[
 {
  "id": "T01",
  "target": "ai_session_search_archive",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不能直接拿旧的 txt 文件列表跑。\n\n原因：\n1. 旧列表可能包含已删除或已更新的文件，也可能漏掉新导出的文件。\n2. 语义搜索会把文件列表当作「允许返回结果的范围」，如果列表里没有某个文件，即使它的向量在缓存里，也不会被返回。\n\n正确做法：\n1. 重新用 `rg --files` 生成一次最新的文件列表（用同一个 glob `*.md`）。\n2. 把新列表传给 `semantic-search`，再跑查询。\n3. 缓存里的向量不会因为重新生成列表而丢失或失效；只要文件路径还在，缓存就能直接命中。\n\n一句话总结：先刷新文件列表，再跑语义搜索。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1246,
  "completion_tokens": 152,
  "sec": 4.457255840301514,
  "cost": 0.0019375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T02",
  "target": "compressor",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "把 `-format json` 紧跟在 `-monitor` 后面即可：\n\n```bash\n\"/Applications/Compressor.app/Contents/MacOS/Compressor\" \\\n  -monitor -format json \\\n  -batchid \"<batch_id>\" \\\n  -once\n```",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2654,
  "completion_tokens": 52,
  "sec": 2.476998805999756,
  "cost": 0.0034475,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T03",
  "target": "deployment_github_actions_koyeb",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "在 `koyeb/action-git-deploy` 的 `with` 里加上这一行：\n\n```yaml\ngit-builder: \"docker\"\n```\n\n把 `git-builder` 设为 `\"docker\"` 即可强制 Koyeb 使用 Dockerfile 构建，而不是 buildpack。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1551,
  "completion_tokens": 54,
  "sec": 4.462677955627441,
  "cost": 0.00207375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T04",
  "target": "mac_universal_clipboard",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "```bash\ndefaults delete ~/Library/Preferences/com.apple.coreservices.useractivityd.plist ClipboardSharingEnabled 2>/dev/null; \\\ndefaults write ~/Library/Preferences/com.apple.coreservices.useractivityd.plist ClipboardSharingEnabled 1; \\\nkillall useractivityd 2>/dev/null; \\\nkillall sharingd 2>/dev/null; \\\nkillall pboard 2>/dev/null; \\\nsleep 2; \\\npgrep -fl 'useractivityd|sharingd|pboard'\n```",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1144,
  "completion_tokens": 104,
  "sec": 8.01295280456543,
  "cost": 0.00169,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T05",
  "target": "openreview",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "直接删除 `tmp/openreview_token.json` 即可。删除后重新执行查询，CLI 会用 `.env` 里的账号重新登录，绕过 403。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1627,
  "completion_tokens": 35,
  "sec": 4.03817892074585,
  "cost": 0.00212125,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T06",
  "target": "workflow_bilibili_whisper_transcription",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "根据 skill 文档里的“踩坑记录”和“最佳实践”，**下载阶段**明确建议：\n\n- **单线程**  \n- **每次下载之间间隔 2-3 秒**  \n\n这个 2-3 秒的延迟是目前文档里给出的最稳妥做法，能显著降低触发 B 站 352 反爬的概率。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1722,
  "completion_tokens": 75,
  "sec": 3.3505210876464844,
  "cost": 0.00234,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T07",
  "target": "workflow_watchdog",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "默认 30 分钟（1800 秒）后自动醒来，用 `ScheduleWakeup` 动作来设置。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 594,
  "completion_tokens": 26,
  "sec": 3.3692920207977295,
  "cost": 0.0008075,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T08",
  "target": "workflow_parallel_subagents",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不行。`subagent_type` 必须是已在 `opencode.json` 或 `.opencode/agents/*.md` 里注册过的 agent 名，而不是 provider/model 路径。\n\n正确做法是先把模型包装成一个 agent，例如：\n\n```json\n// ~/.config/opencode/opencode.json\n{\n  \"agents\": {\n    \"deepseek_v4_pro\": {\n      \"model\": \"deepseek/deepseek-v4-pro\"\n    }\n  }\n}\n```\n\n然后在 `functions.task` 里把 `subagent_type` 设为 `\"deepseek_v4_pro\"` 即可。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 4714,
  "completion_tokens": 122,
  "sec": 8.012150049209595,
  "cost": 0.0061975,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T09",
  "target": "codex_cli",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "在 Codex 0.156.1 的配置里，彻底禁用 `web_search` 工具的写法是：\n\n```toml\nweb_search = \"disabled\"\n```\n\n- **值**：必须是字符串 `\"disabled\"`（注意引号）。  \n- **布尔值**：`false`、`on`、`off`、`enabled` 都会被拒绝，提示 `expected string only in 'web_search'`。  \n- **层级**：放在 **最顶层**（bare key），不要嵌在任何 `[section]` 里。  \n\n示例 `config.toml` 片段：\n\n```toml\nweb_search = \"disabled\"\n\n[model]\n# 其他配置...\n```",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2079,
  "completion_tokens": 142,
  "sec": 4.660723924636841,
  "cost": 0.0029537499999999998,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T10",
  "target": "ai_session_profanity_rate",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不能把两种来源一起喂进同一次统计。\n\n`ai-session-profanity-rate` 的文档明确写着：\n\n> Do not combine an archive with its corresponding native stores in one run; that counts the same messages twice.\n\n如果你把 Markdown 归档和原始会话库同时作为输入，系统会把同一批消息统计两遍，导致重复计数和结果失真。\n\n正确做法是二选一：\n\n1. 优先使用原始会话库（OpenCode / Claude Code / Codex / Antigravity），因为它保留了精确时间戳和更强的 per-message 模型归属。\n2. 如果需要跨机器或跨工具的统一格式，再用 AI Session Export 生成的 Markdown 归档单独跑一次。\n\n一次 `prepare` 命令里只能指定 `--source archive` 或原生库，不能同时指定两者。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1550,
  "completion_tokens": 174,
  "sec": 5.062785625457764,
  "cost": 0.0023725,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T11",
  "target": "apple_photos",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "**不可以一次性发起删除。**\n\n根据 skill 文档的硬性限制：\n\n- **每批上限 50 对**：`Delete Batch Bound` 明确规定 “Limit each delete manifest to 50 pairs”。120 对必须拆成至少 3 个独立计划（例如 50 + 50 + 20）。\n- **授权不可复用**：每个计划都需要 **全新的一次性授权令牌**（single-use, manifest-bound HMAC token）。之前拿到的 token 不能用于下一批，也不能重复使用。\n- **不确定结果不能重试**：任何批次出现 `partial` 或 `outcome_unknown` 都视为失败，必须 **停止并只做只读核对**，禁止自动重试或再次尝试同一批次。\n\n**实际操作建议**  \n1. 把 120 对拆成 3 个独立 manifest（≤50 对/个）。  \n2. 每个 manifest 走完整的流程：生成计划 → 人工审查 → 获得新的显式授权 → 执行。  \n3. 任何一批出现非终端状态，立即停止并做只读核对，不要重试。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2141,
  "completion_tokens": 233,
  "sec": 3.099802255630493,
  "cost": 0.00325875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T12",
  "target": "asr",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不能直接处理。\n\n**拒绝阶段**：  \n在 `asr-skill run`（或 `diarize`/`align`）执行前，工具会先检查文件长度。**单个文件超过 7200 秒（2 小时）即被拒绝**，此时模型尚未加载，错误发生在加载模型之前。\n\n**切分是否能解决**：  \n能。把音频切成多个独立文件，每段 ≤ 7200 秒即可绕过限制。注意：  \n- 切分后每个文件独立处理，**说话人标签不会跨文件延续**（即 A 文件的 speaker_0 和 B 文件的 speaker_0 不会自动合并）。  \n- 后续编辑时需手动在 `edited.json` 的 `name_map` 中把同一说话人对应到同一真实姓名。  \n- 切分后的文件可批量放入同一任务目录一起跑 `run`。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2185,
  "completion_tokens": 189,
  "sec": 3.131237030029297,
  "cost": 0.00320375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T13",
  "target": "ct_education",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不行，`ct-edu serve` 的 `--video-file` / `--rendered-video-file` 参数**只能接受相对路径**，且该路径必须位于已完成的 workspace 目录内部。\n\n正确做法是：\n\n1. 把 MP4 复制（或移动）到 workspace 目录下，例如：\n   ```bash\n   cp /tmp/my-demo.mp4 /path/to/external/workspace/demo.mp4\n   ```\n2. 再用相对路径启动服务：\n   ```bash\n   ct-edu serve --workspace /path/to/external/workspace --video-file demo.mp4\n   ```\n\n绝对路径、符号链接、workspace 之外的文件都会触发 `E_VIDEO_FILE` 错误并导致启动失败。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 6485,
  "completion_tokens": 143,
  "sec": 3.96240496635437,
  "cost": 0.00846375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T14",
  "target": "dataforseo",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "40104 错误表示你的账号已通过身份验证，但尚未完成邮箱/电话验证。DataForSEO 的关键词接口（包括 sandbox）在账号未验证前都会被封禁。\n\n解决办法只有一条：去 DataForSEO 后台完成验证流程（通常是邮箱或手机号验证）。验证通过后，关键词接口才会开放。CLI 的 `balance` 命令能用，只是因为它不走关键词端点。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1000,
  "completion_tokens": 88,
  "sec": 2.4906880855560303,
  "cost": 0.00147,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T15",
  "target": "eink_diary",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不需要自己改时间窗，直接用下面这条命令即可：\n\n```bash\neink-diary run --full-day\n```\n\n它会自动把“全天版本”解释为**昨天 00:00 到今天 00:00**（即昨天完整一天），不需要你再加 `--minutes` 或 `--end`。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 3427,
  "completion_tokens": 67,
  "sec": 4.9359118938446045,
  "cost": 0.00445125,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T16",
  "target": "skill_google_maps_routing",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "**是的，只能提供其中一个**：`--departure-time` 或 `--arrival-time` 必须二选一，不能同时提供。\n\n**关于接近当前时刻的 ISO 时间戳**：**会**被 Routes API 拒绝。文档明确指出：\n\n> The CLI avoids sending a fragile explicit near-current timestamp because Routes API may reject it as not sufficiently in the future.\n\n**正确做法**：直接传 `--departure-time now`，CLI 会自动处理成当前交通状况的请求。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1401,
  "completion_tokens": 100,
  "sec": 2.0356690883636475,
  "cost": 0.00200125,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T17",
  "target": "hybrid_ai_video",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "**不能直接再发一次同样的创建请求。**\n\n根据 skill 文档的明确规定：\n\n> “Ambiguous submissions must block automatic resubmission. Never issue a new POST to resume a known task.”\n\n> “Persist both attempt state and a conservative budget reservation before POST. Use exclusive attempt creation and one ledger writer or serialized reservations; separate workers must not race to spend the same reserve.”\n\n> “Ambiguous POST timeout or missing task ID: Retain the attempt and budget reservation; block automatic resubmission pending owner/provider reconciliation.”\n\n你的情况属于“Ambiguous POST timeout or missing task ID”，必须**保留这次尝试和预算预留**，**阻塞自动重发**，等待所有者或提供商协调后再决定是否重试。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 4936,
  "completion_tokens": 152,
  "sec": 2.6583690643310547,
  "cost": 0.00655,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T18",
  "target": "ecg_plot",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "直接拒绝出图。\n\n根据 skill 文档的核对规则：  \n“全部样本落在 0 到 30 秒内。超出 6 条 5 秒窗口的点不会被裁掉，而是拒绝作图。”  \n\n因此，如果你的 JSON 里存在任何采样点时间 > 30 秒，命令会写完整性 JSON，但不会生成 PNG。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 961,
  "completion_tokens": 78,
  "sec": 1.8905019760131836,
  "cost": 0.0013962500000000001,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T19",
  "target": "koyeb",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不可以。`--tail` 在 Koyeb CLI 中是一个布尔开关（true/false），不是行数参数。\n\n正确做法：\n\n1. 先用 `--tail` 打开实时跟随（它会从当前最新日志开始输出）：\n```bash\npython3 scripts/koyeb_env.py -- services logs <service-name> --app <app-name> --tail\n```\n\n2. 如果你确实需要“只看最后 100 行”，Koyeb CLI 没有提供行数限制参数。只能通过 `--start-time`/`--end-time` 缩小时间窗口来间接控制输出量，或直接用 `--tail` 实时跟随。\n\n总结：想实时跟随当前日志就直接加 `--tail` 即可；想限制行数则无法用 `--tail 100` 这种写法。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2666,
  "completion_tokens": 167,
  "sec": 3.117872953414917,
  "cost": 0.00375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T20",
  "target": "narwal",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "根据 skill 文档，`NOT_READY`（退出码 20）是机器人主动拒绝命令的明确答复。文档明确说明：\n\n> `clean/start_clean` **only applies while the robot is docked**；否则机器人会回答 `NOT_READY`。\n\n**原因**：你是在客厅直接下达 `start` 命令，而机器人不在基站上，所以拒绝执行。\n\n**正确做法**：先让机器人回到基站，再下达清扫指令。\n\n1. **先召回基站**（需要 `--yes` 确认）：\n   ```bash\n   narwal-local dock --yes\n   ```\n   或者先用 `--dry-run` 预览：\n   ```bash\n   narwal-local dock --dry-run\n   ```\n\n2. **确认已停靠**后再启动全屋清扫：\n   ```bash\n   narwal-local start --yes\n   ```\n\n或者用 `--dry-run` 先看一下会发送什么：\n```bash\nnarwal-local start --dry-run\n```\n\n**注意**：`start` 命令只在机器人停靠在基站时才有效；如果它正在执行其他任务，也可能返回 `NOT_READY` 或 `CONFLICT`。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 4311,
  "completion_tokens": 250,
  "sec": 3.7679269313812256,
  "cost": 0.00601375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T21",
  "target": "skill_open_router_data_scraper",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "直接传 **canonical_slug**（即模型 slug 后面带 8 位日期的完整形式）即可。\n\n示例：  \n- 短标识：`deepseek/deepseek-chat`  \n- 正确传参：`deepseek/deepseek-chat:free-20250702`（或你实际拿到的 canonical_slug）\n\nCLI 内部已自动做这个转换；如果你手动调用接口，必须用 canonical_slug，否则返回空数组。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1435,
  "completion_tokens": 90,
  "sec": 4.218780994415283,
  "cost": 0.00201875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T22",
  "target": "skill_opencode_data",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "是的，默认情况下会把选中的会话及其子会话（通过 `parent_id` 链）一起算进去。\n\n如果你只想操作被你选中的那些会话本身，不带子会话，需要加 `--no-expand` 参数。\n\n示例命令：\n\n```bash\n.venv/bin/python -m opencode_skill plan --selector title --prefix batch- --no-expand\n```\n\n```bash\n.venv/bin/python -m opencode_skill apply --selector title --prefix batch- --dest ~/.local/share/opencode/opencode_archive.db --confirm --no-expand\n```",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1548,
  "completion_tokens": 117,
  "sec": 2.5372228622436523,
  "cost": 0.0022275000000000003,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T23",
  "target": "video-scoring-audio",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "是的，问题出在 `-ac 1` 这一步。\n\n**原因**：当你把双声道混音（尤其是左右声道高度相关的单声道内容）用 `-ac 1` 转单声道时，ffmpeg 会把两个声道相加，幅度大约增加 **+3 dB**。如果原始混音的峰值已经接近 0 dBFS（比如 -1.5 dBFS），相加后就会超过 1.0，报告削波。\n\n**正确做法**：\n\n1. **测峰值时不要先下混**。直接在双声道文件上测：\n   ```bash\n   ffmpeg -i out.wav -af ebur128=peak=true -f null -\n   ```\n   这会给出 **True Peak**（带过采样检测），比简单 `max(abs(samples))` 更可靠。\n\n2. **如果你确实需要单声道版本**，先把增益拉下来再下混：\n   ```bash\n   ffmpeg -i out.wav -af \"volume=-3dB,pan=mono|c0=0.5*c0+0.5*c1\" mono.wav\n   ```\n   或者用 `amerge` + `pan` 手动控制系数。\n\n3. **交叉验证**：用 `ebur128` 同时看 **Integrated Loudness** 和 **True Peak**，再用 Python 脚本直接读双声道原始样本算峰值做对比。两者一致才算可靠。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 5310,
  "completion_tokens": 309,
  "sec": 4.799237012863159,
  "cost": 0.00741,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T24",
  "target": "speaker_notes",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "普通句子最好控制在 12–20 词之间，绝对不要超过 22 词。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 3132,
  "completion_tokens": 21,
  "sec": 1.3842880725860596,
  "cost": 0.0039675,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T25",
  "target": "skill_tavily",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "**不可以**。`--stdout` 是一个**未被识别的参数**，会直接触发**用法错误**，**不会发起任何网络请求**，退出码为 **2**。\n\n具体表现：\n- 命令行会报错：`unrecognized arguments: --stdout`\n- 不会调用 Tavily API\n- `proc.returncode == 2`\n\n如果你想在控制台看到完整结果，正确做法是**读取 CLI 写入的 JSON 文件**，而不是加 `--stdout`。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 5114,
  "completion_tokens": 104,
  "sec": 2.2967848777770996,
  "cost": 0.0066524999999999996,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T26",
  "target": "zun_music",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "**不可以**。  \n根据 skill 文档的 **Acceptance criteria 第 2 条**：\n\n> “At each strong beat (beats 1 and 3 in 4/4), the arrangement's pitch class equals the untouched source transcription's. Every deliberate departure is listed in `skeleton_allow`, capped at **one per four bars**; normally it is only the final cadence.”\n\n**合规上限**：  \n- 8 小节里最多 **2 处**（每 4 小节 1 处）。  \n- 通常只允许 **1 处**，且只放在 **结尾终止式**（final cadence）。\n\n**测试方法**：  \n1. 把原旋律的强拍音高（1、3 拍）写进 `SOURCE`。  \n2. 编曲后的 `MELODY` 在这些位置必须与 `SOURCE` 完全一致（音高类相同）。  \n3. 任何改动都必须显式列在 `skeleton_allow` 里，且总数 ≤ 2（8 小节）。  \n4. 运行 `python scripts/render_example.py <tune> --midi-only`，若 `check()` 通过即合规，否则不合规。\n\n**结论**：强拍上改动原旋律的上限是 **每 4 小节 1 处**，8 小节最多 2 处，且通常只改终止式。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2827,
  "completion_tokens": 298,
  "sec": 4.073776960372925,
  "cost": 0.00427875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T27",
  "target": "skill_image_generation",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "在 Gemini 模型下，`--quality` 参数不会生效。\n\n根据 skill 文档的说明：\n\n> Gemini accepts the flag but ignores it because quality tiers are provider-specific.\n\n也就是说，Gemini 虽然接受 `--quality` 这个参数，但会直接忽略它。Gemini 的图像生成质量由模型本身（gemini-flash / gemini-pro）决定，没有像 OpenAI GPT Image 模型那样的 quality 层级。\n\n如果你想获得更高质量的 Gemini 图像，应该改用 `--model gemini-pro` 而不是 `--model gemini-flash`（默认）。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2030,
  "completion_tokens": 116,
  "sec": 3.365122079849243,
  "cost": 0.0028274999999999997,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T28",
  "target": "skill_process_launcher",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "要让两条同一时刻运行的任务同时保留：\n\n在 `POST /run` 请求里加上 `\"confirm_conflict\": true` 即可。\n\n示例：\n```bash\ncurl -sf -X POST http://127.0.0.1:7997/run \\\n  -H 'Content-Type: application/json' \\\n  -d '{\n    \"command\": [\"python\", \"job_a.py\"],\n    \"label\": \"job_a\",\n    \"run_at\": \"2026-06-10T09:00:00-07:00\",\n    \"confirm_conflict\": true\n  }'\n```\n\n---\n\n要用新的替换掉旧的：\n\n1. 先取消旧任务：\n   ```bash\n   curl -sf -X POST http://127.0.0.1:7997/scheduled/{旧任务的job_id}/cancel\n   ```\n\n2. 再提交新任务（无需 `confirm_conflict`）。\n\n或者直接用 `PATCH` 把旧任务的时间改掉（改到别的时刻），再提交新任务。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 4011,
  "completion_tokens": 216,
  "sec": 3.36798095703125,
  "cost": 0.00555375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T29",
  "target": "skill_resend_email",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "不能把 `--after/--before` 直接设成 ISO 时间。  \nResend 的这两个参数是**分页游标**（UUID），不是时间戳，传 ISO 字符串会报错。\n\n正确做法（离线环境）：\n\n1. 先导出最近一批邮件（把 `--limit` 设大一点，例如 100）：\n   ```bash\n   .venv/bin/python -m resend_email_skill.cli received export-all-md \\\n     --limit 100 \\\n     --output-dir data/received/markdown \\\n     --format json\n   ```\n\n2. 导出完成后，在本地 Markdown 文件里按 `created_at` 前置元数据筛选“最近两天”的文件即可。  \n   例如用 `grep` 或简单脚本过滤 `created_at` 在过去 48 小时内的文件。\n\n这样既不会触发 API 验证错误，又能得到按日期筛选的结果。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2164,
  "completion_tokens": 181,
  "sec": 2.735438823699951,
  "cost": 0.0031574999999999997,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 },
 {
  "id": "T30",
  "target": "skill_typefully_twitter",
  "model": "grok-4-fast",
  "condition": "full_1",
  "answer": "默认用 **long-post 模式**（即 Typefully 的长帖模式），因为你明确说“只发一条帖子但不限制在 280 字内”。\n\n如果用户明确要求短推（普通 280 字限制），需要在命令里加参数 `--no-long-post`。",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2498,
  "completion_tokens": 63,
  "sec": 1.9483599662780762,
  "cost": 0.00328,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop"
 }
]