The Claude skill functionality allows users to summarize or extract text from various sources including URLs, local files, and podcasts. It employs a decision tree to parse user input and apply the appropriate processing methods based on content type and user-defined flags. The skill integrates with various API services to enhance its capabilities, though potential issues might arise if the necessary API keys are missing.
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# summarize - Claude Skill Details | SkillHub
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# summarize
by openclaw 342.7k 15.6k[GitHub](https://github.com/openclaw/openclaw#skills-summarize)
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
### User Request
input Node
User provides input (URL, file path, or YouTube link) with optional flags
### Parse Input & Flags
processing Node
Extract source type, determine if --extract-only, --youtube, --json, --length flags are set
### Source Type?
decision Node
Determine whether input is URL, local file, or YouTube link
### Load Config
config Node
Read ~/.summarize/config.json for default model and settings
### API Keys Present?
decision Node
Check for required API keys based on selected model provider
### Process Local File
processing Node
Read and extract text from PDF, TXT, DOCX, etc. using local parsing
### Fetch URL Content
processing Node
Download and extract main content from webpage using built-in extractor
### Firecrawl Needed?
decision Node
Evaluate if site is blocked/JS-heavy requiring Firecrawl fallback
### Use Firecrawl
processing Node
Call Firecrawl API with FIRECRAWL_API_KEY for complex sites
### YouTube Flag Set?
decision Node
Check if --youtube auto flag is present for YouTube URLs
### Extract Only?
decision Node
Check if --extract-only flag is set for transcript extraction
### Use Apify Fallback
processing Node
Use Apify API with APIFY_API_TOKEN for YouTube transcript extraction
### Built-in YouTube
processing Node
Use built-in YouTube extraction without external APIs
### Select AI Model
config Node
Choose model based on config, flags, or default (gemini-3-flash-preview)
### Call LLM API
processing Node
Send extracted content to selected AI model with length/token constraints
### Format Output
processing Node
Apply --json flag formatting or pretty markdown based on flags
### Return Summary
output Node
Deliver final summary/transcript to user
### Return Transcript
output Node
Deliver extracted transcript (no summarization)
### API Error
output Node
Return error message about missing API keys
[React Flow](https://reactflow.dev/)
Press enter or space to select a node.You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.
Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.
Live Logic Circuit
### Output Preview
Copy Output
output_preview.md
```
# Summary: "The Future of AI Agents" (YouTube Video)
## 📊 Key Insights
- **Agentic workflows** are shifting from single-prompt interactions to multi-step reasoning with tool use
- **Three emerging patterns**: Reflection (self-critique), Tool use (API calls), and Multi-agent collaboration
- **Critical challenge**: Maintaining consistency across long reasoning chains ("hallucination drift")
## 🎯 Core Argument
The next breakthrough won't be larger models, but better **orchestration systems** that can:
1. Decompose complex problems into sub-tasks
2. Validate intermediate results
3. Recover from errors autonomously
4. Integrate specialized tools (calculators, code executors, web search)
## 🔧 Technical Implementation
```python
# Example agentic loop pattern
for attempt in range(max_retries):
plan = llm.generate_plan(task)
for step in plan:
result = execute_tool(step)
validation = llm.validate(result, step)
if not validation.passed:
llm.repair(step, result) # Self-correction
```
## ⚠️ Limitations & Risks
* **Cost scaling**: Each reflection/tool-use cycle adds latency and API costs
* **Over-reliance**: Agents may get stuck in infinite correction loops
* **Security**: Tool access requires careful sandboxing
## 🚀 Practical Recommendation
Start with **single-agent, single-tool** workflows before scaling to multi-agent systems. The `summarize` tool itself demonstrates effective tool use by combining content extraction with LLM summarization.
_Duration: 24:18 | Transcript available with `--extract-only`_
10/10 Security
Low security risk, safe to use
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SKILL.md Evaluation Usage Guide
SKILL.md
# Summarize
Fast CLI to summarize URLs, local files, and YouTube links.
## When to use (trigger phrases)
Use this skill immediately when the user asks any of:
* “use summarize.sh”
* “what’s this link/video about?”
* “summarize this URL/article”
* “transcribe this YouTube/video” (best-effort transcript extraction; no `yt-dlp` needed)
## Quick start
```bash
summarize "https://example.com" --model google/gemini-3-flash-preview
summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview
summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto
```
## YouTube: summary vs transcript
Best-effort transcript (URLs only):
```bash
summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto --extract-only
```
If the user asked for a transcript but it’s huge, return a tight summary first, then ask which section/time range to expand.
## Model + keys
Set the API key for your chosen provider:
* OpenAI: `OPENAI_API_KEY`
* Anthropic: `ANTHROPIC_API_KEY`
* xAI: `XAI_API_KEY`
* Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`)
Default model is `google/gemini-3-flash-preview` if none is set.
## Useful flags
* `--length short|medium|long|xl|xxl|<chars>`
* `--max-output-tokens <count>`
* `--extract-only` (URLs only)
* `--json` (machine readable)
* `--firecrawl auto|off|always` (fallback extraction)
* `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set)
## Config
Optional config file: `~/.summarize/config.json`
```json
{ "model": "openai/gpt-5.2" }
```
Optional services:
* `FIRECRAWL_API_KEY` for blocked sites
* `APIFY_API_TOKEN` for YouTube fallback
Source: [https://github.com/openclaw/openclaw#skills-summarize](https://github.com/openclaw/openclaw#skills-summarize)
Content curated from original sources, copyright belongs to authors
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Best Practices:A
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Claude

Codex

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Roo
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Kiro
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Junie
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