Add Skill: graph-based-node-orchestration

Extracted from: https://github.com/temiroff/Blacknode.git
Score: 1.0

skills/autonomous-web-research-agent/SKILL.md

@@ -1,77 +0,0 @@
----
-name: autonomous-web-research-agent
-version: 1.0.0
-description: Autonomously research a given query on the web using multiple search
-  tools and generate a structured report with summary, detailed sections, source tracking,
-  and bias analysis.
-inputs:
-- 'query (string): the research question or topic to investigate'
-- 'tools (list, optional): selected web search/tools to use (e.g., Tavily, Google,
-  NewsAPI, DuckDuckGo)'
-- 'api_keys (dict, optional): credentials for LLM and external search APIs'
-- 'model_config (dict, optional): LLM provider and parameters'
-steps:
-- 1. Accept user query and optional tool selections.
-- '2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search
-  (Tavily, Google, DuckDuckGo), news API, web scraping.'
-- 3. Decompose query into sub-questions if needed and iteratively call tools to gather
-  relevant information.
-- 4. Extract and deduplicate content from retrieved sources, tracking source metadata
-  (URL, tool used).
-- 5. Use a large language model to synthesize findings into an executive summary and
-  detailed sections.
-- 6. Analyze potential biases or limitations of gathered sources.
-- 7. Compile a structured report object (ResearchReport) containing query, summary,
-  sections, sources, biases.
-- 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
-outputs:
-- 'ResearchReport (JSON/dict) with fields: query (string), summary (string), sections
-  (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases
-  (string)'
-- Optional UI rendering of report with source badges and expandable sections
-tags: []
-metadata:
-  source_repo: https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# autonomous-web-research-agent
-
-Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.
-
-## Steps
-
-1. 1. Accept user query and optional tool selections.
-2. 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.
-3. 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
-4. 4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).
-5. 5. Use a large language model to synthesize findings into an executive summary and detailed sections.
-6. 6. Analyze potential biases or limitations of gathered sources.
-7. 7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.
-8. 8. Optionally present report via a UI (e.g., Streamlit) or return as JSON.
-
-## Inputs
-
-- query (string): the research question or topic to investigate
-- tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)
-- api_keys (dict, optional): credentials for LLM and external search APIs
-- model_config (dict, optional): LLM provider and parameters
-
-## Outputs
-
-- ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)
-- Optional UI rendering of report with source badges and expandable sections
-
-## Failure Modes
-
-- Missing or invalid API keys causing tool authentication failures
-- Rate limits or network errors from search APIs
-- Insufficient or low-quality search results leading to incomplete report
-- LLM hallucination or mis-summarization despite source tracking
-- Parsing errors in HTML/scraped content
-
-## Source
-
-Extracted from: [https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git](https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git)
-Confidence: 0.85

skills/autonomous-web-research-agent/commands.md

@@ -1,6 +0,0 @@
-# Commands: autonomous-web-research-agent
-
-## Available Commands
-
-- `/skill autonomous-web-research-agent` — Load this skill
-- `/run autonomous-web-research-agent` — Execute workflow

skills/autonomous-web-research-agent/examples.md

@@ -1,10 +0,0 @@
-# Examples: autonomous-web-research-agent
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: query (string): the research question or topic to investigate, tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo), api_keys (dict, optional): credentials for LLM and external search APIs, model_config (dict, optional): LLM provider and parameters
-# Process: 1. Accept user query and optional tool selections. → 2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping. → 3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.
-# Outputs: ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string), Optional UI rendering of report with source badges and expandable sections
-```

skills/autonomous-web-research-agent/metadata.json

@@ -1,36 +0,0 @@
-{
-  "name": "autonomous-web-research-agent",
-  "version": "1.0.0",
-  "goal": "Autonomously research a given query on the web using multiple search tools and generate a structured report with summary, detailed sections, source tracking, and bias analysis.",
-  "inputs": [
-    "query (string): the research question or topic to investigate",
-    "tools (list, optional): selected web search/tools to use (e.g., Tavily, Google, NewsAPI, DuckDuckGo)",
-    "api_keys (dict, optional): credentials for LLM and external search APIs",
-    "model_config (dict, optional): LLM provider and parameters"
-  ],
-  "steps": [
-    "1. Accept user query and optional tool selections.",
-    "2. Initialize agent framework (e.g., LangGraph) with integrated tools: web search (Tavily, Google, DuckDuckGo), news API, web scraping.",
-    "3. Decompose query into sub-questions if needed and iteratively call tools to gather relevant information.",
-    "4. Extract and deduplicate content from retrieved sources, tracking source metadata (URL, tool used).",
-    "5. Use a large language model to synthesize findings into an executive summary and detailed sections.",
-    "6. Analyze potential biases or limitations of gathered sources.",
-    "7. Compile a structured report object (ResearchReport) containing query, summary, sections, sources, biases.",
-    "8. Optionally present report via a UI (e.g., Streamlit) or return as JSON."
-  ],
-  "outputs": [
-    "ResearchReport (JSON/dict) with fields: query (string), summary (string), sections (list of {heading, content}), sources (list of {url, tool_used, title}), potential_biases (string)",
-    "Optional UI rendering of report with source badges and expandable sections"
-  ],
-  "failure_modes": [
-    "Missing or invalid API keys causing tool authentication failures",
-    "Rate limits or network errors from search APIs",
-    "Insufficient or low-quality search results leading to incomplete report",
-    "LLM hallucination or mis-summarization despite source tracking",
-    "Parsing errors in HTML/scraped content"
-  ],
-  "confidence": 0.85,
-  "explanation": "The repository implements a generic autonomous web research agent that can be reused for any topical query. The workflow of querying, multi-tool retrieval, synthesis, and structured reporting is not domain-specific and can be extracted as a reusable skill.",
-  "source_repo": "https://github.com/DennisDRX/Faraday-Web-Researcher-Agent.git",
-  "score": 1.0
-}

skills/graph-based-node-orchestration/SKILL.md

@@ -0,0 +1,108 @@
+---
+name: graph-based-node-orchestration
+version: 1.0.0
+description: Build a typed node graph that processes data through a sequence of operations
+  and produces a final result
+inputs:
+- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
+- blacknode package with Graph, Node, and cook functionality
+- Python script defining node types with inputs/outputs and connecting them via edges
+steps:
+- Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available
+  via require_nim_api_key()
+- Create a bn.Graph() instance to hold all nodes and their connections
+- Define individual nodes with specific input/output parameters (e.g., Text node with
+  'value' input, LLMAgent node with model parameter, Output node with 'value' output)
+- Connect nodes together using edge definitions (from_port -> to_port) to establish
+  data flow
+- Execute the graph using g.cook() to run the pipeline and process data through the
+  node chain
+- Extract the final result from the output node to complete the workflow
+outputs:
+- A fully constructed graph with typed nodes and defined connections
+- Executed result (e.g., processed text, summary, or other output) from the final
+  node
+- A reusable pattern that can be adapted to different models, hardware, or task types
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# graph-based-node-orchestration
+
+Build a typed node graph that processes data through a sequence of operations and produces a final result
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install blacknode >= 0.3.0 Python >= 3.11 NVIDIA NIM API key (optional but recommended) Anthropic, OpenAI, or other LLM models
+```
+
+**Setup steps:**
+
+1. Clone the repository and install dependencies: pip install -e .
+1. Set NVIDIA_API_KEY or other required API keys in .env
+1. Run the example script: python examples/hello_agent.py
+1. For production, configure hardware pairing and deploy via the blacknode CLI
+
+## Key Files
+
+- `examples/converted_nvidia_nim.py - Full graph with Model, Text, LLMAgent, Output nodes`
+- `examples/hello_agent.py - Minimal agent example connecting Literal → LLMAgent → Print`
+- `blacknode/core - Graph and node implementation (internal)`
+
+## Steps
+
+1. Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()
+2. Create a bn.Graph() instance to hold all nodes and their connections
+3. Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
+4. Connect nodes together using edge definitions (from_port -> to_port) to establish data flow
+5. Execute the graph using g.cook() to run the pipeline and process data through the node chain
+6. Extract the final result from the output node to complete the workflow
+
+## Implementation Details
+
+```python
+g = bn.Graph()
+```
+
+```python
+model = g.node('Model', **{'value': 'nim:meta/llama-3.1-8b-instruct'})
+```
+
+```python
+agent = g.node('LLMAgent', **{model})
+```
+
+```python
+result = g.cook(output, 'value')
+```
+
+## Inputs
+
+- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
+- blacknode package with Graph, Node, and cook functionality
+- Python script defining node types with inputs/outputs and connecting them via edges
+
+## Outputs
+
+- A fully constructed graph with typed nodes and defined connections
+- Executed result (e.g., processed text, summary, or other output) from the final node
+- A reusable pattern that can be adapted to different models, hardware, or task types
+
+## Failure Modes
+
+- Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail
+- Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow
+- Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail
+- Graph execution error due to incorrect edge configuration or circular dependencies
+- Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95

skills/graph-based-node-orchestration/commands.md

@@ -0,0 +1,6 @@
+# Commands: graph-based-node-orchestration
+
+## Available Commands
+
+- `/skill graph-based-node-orchestration` — Load this skill
+- `/run graph-based-node-orchestration` — Execute workflow

skills/graph-based-node-orchestration/examples.md

@@ -0,0 +1,10 @@
+# Examples: graph-based-node-orchestration
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model, blacknode package with Graph, Node, and cook functionality, Python script defining node types with inputs/outputs and connecting them via edges
+# Process: Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key() → Create a bn.Graph() instance to hold all nodes and their connections → Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
+# Outputs: A fully constructed graph with typed nodes and defined connections, Executed result (e.g., processed text, summary, or other output) from the final node, A reusable pattern that can be adapted to different models, hardware, or task types
+```

skills/graph-based-node-orchestration/metadata.json

@@ -0,0 +1,34 @@
+{
+  "name": "graph-based-node-orchestration",
+  "version": "1.0.0",
+  "goal": "Build a typed node graph that processes data through a sequence of operations and produces a final result",
+  "inputs": [
+    "NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model",
+    "blacknode package with Graph, Node, and cook functionality",
+    "Python script defining node types with inputs/outputs and connecting them via edges"
+  ],
+  "steps": [
+    "Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()",
+    "Create a bn.Graph() instance to hold all nodes and their connections",
+    "Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)",
+    "Connect nodes together using edge definitions (from_port -> to_port) to establish data flow",
+    "Execute the graph using g.cook() to run the pipeline and process data through the node chain",
+    "Extract the final result from the output node to complete the workflow"
+  ],
+  "outputs": [
+    "A fully constructed graph with typed nodes and defined connections",
+    "Executed result (e.g., processed text, summary, or other output) from the final node",
+    "A reusable pattern that can be adapted to different models, hardware, or task types"
+  ],
+  "failure_modes": [
+    "Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail",
+    "Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow",
+    "Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail",
+    "Graph execution error due to incorrect edge configuration or circular dependencies",
+    "Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow demonstrates a declarative graph-based orchestration pattern where nodes are connected via explicit ports and data flows through the graph. The pattern is highly reusable across different domains (robotics, research, data processing) because it separates graph structure from execution logic. The same graph construction and cook pattern can be adapted to different models (NIM, Anthropic, OpenAI), hardware targets (CPU, GPU, Jetson), and task types (LLM reasoning, file I/O, sensor processing).",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}

skills/graph-based-node-orchestration/tests.md

@@ -1,4 +1,4 @@
-# Tests: autonomous-web-research-agent
+# Tests: graph-based-node-orchestration
 
 ## Test Checklist