Agent skill

URL Dump

by huytieu in huytieu/COG-second-brain

Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets

MITAuto-check passedKnowledge Management

Install URL Dump

skills CLI
$ npx skills add huytieu/COG-second-brain --skill url-dump -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install huytieu/COG-second-brain url-dump --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/huytieu/COG-second-brain.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/url-dump .claude/skills/url-dump && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
url-dump
GitHub stars
1.3k
Token cost
~3.6k tokens
SKILL.md length
1,166 words
Files
1
Skills in repo
37
Repo updated
First seen
Licence
MIT

At a glance

Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets

  • Works in 8 steps: User Interaction & Input Collection → URL Validation & Fetch → Category Selection → …
  • Knowledge Management work in your project
  • SKILL.md covers Purpose, When to Invoke, Agent Mode Awareness and Pre-Flight Check, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

URL Dump is an agent skill from huytieu/COG-second-brain. Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management. The repository describes itself as: Self-evolving second brain with 35 AI skills, 10 agents, and people CRM. Closed-loop harness: a V-model verification lifecycle where the worker never grades its own homework… The licence is MIT.

When your agent uses it

  • Knowledge Management work in your project

Example prompts

  • “/url-dump”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. User Interaction & Input Collection
  2. URL Validation & Fetch
  3. Category Selection
  4. Content Analysis and Processing
  5. Generate Structured Output
  6. Tool/Resource Special Handling
  7. Batch Processing
  8. Confirm Completion

What it can do on your machine

Read from SKILL.md and the folder at commit 36ac9d7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

URL Dump loads about 3.6k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 1,166 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from huytieu/COG-second-brain at commit 36ac9d7, republished under its MIT licence (© huytieu). 1,166 words, ~3,572 tokens.

Download SKILL.mdSave it as .claude/skills/url-dump/SKILL.md (or your agent's skills folder).
name
url-dump
description
Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets
roles
all
integrations
web-fetch

COG URL Dump Skill

Purpose

Transform raw URLs into structured, insightful knowledge entries through intelligent content extraction, categorization, and integration with the user's knowledge base. Quick capture with automatic insight generation.

When to Invoke

  • User shares a URL they want to save
  • User says "save this link", "bookmark this", "url dump", or "save for later"
  • User pastes a URL and wants to capture it
  • User wants to organize web resources into their knowledge base

Agent Mode Awareness

Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:

  • If agent_mode: team — delegate content extraction, analysis, and categorization to a sub-agent while handling user interaction directly. The sub-agent fetches URL content, generates insights, and returns structured results for filing.
  • If agent_mode: solo (default) — handle everything directly in the conversation. No delegation.

Pre-Flight Check

Before executing, check for user profile:

  1. Look for 00-inbox/MY-PROFILE.md in the vault
  2. If NOT found:
    Welcome to COG! It looks like this is your first time.
    
    Before we start, let's quickly set up your profile (takes 2 minutes).
    
    Would you like to run onboarding first, or should I proceed with default settings?
  3. If found:
    • Read the profile to get user's interests and projects
    • Use interests to help with auto-categorization
    • Check for existing booklet categories in 05-knowledge/booklets/

Process Flow

1. User Interaction & Input Collection
  • Accept URL(s) from the user (single URL or batch)
  • Optionally accept user's quick note about why they're saving this
  • Accept any format: bare URL, markdown link, or with notes

Prompt:

What URL(s) would you like to save?
(You can paste one or more URLs, optionally with a note about why you're saving it)
2. URL Validation & Fetch
  • Validate URL format
  • Check if URL is accessible
  • Detect duplicate URLs in existing knowledge base
  • Fetch the web page content
Content Extraction

Extract from the page:

  • Page Title: [extracted-title]
  • Meta Description: [if available]
  • Author: [if detected]
  • Published Date: [if detected]
  • Word Count: [estimated]
  • Read Time: [X minutes]
  • Main Content: [extracted body text]
  • Key Headings: [list of H1/H2s]
3. Category Selection

Default Categories:

  • Articles & Blogs: Long-form content, tutorials, opinion pieces
  • Tools & Resources: Software, utilities, services, APIs
  • Reference: Documentation, specs, standards
  • Research: Papers, studies, academic content
  • Inspiration: Design, ideas, creative references
  • Videos & Media: YouTube, podcasts, multimedia
  • News & Updates: Industry news, announcements
  • Project-Specific: Related to a specific project (offer project list from MY-PROFILE.md)
  • To Review: Unsure, save for later categorization

Custom Categories:

  • Check 05-knowledge/booklets/ for existing custom categories
  • Offer to create new category if needed

Auto-suggestion: Based on content analysis, suggest the most likely category but let user confirm or change.

4. Content Analysis and Processing
Phase 1: Content Classification

Determine:

  • Content Category: [article|tool|reference|research|video|news|etc]
  • Primary Topics: [topic1, topic2, topic3]
  • Tone: [informative|opinion|tutorial|news|etc]
  • Quality Assessment: [high|medium|low]
  • Credibility Indicators: [author credentials, citations, etc]
Phase 2: Insight Extraction

Generate:

  • Executive Summary: [2-3 sentences]
  • Key Insights:
    1. [Insight 1 with context]
    2. [Insight 2 with context]
    3. [Insight 3 with context]
  • Notable Quotes: [if any stand out]
  • Action Items: [practical takeaways]
Phase 3: Relevance Assessment

Analyze:

  • User Interest Match: [high|medium|low] - [which interests from profile]
  • Project Relevance: [project-name] - [why relevant]
  • Knowledge Gap: [yes|no] - [what gap it fills]
  • Timeliness: [evergreen|current|dated]
  • Uniqueness: [novel|common|duplicate-adjacent]
Phase 4: Cross-Reference

Identify connections to:

  • Related Bookmarks: [existing similar saves]
  • Related Braindumps: [if content connects]
  • Related Projects: [if applicable]
  • Suggested Tags: [tag1, tag2, tag3]
5. Generate Structured Output

Create bookmark file with this structure:

markdown
---
type: "url-bookmark"
category: "[category-name]"
domain: "[source-domain.com]"
date_saved: "YYYY-MM-DD"
date_accessed: "YYYY-MM-DD HH:MM"
url: "[original-url]"
title: "[page-title]"
author: "[author-if-available]"
published: "[publish-date-if-available]"
tags: ["#bookmark", "#category-tag", "#topic-tags"]
relevance: "[high|medium|low]"
status: "unread"
related_projects: ["project1", "project2"]
confidence: "[high|medium|low]"
---

# [Title]

## Quick Summary
[2-3 sentence summary of the content]

## Key Insights
- **Insight 1:** [description with context]
- **Insight 2:** [description with context]
- **Insight 3:** [description with context]

## Why This Matters
[Connection to user's interests/projects. What makes this worth saving?]

## User Note
[Original user note if provided, otherwise omit section]

## Content Highlights
[Key excerpts or quotes from the content - 200-400 words max]

## Practical Takeaways
- [ ] [Action item 1 if applicable] 📅 [YYYY-MM-DD = date +1 week from today]
- [ ] [Action item 2 if applicable] 📅 [YYYY-MM-DD = date +1 week from today]

## Related Knowledge
- **Similar Bookmarks:** [[bookmark1]], [[bookmark2]]
- **Connected Projects:** [[project1]]
- **Related Notes:** [[note1]], [[note2]]

## Source Details
| Field | Value |
|-------|-------|
| Domain | [domain] |
| Author | [author or "Unknown"] |
| Published | [date or "Unknown"] |
| Word Count | [~X words] |
| Read Time | [~X minutes] |

## Processing Notes
- **Extracted:** [timestamp]
- **Category Confidence:** [percentage]
- **Review Needed:** [yes|no] - [reason if yes]

---

*Processed by COG URL Curator*

Save to appropriate location:

  • Standard: 05-knowledge/booklets/[category-slug]/[title-slug]-YYYY-MM-DD.md
  • Project-specific: 04-projects/[project-slug]/resources/[title-slug]-YYYY-MM-DD.md
  • Mixed/Unclear: 00-inbox/url-[title-slug]-YYYY-MM-DD.md
6. Tool/Resource Special Handling

For tools and software, use enhanced template:

markdown
---
type: "url-tool"
category: "tools"
domain: "[domain]"
url: "[url]"
title: "[tool-name]"
date_saved: "YYYY-MM-DD"
pricing: "[free|freemium|paid|enterprise]"
tags: ["#tool", "#category-tags"]
status: "to-evaluate"
---

# [Tool Name]

## What It Does
[1-2 sentence description]

## Key Features
- Feature 1
- Feature 2
- Feature 3

## Use Cases
- Use case 1
- Use case 2

## Pricing
[Pricing details if available]

## Why It's Relevant
[Connection to user's work/interests]

## Evaluation Status
- [ ] Sign up / try demo 📅 [YYYY-MM-DD = date +3 days from today]
- [ ] Test key features 📅 [YYYY-MM-DD = date +1 week from today]
- [ ] Compare with alternatives 📅 [YYYY-MM-DD = date +1 week from today]
- [ ] Decision: [use|pass|revisit] 📅 [YYYY-MM-DD = date +2 weeks from today]

## Notes
[Space for user's evaluation notes]

---

*Processed by COG URL Curator*
7. Batch Processing

For multiple URLs:

Processing [X] URLs...

1. [URL 1] → [category] → Saved to [path]
2. [URL 2] → [category] → Saved to [path]
3. [URL 3] → [category] → Saved to [path]

Summary:
- Articles: 2 saved
- Tools: 1 saved
- Total: 3 URLs processed
8. Confirm Completion
  • Confirm file(s) created
  • Show user: "URL saved to [file path]"
  • Show quick summary: title, category, key insight preview
  • Ask if they want to:
    • Add another URL
    • Deep-dive into the content
    • Connect to specific project or braindump

Booklet Structure

URLs are organized into "booklets" (category folders):

05-knowledge/
└── booklets/
    ├── articles/
    │   ├── _index.md (category overview - auto-created)
    │   └── [article-entries].md
    ├── tools/
    │   ├── _index.md
    │   └── [tool-entries].md
    ├── reference/
    │   ├── _index.md
    │   └── [reference-entries].md
    ├── research/
    │   ├── _index.md
    │   └── [research-entries].md
    ├── inspiration/
    │   ├── _index.md
    │   └── [inspiration-entries].md
    ├── videos/
    │   ├── _index.md
    │   └── [video-entries].md
    └── [custom-category]/
        ├── _index.md
        └── [entries].md
Category Index Template

When creating a new category, also create an index file:

markdown
---
type: "booklet-index"
category: "[category-name]"
created: "YYYY-MM-DD"
last_updated: "YYYY-MM-DD"
entry_count: 0
---

# [Category Name] Booklet

## Description
[What this category contains]

## Recent Additions
[Auto-updated list - most recent 10 entries]

## Top Entries
[Manually curated or most-accessed entries]

## Tags in This Category
[List of common tags used]

## Related Categories
- [[other-category-1]]
- [[other-category-2]]

YAML Formatting Requirements

CRITICAL: All YAML frontmatter must use proper Obsidian-compatible formatting:

  • All string values MUST be quoted with double quotes
  • Arrays MUST use quoted strings: ["item1", "item2", "item3"]
  • URLs MUST be quoted to handle special characters
  • Boolean values should NOT be quoted: true or false
  • Ensure proper YAML syntax to prevent parsing errors in Obsidian

Examples:

yaml
# CORRECT
type: "url-bookmark"
url: "https://example.com/path?query=value"
tags: ["#bookmark", "#article", "#ai"]
relevance: "high"
reviewed: false

# INCORRECT
type: url-bookmark
url: https://example.com/path?query=value
tags: [#bookmark, #article, #ai]
relevance: high
reviewed: "false"

Verification Protocols

Content Accuracy
  • Title Verification: Ensure extracted title matches page
  • Author Attribution: Verify author if stated
  • Date Accuracy: Confirm publication date if shown
  • Summary Fidelity: Ensure summary accurately represents content
Categorization Verification
  • Category Fit: Confirm content matches selected category
  • Tag Relevance: Verify tags accurately describe content
  • Interest Alignment: Confirm relevance assessment is accurate
  • Project Connection: Verify project relevance if claimed
Show full SKILL.md (474 more words)Show less
Quality Checks
  • Completeness: All required fields populated
  • Formatting: Proper markdown and YAML syntax
  • Links: All internal links valid
  • Metadata: Frontmatter properly formatted

Uncertainty Handling

When Content is Unclear
  • Paywalled Content: Note limitation, extract available preview
  • Dynamic Content: Note if content may change
  • Complex Content: Flag for manual review if needed
  • Non-English: Note language, provide translation if possible
Confidence Indicators
  • High Confidence (90%+): Clear content with obvious categorization
  • Medium Confidence (70-89%): Generally clear with some ambiguity
  • Low Confidence (50-69%): Significant ambiguity requiring user input
  • Very Low Confidence (<50%): Major uncertainty, save to inbox

Always explicitly state confidence levels and reasoning in processing notes.

Loop Engineering

URL capture is a fetch-retry loop with a quality gate, not a single fetch-and-file. See .claude/skills/loop-engineering/SKILL.md for the shared vocabulary.

The loop (per URL): fetch → if the fetch fails or returns an empty/blocked body, retry a different way (https vs http, reader mode, an archive snapshot) → once content is present, run the quality gate → file it, or escalate to the user / save to inbox with a Review Needed flag.

The verifier (deterministic):

  • Fetch returned a non-empty body (not a paywall stub or error page).
  • Required fields are populated: title, at least one key insight, a category.
  • YAML frontmatter is valid (see YAML Formatting Requirements).
  • Category confidence clears the threshold. Below ~70%, the loop does not silently guess.

Termination conditions (layered):

  • Goal met: content extracted and the quality gate passes → save to the category folder.
  • Retry cap: stop after ~3 fetch attempts → save what was extracted with a low-confidence flag (see Uncertainty Handling), do not invent missing fields.
  • Hard stop on paywall / login wall: note the limitation, capture the available preview, do not loop forever.
  • Human escalation: confidence below threshold → present the best guess and ask the user to confirm category, rather than filing it wrong.

Patterns: reflect-retry (each failed fetch picks a different method) + evaluator (the quality gate) + human-in-the-loop (low-confidence escalation).

In-loop context: once insights and metadata are extracted, drop the raw page body. For batch input, process each URL as its own independent loop so one bad URL never stalls the rest.

Integration with Other Skills

Immediate Follow-up

After URL capture, suggest:

  • /braindump - Capture thoughts about the URL
  • /knowledge-consolidation - Integrate into knowledge frameworks
  • Daily brief will surface relevant saved URLs
Cross-Referencing

Automatically check for connections to:

  • Active projects (from MY-PROFILE.md)
  • Recent braindumps
  • Competitive watchlist companies (if exists)
  • User interests

Success Metrics

  • Speed of capture (< 30 seconds for single URL)
  • Accurate categorization with user confirmation
  • Useful insight extraction
  • Proper integration with existing knowledge
  • Easy retrieval and discovery later
  • High confidence in extractions

Learning and Adaptation

Pattern Learning
  • Track which bookmarks get revisited
  • Learn user's categorization preferences
  • Improve relevance scoring based on engagement
  • Refine insight extraction based on what user finds useful
Continuous Improvement
  • Monitor categorization accuracy over time
  • Adapt to user's preferred tag taxonomy
  • Learn domain-specific terminology
  • Improve cross-referencing accuracy

© huytieu, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/url-dump of huytieu/COG-second-brain.

Open the folder on GitHubat commit 36ac9d7

Compare with similar skills

URL Dump next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

URL Dump compared with similar skills
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Baoyu URL To Markdownsdyckjq-lab/llm-wiki-skill2.5k2 repos~3.2kAutomated safety check: PassNone
Obsidian CLIAtmosphere/atmosphere3.8k13 repos~795Automated safety check: PassApache-2.0
Esm Cjs Risk Scanlogseq/logseq45k—~3.3kAutomated safety check: PassAGPL-3.0
Capture Conversationoutline/outline41k—~474Automated safety check: PassCustom licence

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Questions about URL Dump

What does URL Dump do?

Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets. URL Dump is an agent skill from huytieu/COG-second-brain.

When should I use URL Dump?

URL Dump fits situations like: knowledge Management work in your project.

How do I install URL Dump in Claude Code?

Run `npx skills add huytieu/COG-second-brain --skill url-dump -a claude-code`. Or copy the skill folder (skills/url-dump in huytieu/COG-second-brain) into .claude/skills/url-dump in your project. Claude Code loads it when a task matches its description.

How do I install URL Dump in Codex?

Run `npx skills add huytieu/COG-second-brain --skill url-dump -a codex`. Or copy the skill folder (skills/url-dump in huytieu/COG-second-brain) into .agents/skills/url-dump in your project. Codex loads it when a task matches its description.

Can I use URL Dump in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add huytieu/COG-second-brain --skill url-dump -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/url-dump, .gemini/skills/url-dump, .github/skills/url-dump and .opencode/skills/url-dump in your project.

What does URL Dump need to run?

SKILL.md names no scripts, command-line tools or credentials: URL Dump is instructions for the agent only.

Does URL Dump access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is URL Dump safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does URL Dump use?

URL Dump is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does URL Dump use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to URL Dump?

Skills that share tags, products or a category with URL Dump: Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars), Obsidian CLI (Atmosphere/atmosphere, 3.8k stars) and Esm Cjs Risk Scan (logseq/logseq, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains URL Dump?

huytieu (a GitHub user) maintains it in huytieu/COG-second-brain, which has 1,267 GitHub stars. The repository holds 37 skills in this directory. The repository was last updated on October 2, 2026.

Source: huytieu/COG-second-brain on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.