Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/s1366560/agi-demos --skill capture-api-response-test-fixture명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | capture-api-response-test-fixture |
| description | Capture API response test fixture. |
| metadata | {"internal":true} |
For provider response parsing tests, we aim at storing test fixtures with the true responses from the providers (unless they are too large in which case some cutting that does not change semantics is advised).
The fixtures are stored in a __fixtures__ subfolder, e.g. packages/openai/src/responses/__fixtures__. See the file names in packages/openai/src/responses/__fixtures__ for naming conventions and packages/openai/src/responses/openai-responses-language-model.test.ts for how to set up test helpers.
You can use our examples under /examples/ai-functions to generate test fixtures.
For generateText, log the raw response output to the console and copy it into a new test fixture.
import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';
import { run } from '../lib/run';
run(async () => {
const result = await generateText({
model: openai('gpt-5-nano'),
prompt: 'Invent a new holiday and describe its traditions.',
});
console.log(JSON.stringify(result.response.body, null, 2));
});
For streamText, you need to set includeRawChunks to true and use the special saveRawChunks helper. Run the script from the /example/ai-functions folder via pnpm tsx src/stream-text/script-name.ts. The result is then stored in the /examples/ai-functions/output folder. You can copy it to your fixtures folder and rename it.
import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';
import { run } from '../lib/run';
import { saveRawChunks } from '../lib/save-raw-chunks';
run(async () => {
const result = streamText({
model: openai('gpt-5-nano'),
prompt: 'Invent a new holiday and describe its traditions.',
includeRawChunks: true,
});
await saveRawChunks({ result, filename: 'openai-gpt-5-nano' });
});
Sandbox MCP Server 是一个隔离的代码执行环境,提供完整的文件系统操作、命令执行、 代码分析、测试运行和远程桌面能力。当你需要执行代码、操作文件、运行测试、 分析代码结构、或需要图形界面操作时使用此技能。支持 Python、Node.js、Java 等多语言环境。
Evidence-led triage and scoped recovery for local MemStack development runtime incidents. Use when FastAPI, React/Vite Web, Ray, the agent actor, Docker infrastructure, Redis, Postgres, Neo4j, the sandbox, or the Electron desktop sidecar fails to start, crashes, consumes abnormal resources, or becomes unresponsive.
Deliver and verify MemStack native desktop changes in agi-demos against the design-prototype or Web product surface while preserving the Electron shell, Rust sidecar, IPC, encrypted-vault, signing, and updater boundaries. Use for implementation, parity repair, or QA work scoped to agi-stack/apps/desktop; do not use for generic Web QA, mobile work, or unrelated desktop applications.