agent-models.json. Use WaterNetwork fast/expert models with 272k context and validate upstream names before readiness. Add an isolated model check command with signal-safe cleanup and plugin, Skill and MCP management docs. Verified in isolation: type checks, 26 focused tests and both model catalog checks; no model inference requests.
25 lines
1.3 KiB
TypeScript
25 lines
1.3 KiB
TypeScript
import { readFileSync } from "node:fs";
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import { resolve } from "node:path";
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import { normalizeMcpConfig } from "./mcp-config.js";
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import { compileOpencodeModels, loadAgentModelConfig, type AgentModelConfig } from "../chat/modelConfig.js";
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// A conversation Location can be outside the checkout. Load only the trusted
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// service assets explicitly, rather than relying on its current directory.
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export function serviceOpencodeConfig(input: Record<string, unknown>, models: AgentModelConfig = loadAgentModelConfig()) {
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const agents = (input.agents ?? {}) as Record<string, unknown>;
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const instruction = (agents.instruction ?? {}) as Record<string, unknown>;
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const system = readFileSync(resolve(".opencode/agents/instruction.md"), "utf8")
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.replace(/^---\s*\n[\s\S]*?\n---\s*\n/, "");
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return {
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...input,
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...compileOpencodeModels(models),
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snapshots: false,
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mcp: normalizeMcpConfig(input.mcp),
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plugins: ["*", ...((input.plugins ?? []) as unknown[]), resolve(".opencode/tjwater-plugin")],
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skills: [resolve(process.env.OPENCODE_SKILLS_ROOT_DIR ?? ".opencode/skills")],
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agents: { ...agents, instruction: {
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description: "TJWater structured data and UI agent", mode: "primary", ...instruction, model: models.default_model, system,
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} },
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};
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}
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