Initial commit: hardened DeerFlow factory
Vendored deer-flow upstream (bytedance/deer-flow) plus prompt-injection hardening: - New deerflow.security package: content_delimiter, html_cleaner, sanitizer (8 layers — invisible chars, control chars, symbols, NFC, PUA, tag chars, horizontal whitespace collapse with newline/tab preservation, length cap) - New deerflow.community.searx package: web_search, web_fetch, image_search backed by a private SearX instance, every external string sanitized and wrapped in <<<EXTERNAL_UNTRUSTED_CONTENT>>> delimiters - All native community web providers (ddg_search, tavily, exa, firecrawl, jina_ai, infoquest, image_search) replaced with hard-fail stubs that raise NativeWebToolDisabledError at import time, so a misconfigured tool.use path fails loud rather than silently falling back to unsanitized output - Native client back-doors (jina_client.py, infoquest_client.py) stubbed too - Native-tool tests quarantined under tests/_disabled_native/ (collect_ignore_glob via local conftest.py) - Sanitizer Layer 7 fix: only collapse horizontal whitespace, preserve newlines and tabs so list/table structure survives - Hardened runtime config.yaml references only the searx-backed tools - Factory overlay (backend/) kept in sync with deer-flow tree as a reference / source See HARDENING.md for the full audit trail and verification steps.
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deer-flow/skills/public/podcast-generation/SKILL.md
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deer-flow/skills/public/podcast-generation/SKILL.md
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---
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name: podcast-generation
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description: Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
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---
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# Podcast Generation Skill
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## Overview
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This skill generates high-quality podcast audio from text content. The workflow includes creating a structured JSON script (conversational dialogue) and executing audio generation through text-to-speech synthesis.
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## Core Capabilities
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- Convert any text content (articles, reports, documentation) into podcast scripts
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- Generate natural two-host conversational dialogue (male and female hosts)
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- Synthesize speech audio using text-to-speech
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- Mix audio chunks into a final podcast MP3 file
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- Support both English and Chinese content
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## Workflow
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### Step 1: Understand Requirements
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When a user requests podcast generation, identify:
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- Source content: The text/article/report to convert into a podcast
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- Language: English or Chinese (based on content)
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- Output location: Where to save the generated podcast
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- You don't need to check the folder under `/mnt/user-data`
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### Step 2: Create Structured Script JSON
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Generate a structured JSON script file in `/mnt/user-data/workspace/` with naming pattern: `{descriptive-name}-script.json`
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The JSON structure:
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```json
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{
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"locale": "en",
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"lines": [
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{"speaker": "male", "paragraph": "dialogue text"},
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{"speaker": "female", "paragraph": "dialogue text"}
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]
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}
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```
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### Step 3: Execute Generation
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Call the Python script:
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```bash
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python /mnt/skills/public/podcast-generation/scripts/generate.py \
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--script-file /mnt/user-data/workspace/script-file.json \
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--output-file /mnt/user-data/outputs/generated-podcast.mp3 \
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--transcript-file /mnt/user-data/outputs/generated-podcast-transcript.md
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```
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Parameters:
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- `--script-file`: Absolute path to JSON script file (required)
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- `--output-file`: Absolute path to output MP3 file (required)
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- `--transcript-file`: Absolute path to output transcript markdown file (optional, but recommended)
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> [!IMPORTANT]
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> - Execute the script in one complete call. Do NOT split the workflow into separate steps.
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> - The script handles all TTS API calls and audio generation internally.
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> - Do NOT read the Python file, just call it with the parameters.
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> - Always include `--transcript-file` to generate a readable transcript for the user.
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## Script JSON Format
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The script JSON file must follow this structure:
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```json
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{
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"title": "The History of Artificial Intelligence",
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"locale": "en",
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"lines": [
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{"speaker": "male", "paragraph": "Hello Deer! Welcome back to another episode."},
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{"speaker": "female", "paragraph": "Hey everyone! Today we have an exciting topic to discuss."},
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{"speaker": "male", "paragraph": "That's right! We're going to talk about..."}
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]
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}
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```
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Fields:
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- `title`: Title of the podcast episode (optional, used as heading in transcript)
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- `locale`: Language code - "en" for English or "zh" for Chinese
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- `lines`: Array of dialogue lines
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- `speaker`: Either "male" or "female"
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- `paragraph`: The dialogue text for this speaker
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## Script Writing Guidelines
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When creating the script JSON, follow these guidelines:
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### Format Requirements
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- Only two hosts: male and female, alternating naturally
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- Target runtime: approximately 10 minutes of dialogue (around 40-60 lines)
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- Start with the male host saying a greeting that includes "Hello Deer"
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### Tone & Style
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- Natural, conversational dialogue - like two friends chatting
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- Use casual expressions and conversational transitions
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- Avoid overly formal language or academic tone
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- Include reactions, follow-up questions, and natural interjections
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### Content Guidelines
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- Frequent back-and-forth between hosts
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- Keep sentences short and easy to follow when spoken
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- Plain text only - no markdown formatting in the output
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- Translate technical concepts into accessible language
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- No mathematical formulas, code, or complex notation
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- Make content engaging and accessible for audio-only listeners
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- Exclude meta information like dates, author names, or document structure
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## Podcast Generation Example
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User request: "Generate a podcast about the history of artificial intelligence"
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Step 1: Create script file `/mnt/user-data/workspace/ai-history-script.json`:
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```json
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{
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"title": "The History of Artificial Intelligence",
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"locale": "en",
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"lines": [
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{"speaker": "male", "paragraph": "Hello Deer! Welcome back to another fascinating episode. Today we're diving into something that's literally shaping our future - the history of artificial intelligence."},
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{"speaker": "female", "paragraph": "Oh, I love this topic! You know, AI feels so modern, but it actually has roots going back over seventy years."},
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{"speaker": "male", "paragraph": "Exactly! It all started back in the 1950s. The term artificial intelligence was actually coined by John McCarthy in 1956 at a famous conference at Dartmouth."},
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{"speaker": "female", "paragraph": "Wait, so they were already thinking about machines that could think back then? That's incredible!"},
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{"speaker": "male", "paragraph": "Right? The early pioneers were so optimistic. They thought we'd have human-level AI within a generation."},
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{"speaker": "female", "paragraph": "But things didn't quite work out that way, did they?"},
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{"speaker": "male", "paragraph": "No, not at all. The 1970s brought what's called the first AI winter..."}
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]
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}
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```
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Step 2: Execute generation:
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```bash
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python /mnt/skills/public/podcast-generation/scripts/generate.py \
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--script-file /mnt/user-data/workspace/ai-history-script.json \
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--output-file /mnt/user-data/outputs/ai-history-podcast.mp3 \
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--transcript-file /mnt/user-data/outputs/ai-history-transcript.md
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```
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This will generate:
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- `ai-history-podcast.mp3`: The audio podcast file
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- `ai-history-transcript.md`: A readable markdown transcript of the podcast
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## Specific Templates
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Read the following template file only when matching the user request.
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- [Tech Explainer](templates/tech-explainer.md) - For converting technical documentation and tutorials
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## Output Format
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The generated podcast follows the "Hello Deer" format:
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- Two hosts: one male, one female
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- Natural conversational dialogue
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- Starts with "Hello Deer" greeting
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- Target duration: approximately 10 minutes
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- Alternating speakers for engaging flow
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## Output Handling
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After generation:
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- Podcasts and transcripts are saved in `/mnt/user-data/outputs/`
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- Share both the podcast MP3 and transcript MD with user using `present_files` tool
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- Provide brief description of the generation result (topic, duration, hosts)
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- Offer to regenerate if adjustments needed
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## Requirements
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The following environment variables must be set:
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- `VOLCENGINE_TTS_APPID`: Volcengine TTS application ID
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- `VOLCENGINE_TTS_ACCESS_TOKEN`: Volcengine TTS access token
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- `VOLCENGINE_TTS_CLUSTER`: Volcengine TTS cluster (optional, defaults to "volcano_tts")
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## Notes
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- **Always execute the full pipeline in one call** - no need to test individual steps or worry about timeouts
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- The script JSON should match the content language (en or zh)
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- Technical content should be simplified for audio accessibility in the script
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- Complex notations (formulas, code) should be translated to plain language in the script
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- Long content may result in longer podcasts
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