Add Skill: conditional-request-routing

Extracted from: https://github.com/jwwelbor/AgentMap.git
Score: 1.0

skills/conditional-request-routing/SKILL.md

@@ -0,0 +1,54 @@
+---
+name: conditional-request-routing
+version: 1.0.0
+description: Classify an input request and route it to either an approval or rejection
+  path, producing a corresponding result
+inputs:
+- 'request: the input request or content to be reviewed and routed'
+steps:
+- 'Collect input: Use an input agent to capture the user''s request into state field
+  ''request'''
+- 'Branch: Use a branching agent to evaluate ''request'' and route to ''Approve''
+  node on success or ''Reject'' node on failure'
+- 'Approve path: Use a default agent to format and output an approval message with
+  the request'
+- 'Reject path: Use a default agent to format and output a rejection message with
+  the request'
+outputs:
+- 'result: the approval or rejection message containing the original request'
+tags: []
+metadata:
+  source_repo: https://github.com/jwwelbor/AgentMap.git
+  extracted_at: ''
+  confidence: 0.85
+---
+
+# conditional-request-routing
+
+Classify an input request and route it to either an approval or rejection path, producing a corresponding result
+
+## Steps
+
+1. Collect input: Use an input agent to capture the user's request into state field 'request'
+2. Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure
+3. Approve path: Use a default agent to format and output an approval message with the request
+4. Reject path: Use a default agent to format and output a rejection message with the request
+
+## Inputs
+
+- request: the input request or content to be reviewed and routed
+
+## Outputs
+
+- result: the approval or rejection message containing the original request
+
+## Failure Modes
+
+- Branching agent cannot evaluate request and neither path is taken
+- Missing or empty input request
+- Prompt misconfiguration causing incorrect routing
+
+## Source
+
+Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
+Confidence: 0.85

skills/conditional-request-routing/commands.md

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

skills/conditional-request-routing/examples.md

@@ -0,0 +1,10 @@
+# Examples: conditional-request-routing
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: request: the input request or content to be reviewed and routed
+# Process: Collect input: Use an input agent to capture the user's request into state field 'request' → Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure → Approve path: Use a default agent to format and output an approval message with the request
+# Outputs: result: the approval or rejection message containing the original request
+```

skills/conditional-request-routing/metadata.json

@@ -0,0 +1,26 @@
+{
+  "name": "conditional-request-routing",
+  "version": "1.0.0",
+  "goal": "Classify an input request and route it to either an approval or rejection path, producing a corresponding result",
+  "inputs": [
+    "request: the input request or content to be reviewed and routed"
+  ],
+  "steps": [
+    "Collect input: Use an input agent to capture the user's request into state field 'request'",
+    "Branch: Use a branching agent to evaluate 'request' and route to 'Approve' node on success or 'Reject' node on failure",
+    "Approve path: Use a default agent to format and output an approval message with the request",
+    "Reject path: Use a default agent to format and output a rejection message with the request"
+  ],
+  "outputs": [
+    "result: the approval or rejection message containing the original request"
+  ],
+  "failure_modes": [
+    "Branching agent cannot evaluate request and neither path is taken",
+    "Missing or empty input request",
+    "Prompt misconfiguration causing incorrect routing"
+  ],
+  "confidence": 0.85,
+  "explanation": "Extracted from AgentMap's README example 'ReviewFlow' CSV workflow. This is a declarative LangGraph pattern using AgentMap's CSV format that implements conditional branching - a universally reusable pattern for request triage, content moderation, approval gates, or any two-path decision flow. It can be adapted by changing agent types (e.g., using LLM agents instead of default) and prompts.",
+  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
+  "score": 1.0
+}

skills/conditional-request-routing/tests.md

@@ -1,4 +1,4 @@
-# Tests: local-document-research-with-traceable-citations
+# Tests: conditional-request-routing
 
 ## Test Checklist
 

skills/local-document-research-with-traceable-citations/SKILL.md

@@ -1,82 +0,0 @@
----
-name: local-document-research-with-traceable-citations
-version: 1.0.0
-description: Enable users to import local documents, asynchronously process them into
-  an indexed knowledge base, and obtain AI-generated answers that cite specific page
-  locations and OCR evidence.
-inputs:
-- Local document files (PDF, DOCX, PPTX, XLSX, images, TXT)
-- Configured LLM API endpoint and keys (via .env or settings)
-- Optional web search service config if enabled
-- Local OCR model cache (downloaded on first use)
-steps:
-- Import documents into a project; files are queued for asynchronous processing.
-- Convert non-PDF formats (DOCX, PPTX, XLSX) to PDF using native Office or LibreOffice
-  fallback.
-- Run OCR (PaddleOCR) on PDF pages/images to extract text, page numbers, polygons,
-  and confidence scores.
-- Chunk text and generate embeddings; publish to LanceDB hybrid index (dense vector
-  + full-text) only when fully processed.
-- User starts a research session or branch; Leader agent analyzes query.
-- Hybrid RAG retrieves candidate chunks from ready documents; dynamic material scope
-  ensures no half-indexed docs.
-- Leader delegates tasks to sub-agents (researcher, reviewer, writer) via constrained
-  task capability; each delegation logs start, completion, duration, and evidence.
-- Generate answer that references only actually used evidence; citations include document
-  ID, page, coordinates.
-- User clicks citation to open original document and view highlighted OCR location.
-outputs:
-- Project with indexed document library (SQLite metadata + LanceDB vectors)
-- AI answers with verifiable citations to source pages
-- Evidence preview with page image and OCR highlight polygons
-- Persistent session history, branches, and long-term memory
-tags: []
-metadata:
-  source_repo: https://github.com/0verL1nk/PaperSage.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# local-document-research-with-traceable-citations
-
-Enable users to import local documents, asynchronously process them into an indexed knowledge base, and obtain AI-generated answers that cite specific page locations and OCR evidence.
-
-## Steps
-
-1. Import documents into a project; files are queued for asynchronous processing.
-2. Convert non-PDF formats (DOCX, PPTX, XLSX) to PDF using native Office or LibreOffice fallback.
-3. Run OCR (PaddleOCR) on PDF pages/images to extract text, page numbers, polygons, and confidence scores.
-4. Chunk text and generate embeddings; publish to LanceDB hybrid index (dense vector + full-text) only when fully processed.
-5. User starts a research session or branch; Leader agent analyzes query.
-6. Hybrid RAG retrieves candidate chunks from ready documents; dynamic material scope ensures no half-indexed docs.
-7. Leader delegates tasks to sub-agents (researcher, reviewer, writer) via constrained task capability; each delegation logs start, completion, duration, and evidence.
-8. Generate answer that references only actually used evidence; citations include document ID, page, coordinates.
-9. User clicks citation to open original document and view highlighted OCR location.
-
-## Inputs
-
-- Local document files (PDF, DOCX, PPTX, XLSX, images, TXT)
-- Configured LLM API endpoint and keys (via .env or settings)
-- Optional web search service config if enabled
-- Local OCR model cache (downloaded on first use)
-
-## Outputs
-
-- Project with indexed document library (SQLite metadata + LanceDB vectors)
-- AI answers with verifiable citations to source pages
-- Evidence preview with page image and OCR highlight polygons
-- Persistent session history, branches, and long-term memory
-
-## Failure Modes
-
-- OCR quality low for scanned images leading to poor extraction
-- Missing Office/LibreOffice causes conversion failure for Office docs
-- Interrupted indexing leaves documents unpublished and excluded from retrieval
-- LLM API outage or misconfiguration yields no answer
-- Citation coordinates mismatch due to chunk drift
-- Sub-agent recursion if constraints not enforced
-
-## Source
-
-Extracted from: [https://github.com/0verL1nk/PaperSage.git](https://github.com/0verL1nk/PaperSage.git)
-Confidence: 0.85

skills/local-document-research-with-traceable-citations/commands.md

@@ -1,6 +0,0 @@
-# Commands: local-document-research-with-traceable-citations
-
-## Available Commands
-
-- `/skill local-document-research-with-traceable-citations` — Load this skill
-- `/run local-document-research-with-traceable-citations` — Execute workflow

skills/local-document-research-with-traceable-citations/examples.md

@@ -1,10 +0,0 @@
-# Examples: local-document-research-with-traceable-citations
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: Local document files (PDF, DOCX, PPTX, XLSX, images, TXT), Configured LLM API endpoint and keys (via .env or settings), Optional web search service config if enabled, Local OCR model cache (downloaded on first use)
-# Process: Import documents into a project; files are queued for asynchronous processing. → Convert non-PDF formats (DOCX, PPTX, XLSX) to PDF using native Office or LibreOffice fallback. → Run OCR (PaddleOCR) on PDF pages/images to extract text, page numbers, polygons, and confidence scores.
-# Outputs: Project with indexed document library (SQLite metadata + LanceDB vectors), AI answers with verifiable citations to source pages, Evidence preview with page image and OCR highlight polygons, Persistent session history, branches, and long-term memory
-```

skills/local-document-research-with-traceable-citations/metadata.json

@@ -1,40 +0,0 @@
-{
-  "name": "local-document-research-with-traceable-citations",
-  "version": "1.0.0",
-  "goal": "Enable users to import local documents, asynchronously process them into an indexed knowledge base, and obtain AI-generated answers that cite specific page locations and OCR evidence.",
-  "inputs": [
-    "Local document files (PDF, DOCX, PPTX, XLSX, images, TXT)",
-    "Configured LLM API endpoint and keys (via .env or settings)",
-    "Optional web search service config if enabled",
-    "Local OCR model cache (downloaded on first use)"
-  ],
-  "steps": [
-    "Import documents into a project; files are queued for asynchronous processing.",
-    "Convert non-PDF formats (DOCX, PPTX, XLSX) to PDF using native Office or LibreOffice fallback.",
-    "Run OCR (PaddleOCR) on PDF pages/images to extract text, page numbers, polygons, and confidence scores.",
-    "Chunk text and generate embeddings; publish to LanceDB hybrid index (dense vector + full-text) only when fully processed.",
-    "User starts a research session or branch; Leader agent analyzes query.",
-    "Hybrid RAG retrieves candidate chunks from ready documents; dynamic material scope ensures no half-indexed docs.",
-    "Leader delegates tasks to sub-agents (researcher, reviewer, writer) via constrained task capability; each delegation logs start, completion, duration, and evidence.",
-    "Generate answer that references only actually used evidence; citations include document ID, page, coordinates.",
-    "User clicks citation to open original document and view highlighted OCR location."
-  ],
-  "outputs": [
-    "Project with indexed document library (SQLite metadata + LanceDB vectors)",
-    "AI answers with verifiable citations to source pages",
-    "Evidence preview with page image and OCR highlight polygons",
-    "Persistent session history, branches, and long-term memory"
-  ],
-  "failure_modes": [
-    "OCR quality low for scanned images leading to poor extraction",
-    "Missing Office/LibreOffice causes conversion failure for Office docs",
-    "Interrupted indexing leaves documents unpublished and excluded from retrieval",
-    "LLM API outage or misconfiguration yields no answer",
-    "Citation coordinates mismatch due to chunk drift",
-    "Sub-agent recursion if constraints not enforced"
-  ],
-  "confidence": 0.85,
-  "explanation": "The README outlines a clear pipeline from document import to cited answer with evidence location, which is a reusable pattern for local-first RAG applications requiring traceability.",
-  "source_repo": "https://github.com/0verL1nk/PaperSage.git",
-  "score": 1.0
-}