Add Skill: text-concatenation-pipeline

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/text-concatenation-pipeline/SKILL.md

@@ -0,0 +1,60 @@
+---
+name: text-concatenation-pipeline
+version: 1.0.0
+description: Combine two text inputs into a single output string using a Blacknode
+  node graph.
+inputs:
+- text_a (string)
+- text_b (string)
+steps:
+- Instantiate a Text node with parameter value set to text_a
+- Instantiate a Text node with parameter value set to text_b
+- Instantiate a Concat node with no parameters
+- Instantiate an Output node
+- Connect output port 'value' of first Text node to input port 'a' of Concat node
+- Connect output port 'value' of second Text node to input port 'b' of Concat node
+- Connect output port 'value' of Concat node to input port 'value' of Output node
+- Execute graph by cooking the Output node's 'value' port to obtain result
+outputs:
+- concatenated_text (string)
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# text-concatenation-pipeline
+
+Combine two text inputs into a single output string using a Blacknode node graph.
+
+## Steps
+
+1. Instantiate a Text node with parameter value set to text_a
+2. Instantiate a Text node with parameter value set to text_b
+3. Instantiate a Concat node with no parameters
+4. Instantiate an Output node
+5. Connect output port 'value' of first Text node to input port 'a' of Concat node
+6. Connect output port 'value' of second Text node to input port 'b' of Concat node
+7. Connect output port 'value' of Concat node to input port 'value' of Output node
+8. Execute graph by cooking the Output node's 'value' port to obtain result
+
+## Inputs
+
+- text_a (string)
+- text_b (string)
+
+## Outputs
+
+- concatenated_text (string)
+
+## Failure Modes
+
+- One or both text inputs missing or non-string
+- Invalid node connections (port mismatch)
+- Runtime error during graph execution
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95

skills/text-concatenation-pipeline/commands.md

@@ -0,0 +1,6 @@
+# Commands: text-concatenation-pipeline
+
+## Available Commands
+
+- `/skill text-concatenation-pipeline` — Load this skill
+- `/run text-concatenation-pipeline` — Execute workflow

skills/text-concatenation-pipeline/examples.md

@@ -0,0 +1,10 @@
+# Examples: text-concatenation-pipeline
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: text_a (string), text_b (string)
+# Process: Instantiate a Text node with parameter value set to text_a → Instantiate a Text node with parameter value set to text_b → Instantiate a Concat node with no parameters
+# Outputs: concatenated_text (string)
+```

skills/text-concatenation-pipeline/metadata.json

@@ -0,0 +1,31 @@
+{
+  "name": "text-concatenation-pipeline",
+  "version": "1.0.0",
+  "goal": "Combine two text inputs into a single output string using a Blacknode node graph.",
+  "inputs": [
+    "text_a (string)",
+    "text_b (string)"
+  ],
+  "steps": [
+    "Instantiate a Text node with parameter value set to text_a",
+    "Instantiate a Text node with parameter value set to text_b",
+    "Instantiate a Concat node with no parameters",
+    "Instantiate an Output node",
+    "Connect output port 'value' of first Text node to input port 'a' of Concat node",
+    "Connect output port 'value' of second Text node to input port 'b' of Concat node",
+    "Connect output port 'value' of Concat node to input port 'value' of Output node",
+    "Execute graph by cooking the Output node's 'value' port to obtain result"
+  ],
+  "outputs": [
+    "concatenated_text (string)"
+  ],
+  "failure_modes": [
+    "One or both text inputs missing or non-string",
+    "Invalid node connections (port mismatch)",
+    "Runtime error during graph execution"
+  ],
+  "confidence": 0.95,
+  "explanation": "Extracted from examples/converted_text_pipeline.py and referenced templates/text-pipeline.json in the Blacknode repository. The workflow is a basic reusable pattern for string concatenation using the visual node editor's graph model.",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}

skills/text-concatenation-pipeline/tests.md

@@ -1,4 +1,4 @@
-# Tests: autonomous-web-research-agent
+# Tests: text-concatenation-pipeline
 
 ## Test Checklist