Add Skill: conditional-review-workflow

Extracted from: https://github.com/jwwelbor/AgentMap.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/conditional-review-workflow/SKILL.md

@@ -0,0 +1,56 @@
+---
+name: conditional-review-workflow
+version: 1.0.0
+description: Process a user request through branching logic to approve or reject it,
+  producing a routed decision result
+inputs:
+- 'request: string - The user''s request or input collected at runtime via the input
+  agent'
+steps:
+- 'Start: Input agent collects the user request and stores it in the ''request'' state
+  field, then routes to Classify node'
+- 'Classify: Branching agent evaluates the ''request'' field and routes to Approve
+  node on success or Reject node on failure (on_failure)'
+- 'Approve: Default agent formats an approval message using the request and stores
+  it in the ''result'' output field'
+- 'Reject: Default agent formats a rejection message using the request and stores
+  it in the ''result'' output field'
+outputs:
+- 'result: string - Final message indicating whether the request was approved or rejected,
+  containing the original request'
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# conditional-review-workflow
+
+Process a user request through branching logic to approve or reject it, producing a routed decision result
+
+## Steps
+
+1. Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node
+2. Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)
+3. Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
+4. Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field
+
+## Inputs
+
+- request: string - The user's request or input collected at runtime via the input agent
+
+## Outputs
+
+- result: string - Final message indicating whether the request was approved or rejected, containing the original request
+
+## Failure Modes
+
+- Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)
+- Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration
+- LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.85

skills/conditional-review-workflow/commands.md

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

skills/conditional-review-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: conditional-review-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: request: string - The user's request or input collected at runtime via the input agent
+# Process: Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node → Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure) → Approve: Default agent formats an approval message using the request and stores it in the 'result' output field
+# Outputs: result: string - Final message indicating whether the request was approved or rejected, containing the original request
+```

skills/conditional-review-workflow/metadata.json

@@ -0,0 +1,26 @@
+{
+  "name": "conditional-review-workflow",
+  "version": "1.0.0",
+  "goal": "Process a user request through branching logic to approve or reject it, producing a routed decision result",
+  "inputs": [
+    "request: string - The user's request or input collected at runtime via the input agent"
+  ],
+  "steps": [
+    "Start: Input agent collects the user request and stores it in the 'request' state field, then routes to Classify node",
+    "Classify: Branching agent evaluates the 'request' field and routes to Approve node on success or Reject node on failure (on_failure)",
+    "Approve: Default agent formats an approval message using the request and stores it in the 'result' output field",
+    "Reject: Default agent formats a rejection message using the request and stores it in the 'result' output field"
+  ],
+  "outputs": [
+    "result: string - Final message indicating whether the request was approved or rejected, containing the original request"
+  ],
+  "failure_modes": [
+    "Input collection failure - user provides no or invalid input (no explicit on_failure defined for Start node in example)",
+    "Branching classification failure - if branching agent cannot evaluate, it routes to Reject via on_failure configuration",
+    "LLM provider unavailability - if branching or agents rely on LLM backends that are misconfigured or offline"
+  ],
+  "confidence": 0.85,
+  "explanation": "Extracted from AgentMap's documented CSV workflow example (ReviewFlow). This is a reusable conditional routing pattern that can be adapted for any approval/rejection, triage, or binary-decision scenario by modifying the branching prompt and agent types. The CSV-based declarative format makes it portable across the AgentMap framework.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/conditional-review-workflow/tests.md

@@ -1,4 +1,4 @@
-# Tests: autonomous-web-research-agent
+# Tests: conditional-review-workflow
 
 ## Test Checklist