diff --git a/knowledge base/ai/agent.md b/knowledge base/ai/agent.md
index f8df09d..0851afd 100644
--- a/knowledge base/ai/agent.md	
+++ b/knowledge base/ai/agent.md	
@@ -1,12 +1,10 @@
 # AI agent
 
-> [!caution]
-> TODO
-
-AI-enabled system or application capable of autonomously performing tasks of various complexity levels on their own,
-possibly **without** the need to stop to ask permission or consent to the user.
+AI-enabled system or application capable of _autonomously_ performing tasks of various complexity levels by designing
+workflows and using the tools made available to them.
 
 1. [TL;DR](#tldr)
+1. [Skills](#skills)
 1. [Concerns](#concerns)
    1. [How much context is too much?](#how-much-context-is-too-much)
    1. [Security](#security)
@@ -17,10 +15,14 @@ possibly **without** the need to stop to ask permission or consent to the user.
 
 ## TL;DR
 
-Agents design their workflow and utilize the tools that are made available to them.<br/>
-They use natural language processing techniques of [LLMs][large language model] to comprehend user inputs, respond to
-them step-by-step, and determine when to call on external tools to obtain up-to-date information, optimize workflows
-and create subtasks autonomously to achieve complex goals.
+AI agents can encompass a wide range of functions beyond natural language processing.<br/>
+These functions include making decision, problem-solving, interacting with external environments, and performing
+actions.
+
+Agents design their own workflow and utilize the tools that are made available to them.<br/>
+They use [LLMs][large language model] to comprehend user inputs, deconstruct and respond to requests step-by-step,
+determine when to call on external tools to obtain up-to-date information, optimize workflows, and autonomously create
+subtasks to achieve complex goals.
 
 Traditional software is _deterministic_, AI is _probabilistic_.
 
@@ -44,6 +46,13 @@ them during the run.
 
 Prefer **requiring** consent by agents when running them.
 
+## Skills
+
+Skills extend AI agent capabilities with specialized knowledge and workflow definitions.
+
+[Agent Skills] is an open standard for skills. It defines them as folders of instructions, scripts, and resources that
+agents can discover and use to do things more accurately and efficiently.
+
 ## Concerns
 
 Agents created by Anthropic and other companies have a history of not caring about agent abuse, and leave users on
@@ -143,6 +152,7 @@ See [An AI Agent Published a Hit Piece on Me] by Scott Shambaugh.
 <!-- Others -->
 [39C3 - Agentic ProbLLMs: Exploiting AI Computer-Use and Coding Agents]: https://www.youtube.com/watch?v=8pbz5y7_WkM
 [39C3 - AI Agent, AI Spy]: https://www.youtube.com/watch?v=0ANECpNdt-4
+[Agent Skills]: https://agentskills.io/
 [Agentic ProbLLMs - The Month of AI Bugs]: https://monthofaibugs.com/
 [AI Doesn't Reduce Work — It Intensifies It]: https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies-it
 [An AI Agent Published a Hit Piece on Me]: https://theshamblog.com/an-ai-agent-published-a-hit-piece-on-me/
diff --git a/knowledge base/ai/claude/claude code.md b/knowledge base/ai/claude/claude code.md
index 6c976db..7c17c47 100644
--- a/knowledge base/ai/claude/claude code.md	
+++ b/knowledge base/ai/claude/claude code.md	
@@ -203,9 +203,13 @@ Manually add the MCP server definition to `$HOME/.claude.json`:
 
 ## Using skills
 
-Refer [Skills][documentation/skills].
+Refer [Skills][documentation/skills].<br/>
+See also:
 
-See also [create custom skills] and [Prat011/awesome-llm-skills].
+- [Create custom skills].
+- [Prat011/awesome-llm-skills].
+
+Claude Skills follow and extend the [Agent Skills] standard format.
 
 Skills superseded commands.<br/>
 Existing `.claude/commands/` files will currently still work, but skills with the same name will take precedence.
@@ -308,6 +312,7 @@ Claude Code version: `v2.1.41`.<br/>
 - [Gemini CLI]
 - [OpenCode]
 - [Prat011/awesome-llm-skills]
+- [Claude Skills vs. MCP: A Technical Comparison for AI Workflows]
 
 ### Sources
 
@@ -337,7 +342,9 @@ Claude Code version: `v2.1.41`.<br/>
 [Website]: https://claude.com/product/overview
 
 <!-- Others -->
+[Agent Skills]: https://agentskills.io/
 [AWS API MCP Server]: https://github.com/awslabs/mcp/tree/main/src/aws-api-mcp-server
+[Claude Skills vs. MCP: A Technical Comparison for AI Workflows]: https://intuitionlabs.ai/articles/claude-skills-vs-mcp
 [Cost Explorer MCP Server]: https://github.com/awslabs/mcp/tree/main/src/cost-explorer-mcp-server
 [pffigueiredo/claude-code-sheet.md]: https://gist.github.com/pffigueiredo/252bac8c731f7e8a2fc268c8a965a963
 [Prat011/awesome-llm-skills]: https://github.com/Prat011/awesome-llm-skills
diff --git a/knowledge base/ai/llama.cpp.md b/knowledge base/ai/llama.cpp.md
index f5f3038..de028e5 100644
--- a/knowledge base/ai/llama.cpp.md	
+++ b/knowledge base/ai/llama.cpp.md	
@@ -41,7 +41,7 @@ llama-cli -m 'path/to/target/model.gguf' -md 'path/to/draft/model.gguf'
 # Download and run models.
 llama-cli -mu 'https://example.org/some/model'  # URL
 llama-cli -hf 'ggml-org/gemma-3-1b-it-GGUF' -c '32.768'  # Hugging Face
-llama-cli -dr 'ai/qwen2.5-coder' --offline  # Docker Hub
+llama-cli -dr 'ai/qwen2.5' --offline  # Docker Hub
 
 # Launch the OpenAI-compatible API server.
 llama-server -m 'path/to/model.gguf'
diff --git a/knowledge base/ai/llm.md b/knowledge base/ai/llm.md
index 3de400f..a6fb8c0 100644
--- a/knowledge base/ai/llm.md	
+++ b/knowledge base/ai/llm.md	
@@ -133,11 +133,17 @@ is correct by breaking questions in smaller, more manageable steps, and solving
 final answer.<br/>
 The result is more accurate, but it costs more tokens and requires a bigger context window.
 
-The _ReAct loop_ (reason+act) forces models to loop over chain of thoughts.<br/>
-A model breaks the request in smaller steps, acts on those using [functions][function calling] if they deem it useful,
-checks the results, updates the chain of thoughts, and repeat until the request is satisfied.
+The _ReAct loop_ (Reason + Act) paradigm forces models to loop over chain-of-thoughts.<br/>
+A model breaks the request in smaller steps, plans the next action, acts on it using [functions][function calling]
+should it decide it needs to, checks the results, updates the chain of thoughts, and repeats this Think-Act-Observe loop
+to iteratively improve upon responses.
 
-Next step is [agentic AI][agent].
+The _ReWOO_ (Reasoning WithOut Observation) method eliminates the dependence on tool outputs for action planning.<br/>
+Models plan upfront, and avoid redundant usage of tools by anticipating which tools to use upon receiving the initial
+prompt from the user.<br/>
+Users can confirm the plan **before** the model executes it.
+
+[AI agents][agent] use these methods to act autonomously.
 
 ## Prompting
 
@@ -195,6 +201,7 @@ Refer:
 ## Further readings
 
 - [SEQUOIA: Serving exact Llama2-70B on an RTX4090 with half-second per token latency]
+- [Optimizing LLMs for Performance and Accuracy with Post-Training Quantization]
 
 ### Sources
 
@@ -203,6 +210,7 @@ Refer:
 - [Local LLM Hosting: Complete 2026 Guide - Ollama, vLLM, LocalAI, Jan, LM Studio & More]
 - [LLM skills every AI engineer must know]
 - [Function calling in LLMs]
+- [What is chain of thought (CoT) prompting?]
 
 <!--
   Reference
@@ -241,5 +249,7 @@ Refer:
 [Looking back at speculative decoding]: https://research.google/blog/looking-back-at-speculative-decoding/
 [Mistral]: https://mistral.ai/
 [OpenClaw: Who are you?]: https://www.youtube.com/watch?v=hoeEclqW8Gs
+[Optimizing LLMs for Performance and Accuracy with Post-Training Quantization]: https://developer.nvidia.com/blog/optimizing-llms-for-performance-and-accuracy-with-post-training-quantization/
 [Run LLMs Locally: 6 Simple Methods]: https://www.datacamp.com/tutorial/run-llms-locally-tutorial
 [SEQUOIA: Serving exact Llama2-70B on an RTX4090 with half-second per token latency]: https://infini-ai-lab.github.io/Sequoia-Page/
+[What is chain of thought (CoT) prompting?]: https://www.ibm.com/think/topics/chain-of-thoughts
diff --git a/knowledge base/ai/vllm.md b/knowledge base/ai/vllm.md
index a981416..f6e4a1d 100644
--- a/knowledge base/ai/vllm.md	
+++ b/knowledge base/ai/vllm.md	
@@ -34,6 +34,7 @@ capabilities, and enterprise-scale LLM serving.
 ```sh
 pip install 'vllm'
 pipx install 'vllm'
+uv tool install 'vllm'  # 'vllm-metal' on apple silicon
 ```
 
 </details>
diff --git a/knowledge base/uv.md b/knowledge base/uv.md
new file mode 100644
index 0000000..bf8b983
--- /dev/null
+++ b/knowledge base/uv.md	
@@ -0,0 +1,77 @@
+# uv
+
+Fast Python package and project manager written in Rust.
+
+1. [TL;DR](#tldr)
+1. [Further readings](#further-readings)
+   1. [Sources](#sources)
+
+## TL;DR
+
+<details>
+  <summary>Setup</summary>
+
+```sh
+brew install 'uv'
+docker pull 'ghcr.io/astral-sh/uv:0.10.4-python3.12-trixie'
+pip install 'uv'
+pipx install 'uv'
+```
+
+</details>
+
+<details>
+  <summary>Usage</summary>
+
+```sh
+# Install applications.
+# Similar to `pipx install`.
+uv tool install 'ansible'
+
+# List installed applications.
+uv tool list
+
+# Run applications.
+# Similar to `pipx run`.
+uv tool run 'vllm'
+uvx 'vllm'          # alias for `uv tool run`
+
+# Clear the cache.
+uv cache clean
+```
+
+</details>
+
+<!-- Uncomment if used
+<details>
+  <summary>Real world use cases</summary>
+
+```sh
+```
+
+</details>
+-->
+
+## Further readings
+
+- [Website]
+- [Codebase]
+
+### Sources
+
+- [Documentation]
+
+<!--
+  Reference
+  ═╬═Time══
+  -->
+
+<!-- In-article sections -->
+<!-- Knowledge base -->
+<!-- Files -->
+<!-- Upstream -->
+[Codebase]: https://github.com/astral-sh/uv
+[Documentation]: https://docs.astral.sh/uv/
+[Website]: https://docs.astral.sh/uv/
+
+<!-- Others -->