Jingzhe Xu
106 lines
4.0 KiB
TypeScript
106 lines
4.0 KiB
TypeScript
import { describe, expect, it } from "vitest";
|
||
import { EXAMPLE_TASKS } from "../src/features/chat/example-tasks";
|
||
import { buildSkillsMessage } from "../src/features/chat/skill-use";
|
||
import { en } from "../src/lib/strings-en";
|
||
import { zh } from "../src/lib/strings";
|
||
|
||
describe("draft example tasks", () => {
|
||
it.each(["agentBenchmarkBuild", "agentOptimization"] as const)(
|
||
"submits the %s prompt without an implicit Skill block",
|
||
(id) => {
|
||
const task = EXAMPLE_TASKS.find((candidate) => candidate.id === id);
|
||
expect(task).toBeDefined();
|
||
expect(task?.skills).toEqual([]);
|
||
expect(buildSkillsMessage([...(task?.skills ?? [])], zh.chat.exampleTasks[id].prompt)).toBe(
|
||
zh.chat.exampleTasks[id].prompt,
|
||
);
|
||
},
|
||
);
|
||
|
||
it.each([
|
||
{
|
||
locale: "zh",
|
||
buildPrompt: zh.chat.exampleTasks.agentBenchmarkBuild.prompt,
|
||
optimizationPrompt: zh.chat.exampleTasks.agentOptimization.prompt,
|
||
buildMarkers: [
|
||
"依次使用 `agent-creation` 和 `benchmark-design`",
|
||
"id：`finite_choice_agent`",
|
||
"installed_skills：`[]`",
|
||
"id：`contextual-choice-adaptation`",
|
||
"desired_baseline_score：`<75`",
|
||
"pilot_iteration_limit：`5`",
|
||
"足球投注决策",
|
||
"售后政策与工单事实",
|
||
"投资策略、历史市场与当前指标",
|
||
],
|
||
buildForbiddenMarkers: ["thinking_level", "provider", "model_id"],
|
||
optimizationMarkers: [
|
||
"使用 `agent-optimization`",
|
||
"test_agent_id：`finite_choice_agent`",
|
||
"benchmark_id：`contextual-choice-adaptation`",
|
||
"提高信息不完整、规则冲突和有限选项决策中的稳定性",
|
||
"desired_score：`>=95`",
|
||
"candidate_round_limit：`5`",
|
||
],
|
||
},
|
||
{
|
||
locale: "en",
|
||
buildPrompt: en.chat.exampleTasks.agentBenchmarkBuild.prompt,
|
||
optimizationPrompt: en.chat.exampleTasks.agentOptimization.prompt,
|
||
buildMarkers: [
|
||
"Use `agent-creation` followed by `benchmark-design`",
|
||
"id: `finite_choice_agent`",
|
||
"installed_skills: `[]`",
|
||
"id: `contextual-choice-adaptation`",
|
||
"desired_baseline_score: `<75`",
|
||
"pilot_iteration_limit: `5`",
|
||
"football betting decisions",
|
||
"policy and ticket facts",
|
||
"strategy, historical markets, and current indicators",
|
||
],
|
||
buildForbiddenMarkers: ["thinking_level", "provider", "model_id"],
|
||
optimizationMarkers: [
|
||
"Use `agent-optimization`",
|
||
"test_agent_id: `finite_choice_agent`",
|
||
"benchmark_id: `contextual-choice-adaptation`",
|
||
"improve stability under incomplete information, conflicting rules, and finite choices",
|
||
"desired_score: `>=95`",
|
||
"candidate_round_limit: `5`",
|
||
],
|
||
},
|
||
])(
|
||
"$locale preserves the two-session agent evolution contract",
|
||
({
|
||
buildPrompt,
|
||
optimizationPrompt,
|
||
buildMarkers,
|
||
buildForbiddenMarkers,
|
||
optimizationMarkers,
|
||
}) => {
|
||
const normalizedBuild = buildPrompt.replace(/\s+/g, " ");
|
||
const normalizedOptimization = optimizationPrompt.replace(/\s+/g, " ");
|
||
|
||
for (const marker of buildMarkers) {
|
||
expect(normalizedBuild).toContain(marker);
|
||
}
|
||
for (const marker of buildForbiddenMarkers) {
|
||
expect(normalizedBuild).not.toContain(marker);
|
||
}
|
||
for (const marker of optimizationMarkers) {
|
||
expect(normalizedOptimization).toContain(marker);
|
||
}
|
||
|
||
expect(normalizedBuild).not.toContain("penguin run");
|
||
expect(normalizedOptimization).not.toContain("penguin run");
|
||
expect(normalizedBuild).not.toContain("run_subagent");
|
||
expect(normalizedOptimization).not.toContain("run_subagent");
|
||
expect(normalizedBuild).not.toContain("Scoreboard");
|
||
expect(normalizedOptimization).not.toContain("Scoreboard");
|
||
expect(normalizedBuild.length).toBeLessThan(1600);
|
||
expect(normalizedOptimization.length).toBeLessThan(600);
|
||
expect(normalizedBuild).not.toContain("Phase 1");
|
||
expect(normalizedOptimization).not.toContain("Phase 3");
|
||
},
|
||
);
|
||
});
|