Automated summary
Performs deep research on a technical topic by exploring the web, extracting key concepts, and identifying diagram opportunities for blog writing.
Strengths
- Structured research process with multiple web searches and deep reading
- Organized extraction of concepts, historical context, and comparisons
- Systematic identification of diagram opportunities to enhance understanding
- Generates comprehensive research notes ready for blog post creation
Limitations
- Requires reliable web access and may encounter fetch limits
- Quality of output heavily depends on availability of good sources
- Not suitable for non-technical topics or superficial research
Best when preparing a detailed technical blog post that requires deep understanding and visual aids.
Avoid for quick fact-checking or simple definitions, or when the topic is non-technical.
Security analysis
SafeThis skill uses web search and content extraction for research purposes only, with no instructions to execute code, access sensitive data, or perform destructive actions.
No concerns found
How this analysis is madeExamples
Research the Multi-head Latent Attention mechanism from DeepSeek for a blog post. I need a deep dive with diagrams.Look into the GRPO algorithm used by DeepSeek-R1. I want to understand how it works and compare to PPO.Explore speculative decoding techniques for LLM inference. I want to write a technical blog post about it.name: research-topic description: "Deep research on a technical topic for blog writing. Use when user says research, look into, explore, or provides a blog topic to investigate. Searches the web extensively and creates structured research notes with visual opportunity identification." allowed-tools: Bash, Read, Grep, Glob, WebFetch
Deep Research Skill
Input
$ARGUMENTS = the topic to research
Process
Step 1: Broad Search (5 to 8 searches)
Search the web for high-quality sources on the topic:
- Original research papers (arXiv, conference proceedings)
- Official documentation and blog posts from the creators
- Well-written technical blog posts (Lilian Weng, Jay Alammar, etc.)
- Video transcripts or lecture notes if available
- GitHub implementations for reference
Step 2: Deep Read
For each promising source, use WebFetch to read the full content. Extract and organize:
Core Concepts
- What is this? (one-paragraph definition)
- Why does it exist? What problem does it solve?
- What did it replace or improve upon?
How It Works (Technical Depth)
- Step-by-step mechanism
- Key equations and their intuition
- Concrete numerical examples (shapes, dimensions, values)
- Implementation details
Comparisons and Alternatives
- How does this compare to previous approaches?
- What are the trade-offs?
- Quantitative comparisons (benchmarks, memory savings, speedups)
Historical Context
- When was it introduced? By whom?
- What papers are most relevant?
- How has it evolved since introduction?
Step 3: Identify Visual Opportunities
This is critical. For EVERY concept, ask: "Would a diagram help here?" List 6 to 10 concepts that NEED visual diagrams:
- Architecture overviews
- Data flow through components
- Step-by-step process walkthroughs
- Before/after comparisons
- Matrix operations with concrete shapes
- Mathematical derivation steps
For each, write:
- Diagram name (e.g., "fig_mla_architecture")
- What it should show
- Type: architecture / flowchart / comparison / step-by-step / matrix-operation
Step 4: Save Research Notes
Save to: research/<topic-slug>.md
Structure:
# Research: <Topic Name>
## Quick Summary
(2-3 sentence overview)
## Core Concepts
(detailed notes)
## How It Works
(step-by-step technical breakdown)
## Mathematical Foundation
(key equations with explanations)
## Comparisons and Alternatives
(vs previous approaches, with numbers)
## Visual Opportunities
(list of 6-10 diagrams needed with descriptions)
## Running Example
(define the simple example we will use throughout:
e.g., 4 tokens, specific dimensions, concrete values)
## Key Sources
- [Paper Name](url) - what we extracted from it
- [Blog Post](url) - what we extracted from it
Output
Save to research/<topic-slug>.md and summarize key findings to user. Tell the user how many diagram opportunities were identified.
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/deep-technical-research-mm5t8a94/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/deep-technical-research-mm5t8a94' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/deep-technical-research-mm5t8a94/download' --output '.skill-downloads/deep-technical-research-mm5t8a94/SKILL.md.part' && mv '.skill-downloads/deep-technical-research-mm5t8a94/SKILL.md.part' '.skill-downloads/deep-technical-research-mm5t8a94/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Tools declared by the author (check availability in your agent): Bash, Read, Grep, Glob, WebFetch
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/deep-technical-research-mm5t8a94/SKILL.md' && test -s '.skill-downloads/deep-technical-research-mm5t8a94/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/research-topic' && cp -R '.skill-downloads/deep-technical-research-mm5t8a94' '.claude/skills/research-topic'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/deep-technical-research-mm5t8a94'openskills read 'deep-technical-research-mm5t8a94'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/deep-technical-research-mm5t8a94/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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