Skill: 4myhime/test-for-wang (version 0.0.2)

--- name: narrator-ai-cli version: "1.0.1" license: MIT description: >- Create AI-narrated film/drama commentary videos via CLI. Two workflow paths (Original & Adapted narration), 100+ movies, 146 BGM tracks, 63 dubbing voices in 11 languages, 90+ narration templates. Use when creating narration videos, film commentary, short drama dubbing, or video production. user-invocable: true tags: - video-narration - film-commentary - ai-video - short-drama - content-creation - dubbing - tts - video-production metadata: openclaw: emoji: "🎬" primaryEnv: NARRATOR_APP_KEY install: - name: narrator-ai-cli type: pip spec: "narrator-ai-cli @ https://github.com/GridLtd-ProductDev/narrator-ai-cli/archive/refs/tags/v1.0.0.zip" requires: bins: - narrator-ai-cli env: - NARRATOR_APP_KEY --- # narrator-ai-cli — AI Video Narration CLI Skill CLI client for [Narrator AI](https://openapi.jieshuo.cn) video narration API. Designed for AI Agents and developers. **CLI Repo**: https://github.com/GridLtd-ProductDev/narrator-ai-cli **Resources Preview**: https://ceex7z9m67.feishu.cn/wiki/WLPnwBysairenFkZDbicZOfKnbc ## Installation ```bash # From GitHub release (recommended — pinned to a specific version) pip install "narrator-ai-cli @ https://github.com/GridLtd-ProductDev/narrator-ai-cli/archive/refs/tags/v1.0.0.zip" # Or from GitHub latest (tracks main branch) pip install "narrator-ai-cli @ git+https://github.com/GridLtd-ProductDev/narrator-ai-cli.git" # Or clone + editable install git clone https://github.com/GridLtd-ProductDev/narrator-ai-cli.git cd narrator-ai-cli && pip install -e . ``` Requires Python 3.10+. Dependencies: typer, httpx[socks], httpx-sse, pyyaml, rich. ## Setup ```bash # Interactive setup (server URL + API key) narrator-ai-cli config init # Or set directly narrator-ai-cli config set app_key <your_app_key> # No API key yet? Contact support: WeChat `gezimufeng` or email [REDACTED_SECRET] # Verify narrator-ai-cli config show narrator-ai-cli user balance ``` Config stored at `~/.narrator-ai/config.yaml` (permissions 0600). Server defaults to `https://openapi.jieshuo.cn`. **Environment variable overrides** (take precedence over config file): | Variable | Description | Default | |----------|-------------|---------| | `NARRATOR_SERVER` | API server URL | `https://openapi.jieshuo.cn` | | `NARRATOR_APP_KEY` | API key | (from config) | | `NARRATOR_TIMEOUT` | Request timeout in seconds | 30 | ## Architecture ``` src/narrator_ai/ ├── cli.py # Typer main entry point, 7 sub-command groups ├── client.py # httpx client: GET/POST/DELETE/SSE/upload, auto auth via app-key header ├── config.py # YAML config (~/.narrator-ai/config.yaml), env var override ├── output.py # Rich table + JSON dual output (--json flag) ├── commands/ │ ├── config_cmd.py # config init/show/set │ ├── user.py # balance/login/keys/create-key │ ├── task.py # 9 task types, create/query/list/budget/verify/search-movie/narration-styles/templates/get-writing/save-writing/save-clip │ ├── file.py # 3-step upload (presigned URL → OSS PUT → callback), download/list/info/storage/delete │ ├── materials.py # 100+ pre-built movies (--page/--size pagination; no --genre/--search, filter locally) │ ├── bgm.py # 146 BGM tracks (--search filter) │ └── dubbing.py # 63 voices, 11 languages (--lang, --tag, --search filters) └── models/ └── responses.py # API response codes (SUCCESS=10000, FAILED=10001, etc.) + task status constants ``` **Key design choices:** - All commands support `--json` for machine-readable output (always use when parsing programmatically) - Request body via `-d '{"key": "value"}'` or `-d @file.json` - HTTP client uses `app-key` header (not Bearer token) - SSE streaming supported for real-time task progress (`--stream`) - File upload is 3-step: presigned URL → direct OSS upload → callback confirmation ## Core Concepts | Concept | Description | |---------|-------------| | **file_id** | UUID for uploaded files. Via `file upload` or task results | | **task_id** | UUID returned on task creation. Poll with `task query` | | **task_order_num** | Assigned after task creation. Used as `order_num` for downstream tasks | | **file_ids** | Output file IDs in completed task results. Input for next steps | | **learning_model_id** | Narration style model. From popular-learning OR pre-built template (90+) | | **learning_srt** | Reference SRT file_id. Only needed when NOT using learning_model_id | ## Two Workflow Paths ### Path 1: Adapted Narration (二创文案, Standard) ``` material list (local search) → [file upload if not in materials] → popular-learning → generate-writing → clip-data → video-composing → magic-video(optional) ``` ### Path 2: Original Narration (原创文案, Fast & Cheaper) ``` material list (local search) → [search-movie if not in materials] → fast-writing → fast-clip-data → video-composing → magic-video(optional) ``` > ⚠️ **Agent behavior**: Before starting, always ask the user which path to use — **Standard** (二创文案, adapted narration) or **Fast** (原创文案, recommended). Do not auto-select a path. ### 3 Modes (target_mode for fast-writing) | Mode | Name | Required Input | |------|------|----------------| | `"1"` | 热门影视 (纯解说) | `confirmed_movie_json` (from material data or `search-movie`); **no `episodes_data`** | | `"2"` | 原声混剪 (Original Mix) | `confirmed_movie_json` + **`episodes_data[{srt_oss_key, num}]`** required | | `"3"` | 冷门/新剧 (New Drama) | **`episodes_data[{srt_oss_key, num}]`** required; `confirmed_movie_json` optional | ## Resource Selection Protocol **All resource selection steps require user confirmation before proceeding.** Follow these rules at every resource step: 1. **Never auto-select.** Always fetch options via CLI, present them to the user, and wait for explicit confirmation before using any resource in a task. 2. **Present up to 5–8 options** per resource type. Pre-filter by context (content genre, mood, language) to surface the most relevant candidates. 3. **Fallback when user has no preference.** If the user expresses no preference, present exactly **3 options** with a recommendation and the reasoning for each — still wait for confirmation before proceeding. 4. **Show the right fields.** Agent decides which fields to display per resource type, but always include the resource ID needed for the task parameter. 5. **Confirm one resource at a time.** Source files → BGM → Dubbing → Template. Do not advance to task creation until all required resources are confirmed. ## Prerequisites: Select Resources Before creating any task, gather these resources first. ### 1. Source Files (Video + SRT) > ⚠️ **Agent behavior**: Use `material list --json --page 1 --size 100` to fetch pre-built materials. Check the `total` field in the response — if `total > 100`, fetch additional pages until all items are retrieved. **Search programmatically using `grep` or `python3 -c` piped from the JSON output — do NOT rely on the terminal display, which may be truncated and can miss items.** Present **all matching results** (usually ≤ 3) to the user — show title, year, genre, and summary. Wait for the user to pick one before proceeding. If the user wants to upload their own files, guide them through the `file upload` flow for both video and SRT. Do NOT proceed to any writing step until `video_file_id` and `srt_file_id` are confirmed by the user. ```bash # Option A: Pre-built materials (90+ movies, recommended) narrator-ai-cli material list --json --page 1 --size 100 # If total > 100, fetch more pages: --page 2 --size 100, etc., until all items are retrieved ``` Response structure: ```json { "total": 101, "page": 1, "size": 100, "items": [ { "id": "<material_id>", "name": "极限职业", "title": "Extreme Job", "year": "2019", "type": "喜剧片", "story_info": "...", "character_name": "[柳承龙 (Ryu Seung-ryong), 李荷妮 (Lee Ha-nee), ...]", "cover": "https://...", "video_file_id": "<video_file_id>", "srt_file_id": "<srt_file_id>" } ] } ``` ```bash # Search programmatically (case-insensitive) — do NOT rely on truncated terminal output: narrator-ai-cli material list --json --page 1 --size 100 | grep -i "飞驰人生" narrator-ai-cli material list --json --page 1 --size 100 \ | python3 -c "import json, sys; items = json.load(sys.stdin).get('items', []); \ [print(json.dumps(i, ensure_ascii=False)) for i in items if '飞驰' in i.get('name','') or '飞驰' in i.get('title','')]" ``` **Material → `confirmed_movie_json` field mapping** (construct locally, no `search-movie` needed): | Material field | `confirmed_movie_json` field | Notes | |---|---|---| | `name` | `local_title` | Chinese title | | `title` | `title` | English title | | `year` | `year` | | | `type` | `genre` | e.g. `喜剧片` | | `story_info` | `summary` | | | `character_name` | `stars` | Parse JSON array string | | (not in material) | `director` | Omit if unavailable | ```bash # Option B: Upload your own narrator-ai-cli file upload ./movie.mp4 --json # Returns file_id narrator-ai-cli file upload ./subtitles.srt --json narrator-ai-cli file list --json narrator-ai-cli file transfer --link "<url>" --json # transfer by HTTP/Baidu/PikPak link narrator-ai-cli file info <file_id> --json narrator-ai-cli file download <file_id> --json narrator-ai-cli file storage --json narrator-ai-cli file delete <file_id> --json ``` Supported formats: .mp4, .mkv, .mov, .mp3, .m4a, .wav, .srt, .jpg, .jpeg, .png ### 2. BGM (Background Music) > ⚠️ **Agent behavior**: Infer the mood/genre from context, then use `bgm list --search "<keyword>"` to pre-filter. Present **5–8 tracks** (Agent decides which fields best represent each track — e.g., name, style description). If the user has no preference, recommend **3 tracks** with a brief reason for each (e.g., "matches the film's fast-paced action tone") and wait for confirmation. Do NOT use a `bgm` ID in any task until the user confirms. ```bash narrator-ai-cli bgm list --json # 146 tracks narrator-ai-cli bgm list --search "单车" --json # Returns: id (= bgm parameter in task creation) ``` ### 3. Dubbing Voice > ⚠️ **Agent behavior**: Infer the target language from context; if ambiguous, ask the user before listing. Run `dubbing list --lang <language>` to filter, then present **all matching voices** (typically < 15 per language) — include name and tags. If the user has no preference, recommend **3 voices** with reasoning (e.g., "neutral tone fits documentary narration style") and wait for confirmation. Do NOT use a dubbing `id` or `dubbing_type` in any task until the user confirms both. ```bash narrator-ai-cli dubbing list --json # 63 voices, 11 languages narrator-ai-cli dubbing list --lang 普通话 --json narrator-ai-cli dubbing list --tag 喜剧 --json narrator-ai-cli dubbing languages --json narrator-ai-cli dubbing tags --json # Returns: id (= dubbing), type (= dubbing_type) ``` Languages: 普通话(39), English(4), 日语(3), 韩语(2), Spanish(3), Portuguese(2), German(2), French(2), Arabic(2), Thai(2), Indonesian(2). ### 4. Narration Style Templates (90+, 12 genres) > ⚠️ **Agent behavior**: Infer the content genre from context and run `task narration-styles --genre <genre>` to pre-filter. Present **3–5 templates** (Agent decides which fields best represent each). Also share the preview link https://ceex7z9m67.feishu.cn/wiki/WLPnwBysairenFkZDbicZOfKnbc to help the user browse visually. If the user has no preference, recommend **3 templates** with a brief style description and reasoning, and wait for confirmation. Do NOT use a `learning_model_id` in any task until the user confirms. ```bash narrator-ai-cli task narration-styles --json narrator-ai-cli task narration-styles --genre 爆笑喜剧 --json ``` Genres: 热血动作, 烧脑悬疑, 励志成长, 爆笑喜剧, 灾难求生, 悬疑惊悚, 惊悚恐怖, 东方奇谈, 家庭伦理, 情感人生, 奇幻科幻, 传奇人物 Use `learning_model_id` from template directly — **no need for popular-learning step**. ## Fast Path Workflow (Recommended) ### Step 0: Find Source Material & Determine Mode > ⚠️ **Agent behavior**: Confirm the movie or drama name with the user before proceeding (ask if not yet specified). Then follow this decision flow to determine source material and `target_mode`. **Decision flow:** 1. Run `material list --json --page 1 --size 100`. Check `total` in the response — if `total > 100`, fetch subsequent pages until all items are retrieved. **Search programmatically using `grep -i` or `python3 -c` piped from the JSON output — do NOT rely on the terminal display, which may be truncated.** Repeat for each page until a match is found or all pages are exhausted. 2. **Found in pre-built materials** → construct `confirmed_movie_json` from material fields (see mapping in Prerequisites § Source Files). Present the match to the user and **ask which mode**: - **纯解说 / Pure narration (target_mode=1)**: `confirmed_movie_json` from material. **No `episodes_data`.** - **原声混剪 / Original mix (target_mode=2)**: `confirmed_movie_json` from material + `episodes_data` using material's `srt_file_id` as `srt_oss_key`. 3. **Not found in materials (known movie/drama)** → run `task search-movie` (see command below) → `target_mode=1`. Use returned `confirmed_movie_json`. **No `episodes_data`.** 4. **Not found, user provides their own SRT (known movie)** → run `task search-movie` for `confirmed_movie_json` → `target_mode=2`. Use uploaded SRT as `srt_oss_key` in `episodes_data`. 5. **Obscure/new drama, user provides SRT** → `target_mode=3`. `confirmed_movie_json` is optional. Use uploaded SRT in `episodes_data`. **`search-movie` command** (run only for flows 3 and 4 above; never fabricate its output): ```bash narrator-ai-cli task search-movie "飞驰人生" --json ``` Returns up to 3 results. Each result contains: ```json { "title": "string", "local_title": "string", "year": "string", "director": "string", "stars": ["string"], "genre": "string", "summary": "string" } ``` ⚠️ May take **60+ seconds** (Gradio backend). Results cached 24h. ### Step 1: Fast Writing Using the `target_mode`, `confirmed_movie_json`, and `episodes_data` determined in Step 0, create the fast-writing task: ```bash # Case A1: Pre-built material found, user chose pure narration (target_mode=1) # confirmed_movie_json from material data; no episodes_data narrator-ai-cli task create fast-writing --json -d @request.json # request.json: # { # "learning_model_id": "...", # "target_mode": "1", # "playlet_name": "飞驰人生", # "confirmed_movie_json": { # "local_title": "<material.name>", # "title": "<material.title>", # "year": "<material.year>", # "genre": "<material.type>", # "summary": "<material.story_info>", # "stars": <material.character_name (parsed array)> # }, # "model": "flash" # } # Case A2: Pre-built material found, user chose original mix (target_mode=2) # confirmed_movie_json from material data; episodes_data uses material.srt_file_id narrator-ai-cli task create fast-writing --json -d @request.json # request.json: # { # "learning_model_id": "...", # "target_mode": "2", # "playlet_name": "飞驰人生", # "confirmed_movie_json": { # "local_title": "<material.name>", # "title": "<material.title>", # "year": "<material.year>", # "genre": "<material.type>", # "summary": "<material.story_info>", # "stars": <material.character_name (parsed array)> # }, # "episodes_data": [{"srt_oss_key": "<material.srt_file_id>", "num": 1}], # "model": "flash" # } # Case B: Not in pre-built materials, known movie (target_mode=1) — run search-movie in Step 0 narrator-ai-cli task create fast-writing --json -d @request.json # request.json: {"learning_model_id": "...", "target_mode": "1", "playlet_name": "...", # "confirmed_movie_json": {<from search-movie>}, "model": "flash"} # Case C: User's own SRT, known movie (target_mode=2) — run search-movie in Step 0 for confirmed_movie_json narrator-ai-cli task create fast-writing --json -d @request.json # request.json: {"learning_model_id": "...", "target_mode": "2", "playlet_name": "<drama name>", # "confirmed_movie_json": {<from search-movie>}, "episodes_data": [{"srt_oss_key": "<uploaded srt file_id>", "num": 1}], "model": "flash"} # Case D: Obscure/new drama, user's own SRT (target_mode=3) — confirmed_movie_json optional narrator-ai-cli task create fast-writing --json -d '{ "learning_model_id": "<from narration-styles>", "target_mode": "3", "playlet_name": "<drama name>", "episodes_data": [{"srt_oss_key": "<uploaded srt file_id>", "num": 1}], "model": "flash" }' ``` **Full parameters:** | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `learning_model_id` | str | One of two | - | Style model ID (from template or popular-learning) | | `learning_srt` | str | One of two | - | Reference SRT file_id (when no template available) | | `target_mode` | str | Yes | - | "1"=Hot Drama, "2"=Original Mix, "3"=New Drama | | `playlet_name` | str | Yes | - | Movie/drama name | | `confirmed_movie_json` | obj | mode=1,2; optional mode=3 | - | From material data (mode=2 pre-built) or `search-movie` result (mode=1, mode=2 user SRT). Never fabricate. | | `episodes_data` | list | mode=2,3 | - | [{srt_oss_key, num}] | | `model` | str | No | "pro" | "pro" (higher quality, 15pts/char) or "flash" (faster, 5pts/char) | | `language` | str | No | "Chinese (中文)" | Output language | | `perspective` | str | No | "third_person" | "first_person" or "third_person" | | `target_character_name` | str | 1st person | - | Required when perspective=first_person | | `custom_script_result_path` | str | No | - | Custom script result path | | `webhook_url` | str | No | - | Async callback URL | | `webhook_token` | str | No | - | Callback authentication token | | `webhook_data` | str | No | - | Passthrough data for callback | **Output**: On creation returns `data.task_id`. Poll `task query <task_id> --json` until `status=2`. Extract `file_ids[0]`: ```json { "tasks": [{ "task_id": "<task_id>", "order_num": "<order_num>" }], "file_ids": ["<file_id>"] } ``` Save: `task_id` from creation response (for fast-clip-data `task_id` input), `file_ids[0]` (for fast-clip-data `file_id` input). ### Step 2: Fast Clip Data **Input**: `task_id` and `file_id` from Fast Writing (step 1), plus `bgm`, `dubbing`, `episodes_data`. ```bash narrator-ai-cli task create fast-clip-data --json -d '{ "task_id": "<task_id from step 1>", "file_id": "<file_id from step 1>", "bgm": "<bgm_id>", "dubbing": "<voice_id>", "dubbing_type": "<dubbing_type from selected voice>", "episodes_data": [{"video_oss_key": "<video_file_id>", "srt_oss_key": "<srt_file_id>", "negative_oss_key": "<video_file_id>", "num": 1}] }' ``` **Output**: Creation response: ```json {"code": 10000, "message": "", "data": {"task_id": ""}} ``` Save `data.task_id`. Poll `task query <task_id> --json` until `status=2`. On success, read `task_order_num` from the task record — this is the `order_num` required for video-composing (step 3). ### Step 3: Video Composing **IMPORTANT**: `order_num` comes from fast-clip-data (step 2). This is the **only required parameter**. ```bash narrator-ai-cli task create video-composing --json -d '{ "order_num": "<task_order_num from step 2>" }' ``` **Output**: On creation returns `data.task_id`. Poll `task query <task_id> --json` until `status=2`. Extract `video_url` from results: ```json { "tasks": [{ "video_url": "https://oss.example.com/.../output.mp4" }] } ``` Note: `type_name` is `video_composing` (no BGM) or `video_composing_2` (with BGM); both return `video_url` in the same structure. ### Step 4 (Optional): Magic Video — Visual Template > ⚠️ **Agent restriction**: Do NOT auto-create magic-video tasks. Only create when the user explicitly requests a visual template. Present available templates as options and let the user choose. ```bash # List templates first narrator-ai-cli task templates --json # One-stop mode (from video-composing task_id) narrator-ai-cli task create magic-video --json -d '{ "task_id": "<task_id from step 3>", "template_name": ["template_name"] }' # Staged mode (from clip-data/fast-clip-data file_ids[0]) narrator-ai-cli task create magic-video --json -d '{ "file_id": "<file_ids[0] from clip-data or fast-clip-data results>", "template_name": ["template_name"] }' ``` Optional: template_params (per-template params dict), mode (one_stop/staged), clip_data (JSON object for staged mode) **Output**: sub_tasks with rendered video URLs ## Standard Path Workflow ### Step 0: Find Source Material > ⚠️ **Agent behavior**: Confirm the movie or drama name with the user before proceeding. Then run `material list --json --page 1 --size 100` to fetch pre-built materials. Check `total` in the response — if `total > 100`, fetch subsequent pages until all items are retrieved. **Search programmatically using `grep -i` or `python3 -c` piped from the JSON output — do NOT rely on the terminal display, which may be truncated and can miss items.** ```bash narrator-ai-cli material list --json --page 1 --size 100 # If total > 100, fetch more pages: --page 2 --size 100, etc. ``` Response structure: ```json { "total": 101, "page": 1, "size": 100, "items": [ { "id": "<material_id>", "name": "极限职业", "title": "Extreme Job", "year": "2019", "type": "喜剧片", "story_info": "...", "character_name": "[柳承龙 (Ryu Seung-ryong), 李荷妮 (Lee Ha-nee), ...]", "cover": "https://...", "video_file_id": "<video_file_id>", "srt_file_id": "<srt_file_id>" } ] } ``` ```bash # Search programmatically — do NOT rely on truncated terminal output: narrator-ai-cli material list --json --page 1 --size 100 | grep -i "目标影片名" narrator-ai-cli material list --json --page 1 --size 100 \ | python3 -c "import json, sys; items = json.load(sys.stdin).get('items', []); \ [print(json.dumps(i, ensure_ascii=False)) for i in items if '关键词' in i.get('name','') or '关键词' in i.get('title','')]" ``` > `video_file_id` — the video file ID for this pre-built material (used as `video_oss_key` and `negative_oss_key`) > `srt_file_id` — the subtitle (SRT) file ID for this pre-built material (used as `srt_oss_key`) **Decision flow:** 1. Fetch all pages (start with `--page 1 --size 100`, repeat if `total > fetched count`). **Search programmatically** using `grep -i` or `python3 -c` on the JSON output — do NOT scan the terminal display. Repeat per page until match found or all pages exhausted. 2. **Found in pre-built materials** → use the material's `video_file_id` as `video_oss_key`/`negative_oss_key` and `srt_file_id` as `srt_oss_key` in `episodes_data` for Step 2 (generate-writing). No need to upload files. 3. **Not found** → guide user to upload their own video and SRT files via `file upload` (see Prerequisites § Source Files). Use the returned `file_id` values as `video_oss_key`/`negative_oss_key` and `srt_oss_key` in `episodes_data`. ### Step 1: Popular Learning (optional if using pre-built template) ```bash narrator-ai-cli task create popular-learning --json -d '{ "video_srt_path": "<srt_file_id from Step 0>", "narrator_type": "movie", "model_version": "advanced" }' ``` **narrator_type options**: `短剧` `电影` `第一人称电影` `多语种电影` `第一人称多语种` `movie` `short_drama` `first_person_movie` `multilingual` `first_person_multilingual` **model_version**: `advanced` (高级版) or `standard` (标准版) **Output**: On creation returns `data.task_id`. Poll `task query <task_id> --json` until `status=2`. Parse `task_result` JSON string → `agent_unique_code` is the `learning_model_id`: ```json { "tasks": [{ "task_result": "{\"agent_unique_code\": \"narrator-20251121160424-wjtOXO\"}" }] } ``` → `learning_model_id = "narrator-20251121160424-wjtOXO"` Alternatively, use a pre-built template `id` from `task narration-styles --json` as `learning_model_id` directly — **no popular-learning step needed**. ### Step 2: Generate Writing **Input**: Use `video_file_id` and `srt_file_id` determined in Step 0 to construct `episodes_data`: | `episodes_data` field | Source | |---|---| | `video_oss_key` | `video_file_id` from material (Step 0) or uploaded video `file_id` | | `negative_oss_key` | same as `video_oss_key` | | `srt_oss_key` | `srt_file_id` from material (Step 0) or uploaded SRT `file_id` | | `num` | episode number, starting from `1` | ```bash narrator-ai-cli task create generate-writing --json -d '{ "learning_model_id": "<from step 1 or pre-built template>", "playlet_name": "Movie Name", "playlet_num": "1", "episodes_data": [{"video_oss_key": "<video_file_id>", "srt_oss_key": "<srt_file_id>", "negative_oss_key": "<video_file_id>", "num": 1}], "refine_srt_gaps": false }' ``` Optional: `refine_srt_gaps` (bool) — enables AI scene analysis. **Only set to `true` when user explicitly requests it.** **Output**: On creation returns `data.task_id`. Poll `task query <task_id> --json` until `status=2`. Extract `task_result` (narration script file path) and `order_info` from results: ```json { "tasks": [{ "task_result": "video-clips-data/20251126/narrator/t_66449_47KIRY/narration.txt" }], "order_info": { "order_num": "script_69269bfc_GfVEgA" } } ``` Save: `task_id` from the initial creation response — **required as input for clip-data step**. ### Step 3: Clip Data **Input**: `task_id` from generate-writing (step 2), plus `bgm` and `dubbing`. ```bash narrator-ai-cli task create clip-data --json -d '{ "task_id": "<task_id from step 2 (generate-writing) creation response>", "bgm": "<bgm_id>", "dubbing": "<voice_id>", "dubbing_type": "<dubbing_type from selected voice>" }' ``` **Output**: Creation response: ```json {"code": 10000, "message": "", "data": {"task_id": ""}} ``` Save `data.task_id`. Poll `task query <task_id> --json` until `status=2`. On success, read `task_order_num` from the task record — this is the `order_num` required for video-composing (step 4). ### Step 4-5: Same as Fast Path Steps 3-4 **IMPORTANT**: video-composing uses `order_num` from **clip-data (step 3)** `order_info.order_num`, NOT from generate-writing. ## Standalone Tasks ### Voice Clone ```bash narrator-ai-cli task create voice-clone --json -d '{"audio_file_id": "<file_id>"}' ``` Optional: clone_model (default: pro). Output: task_id, voice_id. ### Text to Speech ```bash narrator-ai-cli task create tts --json -d '{"voice_id": "<voice_id>", "audio_text": "Text to speak"}' ``` Optional: clone_model (default: pro). Output: task_id with audio result. ## Task Management ```bash # Query task status (poll until status 2=success or 3=failed) narrator-ai-cli task query <task_id> --json # List tasks with filters narrator-ai-cli task list --json narrator-ai-cli task list --status 2 --type 9 --json # completed fast-writing narrator-ai-cli task list --category commentary --json # Estimate points cost before creating narrator-ai-cli task budget --json -d '{ "learning_model_id": "<id>", "native_video": "<file_id>", "native_srt": "<file_id>" }' # Returns: viral_learning_points, commentary_generation_points, video_synthesis_points, visual_template_points, total_consume_points # Verify materials before task creation narrator-ai-cli task verify --json -d '{ "bgm": "<file_id>", "dubbing_id": "<voice_id>", "native_video": "<file_id>", "native_srt": "<file_id>" }' # Returns: is_valid (bool), errors (list), warnings (list) # Retrieve/save narration scripts narrator-ai-cli task get-writing --json narrator-ai-cli task save-writing -d '{...}' narrator-ai-cli task save-clip -d '{...}' # List task types with details narrator-ai-cli task types -V ``` **Task type IDs** (for --type filter): | ID | Type | |----|------| | 1 | popular_learning | | 2 | generate_writing | | 3 | video_composing | | 4 | voice_clone | | 5 | tts | | 6 | clip_data | | 7 | magic_video | | 8 | subsync | | 9 | fast_writing | | 10 | fast_clip_data | **Task status codes**: 0=init, 1=in_progress, 2=success, 3=failed, 4=cancelled. ## File Operations ```bash narrator-ai-cli file upload ./video.mp4 --json # 3-step: presigned → OSS → callback narrator-ai-cli file list --json # pagination, --search filter narrator-ai-cli file info <file_id> --json # name, path, size, category, timestamps narrator-ai-cli file download <file_id> --json # returns presigned URL (time-limited) narrator-ai-cli file storage --json # used_size, max_size, usage_percentage narrator-ai-cli file delete <file_id> --json # irreversible ``` File categories: 1=video, 2=audio, 3=image, 4=doc, 5=torrent, 6=other. ## User & Account ```bash narrator-ai-cli user balance --json # account points balance narrator-ai-cli user login --json # login with username/password narrator-ai-cli user keys --json # list sub API keys narrator-ai-cli user create-key --json # create a new sub API key ``` ## Error Handling > **Support Contact** (for balance/billing, app_key issues — including obtaining, renewing, or troubleshooting API keys): WeChat `gezimufeng`, or email `[REDACTED_SECRET]` | Code | Meaning | Action | |------|---------|--------| | `10000` | Success | - | | `10001` | Failed | Check params | | `10002` | App key expired | Contact support to renew key (see **Support Contact** above) | | `10003` | Sign expired | Check timestamp | | `10004` | Invalid app key | Run `config show` to verify; if incorrect, contact support to obtain a valid key (see **Support Contact** above) | | `10005` | Invalid sign | Check app_key config; contact support if issue persists (see **Support Contact** above) | | `10006` | Invalid timestamp | Check clock sync | | `10007` | Not found | Check resource ID | | `10008` | Invalid method | Check HTTP method | | `10009` | Insufficient balance | Contact support to top up (see **Support Contact** above) | | `10010` | Task not found | Verify task_id | | `10011` | Task create failed | Retry or check params | | `10012` | Task type not found | Use `task types` to list valid types | | `10013` | Insufficient balance (key) | Contact support to top up sub-key quota (see **Support Contact** above) | | `40000` | Gradio timeout | Retry (backend overloaded) | | `50000` | Unauthorized | Check app_key config; contact support if key is missing or invalid (see **Support Contact** above) | | `50001` | Database error | Retry later | | `50002` | System busy | Retry later | | `50003` | System error | Contact support | | `60000` | Retryable error | Safe to retry | CLI exits code 1 on any error, prints to stderr. ## Data Flow Summary ``` material list / file upload → video_file_id, srt_file_id bgm list → bgm_id dubbing list → dubbing, dubbing_type narration-styles → learning_model_id │ ┌───────────────────┼───────────────────────┐ │ Standard Path │ Fast Path │ │ │ │ ▼ │ ▼ material list --json │ material list --json (local search) │ (local search by title) found → video_file_id │ found → ask user: mode=1 or mode=2? srt_file_id │ mode=1: confirmed_movie_json from material not found → file upload│ mode=2: confirmed_movie_json + episodes_data from material │ │ (both skip search-movie) ▼ │ not found → search-movie (Step 0) → target_mode=1 popular-learning │ user SRT known → search-movie + target_mode=2 OUT: learning_model_id │ user SRT obscure → target_mode=3 (optional confirmed_movie_json) (or use template) │ │ ▼ │ ▼ │ │ fast-writing generate-writing │ OUT: task_id, file_ids[0] OUT: task_id ─────────┬│ │ │ ││ ▼ ▼ ││ fast-clip-data clip-data ││ IN: task_id + file_id IN: generate-writing ││ OUT: file_ids[0] task_id ││ order_info.order_num OUT: file_ids[0] ││ │ order_info ││ │ .order_num ───────┴┴────────────────────────┘ │ ▼ video-composing IN: order_num (from clip-data or fast-clip-data!) bgm, dubbing, dubbing_type OUT: task_id, tasks[0].video_url │ ▼ magic-video (OPTIONAL — only on explicit user request) IN: task_id (one-stop) OR file_ids[0] from clip-data (staged) template_name (from 'task templates') OUT: sub_tasks with rendered video URLs ``` ## ⚠️ Important Notes 1. **`confirmed_movie_json` is required for target_mode=1 and target_mode=2, optional for target_mode=3.** When a pre-built material is found, construct it from material fields directly (no `search-movie` needed). For mode=1 or mode=2 with user-uploaded SRT (no material), always run `search-movie` — never fabricate this value. 2. **Source file_ids from `file list` or `material list`.** Never guess file_ids. 3. **Tasks are async.** Create returns `task_id` → poll `task query <task_id> --json` until status `2` (success) or `3` (failed). 4. **`search-movie` may take 60+ seconds** (Gradio backend, cached 24h). Set adequate timeout. 5. **video-composing uses the clip-data step's `order_info.order_num`** (clip-data in Standard Path, fast-clip-data in Fast Path). NOT the writing step's order_num — this is the most common mistake. 6. **Prefer pre-built narration templates** over running popular-learning. Use `task narration-styles --json` to list, browse https://ceex7z9m67.feishu.cn/wiki/WLPnwBysairenFkZDbicZOfKnbc for preview. 7. **Use `-d @file.json`** for large request bodies to avoid shell quoting issues. 8. **Use `task verify`** before creating expensive tasks to catch missing/invalid materials early. 9. **Use `task budget`** to estimate points cost before committing to a task. ## 🔒 Data & Privacy - **API Endpoint**: All API requests are sent to `https://openapi.jieshuo.cn` (the Narrator AI service). No data is sent to any other third-party service. - **File Upload**: The file upload flow (presigned URL → OSS PUT → callback) transfers user-provided media files to the Narrator AI cloud for server-side video processing. Uploaded files are bound to your account and are not shared publicly. - **Credentials**: An API key (`NARRATOR_APP_KEY`) is required and stored locally at `~/.narrator-ai/config.yaml`. Keep this file private and do not commit it to version control. - **Scope**: This skill only orchestrates CLI commands — it does not access, read, or transmit any files beyond those you explicitly provide as input to a task.


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