Add Skill: research-pipeline

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

pipeline/publisher.py

@@ -1,10 +1,10 @@
 """Stage 8: Publisher — Create branch, commit, open PR on Gitea."""
-import json
 import subprocess
 import os
 import tempfile
-import shutil
 import datetime
+import requests
+
 
 def publish_skill(review_result, config):
     """
@@ -33,23 +33,20 @@ def publish_skill(review_result, config):
     branch_name = f"skill/{skill_name}-{ts}"
 
     with tempfile.TemporaryDirectory() as tmpdir:
-        # Clone repo
         repo_dir = os.path.join(tmpdir, "agent-skills")
+
+        # Clone repo
         result = subprocess.run(
-            ["git", "clone", "--branch", "main", "--single-branch", clone_url, repo_dir],
+            ["git", "clone", "--branch", "main", "--depth", "1", clone_url, repo_dir],
             capture_output=True, text=True, timeout=30
         )
         if result.returncode != 0:
-            # Try without --branch (might not exist yet)
             result = subprocess.run(
-                ["git", "clone", clone_url, repo_dir],
+                ["git", "clone", "--depth", "1", clone_url, repo_dir],
                 capture_output=True, text=True, timeout=30
             )
             if result.returncode != 0:
-                return {
-                    "status": "CLONE_ERROR",
-                    "error": result.stderr[:500],
-                }
+                return {"status": "CLONE_ERROR", "error": result.stderr[:500]}
 
         # Configure git
         subprocess.run(["git", "config", "user.email", "hermes@agent.local"], cwd=repo_dir)
@@ -59,34 +56,80 @@ def publish_skill(review_result, config):
         skill_dir = os.path.join(repo_dir, "skills", skill_name)
         os.makedirs(skill_dir, exist_ok=True)
 
-        # Write files
+        # Write skill files
         for filename, content in files.items():
             filepath = os.path.join(skill_dir, filename)
-            with open(filepath, 'w') as f:
+            with open(filepath, "w") as f:
                 f.write(content)
 
-        # Add and commit
-        subprocess.run(["git", "add", "."], cwd=repo_dir, capture_output=True)
-        subprocess.run(
-            ["git", "commit", "-m", f"Add Skill: {skill_name}\n\nExtracted from: {gen.get('metadata', {}).get('source_repo', 'unknown')}\nScore: {gen.get('metadata', {}).get('score', 0)}"],
-            cwd=repo_dir, capture_output=True
+        # Verify files were written
+        written_files = []
+        for root, dirs, fnames in os.walk(skill_dir):
+            for fn in fnames:
+                written_files.append(os.path.join(root, fn))
+
+        if not written_files:
+            return {"status": "EMPTY_SKILL", "reason": "No files written to skill directory"}
+
+        # Stage and commit
+        add_result = subprocess.run(
+            ["git", "add", "skills/"], cwd=repo_dir, capture_output=True, text=True
         )
 
+        # Check if there are actually staged changes
+        status_result = subprocess.run(
+            ["git", "diff", "--cached", "--name-only"],
+            cwd=repo_dir, capture_output=True, text=True
+        )
+        staged_files = status_result.stdout.strip().split("\n") if status_result.stdout.strip() else []
+
+        if not staged_files:
+            # Nothing to commit — files might already exist. Force add.
+            subprocess.run(["git", "add", "-f", "skills/"], cwd=repo_dir, capture_output=True, text=True)
+            status_result = subprocess.run(
+                ["git", "diff", "--cached", "--name-only"],
+                cwd=repo_dir, capture_output=True, text=True
+            )
+            staged_files = status_result.stdout.strip().split("\n") if status_result.stdout.strip() else []
+
+        if not staged_files:
+            return {
+                "status": "NO_CHANGES",
+                "reason": f"No new files to commit for {skill_name}. Files already exist in repo.",
+            }
+
+        commit_result = subprocess.run(
+            [
+                "git", "commit", "-m",
+                f"Add Skill: {skill_name}\n\nExtracted from: {gen.get('metadata', {}).get('source_repo', 'unknown')}\nScore: {gen.get('metadata', {}).get('score', 0)}"
+            ],
+            cwd=repo_dir, capture_output=True, text=True
+        )
+
+        if commit_result.returncode != 0:
+            return {
+                "status": "COMMIT_ERROR",
+                "error": commit_result.stderr[:500],
+            }
+
+        # Checkout new branch
+        checkout_result = subprocess.run(
+            ["git", "checkout", "-b", branch_name],
+            cwd=repo_dir, capture_output=True, text=True
+        )
+        if checkout_result.returncode != 0:
+            return {
+                "status": "CHECKOUT_ERROR",
+                "error": checkout_result.stderr[:500],
+            }
+
         # Push branch
         auth_url = clone_url.replace("http://", f"http://tonyjbala:{token}@")
         push_result = subprocess.run(
-            ["git", "push", "-u", auth_url, f"main:{branch_name}"],
-            capture_output=True, text=True, timeout=30
+            ["git", "push", "-u", auth_url, branch_name],
+            cwd=repo_dir, capture_output=True, text=True, timeout=30
         )
 
-        if push_result.returncode != 0:
-            # Try creating from current branch
-            subprocess.run(["git", "checkout", "-b", branch_name], cwd=repo_dir, capture_output=True)
-            push_result = subprocess.run(
-                ["git", "push", "-u", auth_url, branch_name],
-                capture_output=True, text=True, timeout=30
-            )
-
         if push_result.returncode != 0:
             return {
                 "status": "PUSH_ERROR",
@@ -97,19 +140,19 @@ def publish_skill(review_result, config):
         pr_url = f"{base_url}/api/v1/repos/{owner}/{repo_name}/pulls"
         pr_payload = {
             "title": f"Add Skill: {skill_name}",
-            "body": f"## Skill: {skill_name}\n\n"
-                    f"**Goal:** {gen.get('metadata', {}).get('goal', '')}\n"
-                    f"**Source:** {gen.get('metadata', {}).get('source_repo', '')}\n"
-                    f"**Score:** {gen.get('metadata', {}).get('score', 0)}\n"
-                    f"**Confidence:** {gen.get('metadata', {}).get('confidence', 0)}\n"
-                    f"**Review:** {review_result.get('reason', '')}\n\n"
-                    f"### Files\n"
-                    + "".join(f"- `{f}`\n" for f in files.keys()),
+            "body": (
+                f"## Skill: {skill_name}\n\n"
+                f"**Goal:** {gen.get('metadata', {}).get('goal', '')}\n"
+                f"**Source:** {gen.get('metadata', {}).get('source_repo', '')}\n"
+                f"**Score:** {gen.get('metadata', {}).get('score', 0)}\n"
+                f"**Confidence:** {gen.get('metadata', {}).get('confidence', 0)}\n\n"
+                f"### Files\n"
+                + "".join(f"- `{f}`\n" for f in files.keys())
+            ),
             "head": branch_name,
             "base": "main",
         }
 
-        import requests
         headers = {
             "Authorization": f"token {token}",
             "Content-Type": "application/json",
@@ -127,7 +170,6 @@ def publish_skill(review_result, config):
                 "message": f"PR opened: {pr_data.get('html_url', '')}",
             }
         elif resp.status_code == 409:
-            # PR already exists for this branch
             return {
                 "status": "PUBLISHED",
                 "skill_name": skill_name,

runs/20260805-053642.json

@@ -1,21 +0,0 @@
-{
-  "run_id": "20260805-053642",
-  "started_at": "2026-08-05T05:36:42.627714",
-  "stages": {
-    "scout": {
-      "count": 2
-    },
-    "filter": {
-      "kept": 2,
-      "rejected": 0
-    }
-  },
-  "results": {
-    "extracted": 0,
-    "scored": 0,
-    "generated": 0,
-    "reviewed": 0,
-    "published": 0
-  },
-  "ended_at": "2026-08-05T05:36:49.213968"
-}

runs/20260805-053718.json

@@ -1,21 +0,0 @@
-{
-  "run_id": "20260805-053718",
-  "started_at": "2026-08-05T05:37:18.169016",
-  "stages": {
-    "scout": {
-      "count": 2
-    },
-    "filter": {
-      "kept": 2,
-      "rejected": 0
-    }
-  },
-  "results": {
-    "extracted": 0,
-    "scored": 0,
-    "generated": 0,
-    "reviewed": 0,
-    "published": 0
-  },
-  "ended_at": "2026-08-05T05:38:25.179277"
-}

runs/20260805-054839.json

@@ -1,21 +0,0 @@
-{
-  "run_id": "20260805-054839",
-  "started_at": "2026-08-05T05:48:39.344784",
-  "stages": {
-    "scout": {
-      "count": 5
-    },
-    "filter": {
-      "kept": 5,
-      "rejected": 0
-    }
-  },
-  "results": {
-    "extracted": 0,
-    "scored": 0,
-    "generated": 0,
-    "reviewed": 0,
-    "published": 0
-  },
-  "ended_at": "2026-08-05T05:48:50.960254"
-}

runs/20260805-054930.json

@@ -1,21 +0,0 @@
-{
-  "run_id": "20260805-054930",
-  "started_at": "2026-08-05T05:49:30.857560",
-  "stages": {
-    "scout": {
-      "count": 5
-    },
-    "filter": {
-      "kept": 5,
-      "rejected": 0
-    }
-  },
-  "results": {
-    "extracted": 0,
-    "scored": 0,
-    "generated": 0,
-    "reviewed": 0,
-    "published": 0
-  },
-  "ended_at": "2026-08-05T05:49:45.411491"
-}

runs/20260805-055041.json

@@ -1,21 +0,0 @@
-{
-  "run_id": "20260805-055041",
-  "started_at": "2026-08-05T05:50:41.871225",
-  "stages": {
-    "scout": {
-      "count": 5
-    },
-    "filter": {
-      "kept": 5,
-      "rejected": 0
-    }
-  },
-  "results": {
-    "extracted": 0,
-    "scored": 0,
-    "generated": 0,
-    "reviewed": 0,
-    "published": 0
-  },
-  "ended_at": "2026-08-05T05:50:57.051862"
-}

skills/code-review-agent-workflow/SKILL.md

@@ -0,0 +1,77 @@
+---
+name: code-review-agent-workflow
+version: 1.0.0
+description: Automate the code review process using a multi-step workflow with human-in-the-loop
+  approval.
+inputs:
+- Sample diff of code changes (str)
+- Repo context (dict)
+steps:
+- 'Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`'
+- 'Step 2: Invoke the graph with initial parameters including sample diff, repo context,
+  user ID, and other metadata'
+- 'Step 3: The graph processes the input through a series of steps, generating messages
+  and issues as it progresses'
+outputs:
+- Final result containing processed messages and issues (dict)
+tags: []
+metadata:
+  source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# code-review-agent-workflow
+
+Automate the code review process using a multi-step workflow with human-in-the-loop approval.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langgraph>=0.3 langchain-core>=0.3 langchain-anthropic>=0.3 langfuse>=2.0 mcp[server]>=1.24,<2.0 langchain-mcp-adapters>=0.1 tenacity>=9.0 fastapi>=0.115 uvicorn[standard]>=0.32 psycopg[binary]>=3.1 langgraph-checkpoint-postgres>=2.0 httpx>=0.27 python-dotenv>=1.0 redis>=5.0
+```
+
+**Setup steps:**
+
+1. cp .env.example .env
+1. docker compose up -d
+1. pip install -e '.[dev]'
+
+## Key Files
+
+- `agentkit/workflow/code_review/graph.py - Contains the `build_graph` function and graph invocation logic.`
+- `examples/run_code_review.py - Example script demonstrating how to run the code review agent.`
+
+## Steps
+
+1. Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`
+2. Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata
+3. Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses
+
+## Implementation Details
+
+```python
+graph = build_graph()
+thread_id = str(uuid.uuid4())
+result = graph.invoke(...)
+```
+
+## Inputs
+
+- Sample diff of code changes (str)
+- Repo context (dict)
+
+## Outputs
+
+- Final result containing processed messages and issues (dict)
+
+## Failure Modes
+
+- Specific failure scenario with mitigation: If the `build_graph()` function fails to initialize properly, ensure all required dependencies are correctly installed.
+
+## Source
+
+Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
+Confidence: 0.95

skills/code-review-agent-workflow/commands.md

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

skills/code-review-agent-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: code-review-agent-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Sample diff of code changes (str), Repo context (dict)
+# Process: Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph` → Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata → Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses
+# Outputs: Final result containing processed messages and issues (dict)
+```

skills/code-review-agent-workflow/metadata.json

@@ -0,0 +1,24 @@
+{
+  "name": "code-review-agent-workflow",
+  "version": "1.0.0",
+  "goal": "Automate the code review process using a multi-step workflow with human-in-the-loop approval.",
+  "inputs": [
+    "Sample diff of code changes (str)",
+    "Repo context (dict)"
+  ],
+  "steps": [
+    "Step 1: Build the graph for the code review agent using `build_graph()` from `agentkit.workflow.code_review.graph`",
+    "Step 2: Invoke the graph with initial parameters including sample diff, repo context, user ID, and other metadata",
+    "Step 3: The graph processes the input through a series of steps, generating messages and issues as it progresses"
+  ],
+  "outputs": [
+    "Final result containing processed messages and issues (dict)"
+  ],
+  "failure_modes": [
+    "Specific failure scenario with mitigation: If the `build_graph()` function fails to initialize properly, ensure all required dependencies are correctly installed."
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is reusable for any code review process that requires a multi-step analysis and human approval.",
+  "source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
+  "score": 1.0
+}

skills/code-review-agent-workflow/tests.md

@@ -0,0 +1,9 @@
+# Tests: code-review-agent-workflow
+
+## Test Checklist
+
+- [ ] Workflow has at least 3 steps
+- [ ] All inputs are defined
+- [ ] All outputs are defined
+- [ ] Failure modes are documented
+- [ ] Skill can be loaded without errors

skills/mcp-server-setup/SKILL.md

@@ -0,0 +1,98 @@
+---
+name: mcp-server-setup
+version: 1.0.0
+description: Set up an MCP server to integrate PipesHub with any MCP-compatible client.
+inputs:
+- MCP server configuration details
+- PipesHub credentials
+steps:
+- 'Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`'
+- 'Step 2: Navigate to the cloned directory with `cd mcp-server`'
+- 'Step 3: Run the interactive installer by executing `./install.sh`'
+- 'Step 4: Follow the prompts in the installer to configure the server, including
+  setting up graph DB, message broker, and KV store'
+- 'Step 5: The installer will generate a `.env` file with necessary environment variables.
+  Ensure these are correctly set'
+- 'Step 6: Start the MCP server by running `docker-compose up -d`'
+outputs:
+- Running MCP server
+- .env file generated
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# mcp-server-setup
+
+Set up an MCP server to integrate PipesHub with any MCP-compatible client.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install docker docker-compose
+```
+
+**Setup steps:**
+
+1. Ensure Docker and Docker Compose are installed on your system.
+1. Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
+
+## Key Files
+
+- `path/to/install.sh - Script to run the interactive installer`
+- `path/to/docker-compose.yml - Configuration for Docker services`
+
+## Steps
+
+1. Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`
+2. Step 2: Navigate to the cloned directory with `cd mcp-server`
+3. Step 3: Run the interactive installer by executing `./install.sh`
+4. Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store
+5. Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set
+6. Step 6: Start the MCP server by running `docker-compose up -d`
+
+## Implementation Details
+
+```python
+```bash
+./install.sh
+```
+Run this script to start the installation process.
+```
+
+```python
+```yaml
+docker-compose:
+  version: '3.9'
+services:
+  mcp-server:
+    image: pipeshubai/mcp-server:latest
+    environment:
+      - PIPESHUB_API_KEY=your_api_key_here
+```
+This snippet shows how to configure the Docker Compose file.
+```
+
+## Inputs
+
+- MCP server configuration details
+- PipesHub credentials
+
+## Outputs
+
+- Running MCP server
+- .env file generated
+
+## Failure Modes
+
+- Installer fails to run due to missing dependencies or incorrect configuration
+- Docker Compose setup issues preventing server from starting
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95

skills/mcp-server-setup/commands.md

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

skills/mcp-server-setup/examples.md

@@ -0,0 +1,10 @@
+# Examples: mcp-server-setup
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: MCP server configuration details, PipesHub credentials
+# Process: Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git` → Step 2: Navigate to the cloned directory with `cd mcp-server` → Step 3: Run the interactive installer by executing `./install.sh`
+# Outputs: Running MCP server, .env file generated
+```

skills/mcp-server-setup/metadata.json

@@ -0,0 +1,29 @@
+{
+  "name": "mcp-server-setup",
+  "version": "1.0.0",
+  "goal": "Set up an MCP server to integrate PipesHub with any MCP-compatible client.",
+  "inputs": [
+    "MCP server configuration details",
+    "PipesHub credentials"
+  ],
+  "steps": [
+    "Step 1: Clone the `pipeshub-ai/mcp-server` repository using `git clone https://github.com/pipeshub-ai/mcp-server.git`",
+    "Step 2: Navigate to the cloned directory with `cd mcp-server`",
+    "Step 3: Run the interactive installer by executing `./install.sh`",
+    "Step 4: Follow the prompts in the installer to configure the server, including setting up graph DB, message broker, and KV store",
+    "Step 5: The installer will generate a `.env` file with necessary environment variables. Ensure these are correctly set",
+    "Step 6: Start the MCP server by running `docker-compose up -d`"
+  ],
+  "outputs": [
+    "Running MCP server",
+    ".env file generated"
+  ],
+  "failure_modes": [
+    "Installer fails to run due to missing dependencies or incorrect configuration",
+    "Docker Compose setup issues preventing server from starting"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is specific but can be adapted for different deployment environments and configurations.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}

skills/mcp-server-setup/tests.md

@@ -0,0 +1,9 @@
+# Tests: mcp-server-setup
+
+## Test Checklist
+
+- [ ] Workflow has at least 3 steps
+- [ ] All inputs are defined
+- [ ] All outputs are defined
+- [ ] Failure modes are documented
+- [ ] Skill can be loaded without errors

skills/multi-agent-sequential-workflow/SKILL.md

@@ -0,0 +1,84 @@
+---
+name: multi-agent-sequential-workflow
+version: 1.0.0
+description: Gather and process information from multiple agents to generate a comprehensive
+  travel guide.
+inputs:
+- User query with location
+steps:
+- 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
+  to gather raw facts about the destination.'
+- 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
+  a structured travel guide based on the user''s request and raw information provided
+  by the researcher.'
+- 'Step 3: Writer agent (agent.py) formats the final response, including the structured
+  guide content and prominently features the ''Suggested Web Pages'' section.'
+outputs:
+- Structured travel guide with key sections
+- Final client response
+tags: []
+metadata:
+  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# multi-agent-sequential-workflow
+
+Gather and process information from multiple agents to generate a comprehensive travel guide.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install python3 fastapi uvicorn strands bedrock-model
+```
+
+**Setup steps:**
+
+1. Install required dependencies using pip
+1. Set up environment variables for API keys and model IDs
+
+## Key Files
+
+- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the multi-agent workflow logic.`
+- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
+
+## Steps
+
+1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
+2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.
+3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
+
+## Implementation Details
+
+```python
+research_output = researcher_agent(query, stream=False)
+```
+
+```python
+guide_output = travel_guide_agent(planner_prompt, stream=False)
+```
+
+```python
+final_response = writer_agent(writer_prompt, stream=False)
+```
+
+## Inputs
+
+- User query with location
+
+## Outputs
+
+- Structured travel guide with key sections
+- Final client response
+
+## Failure Modes
+
+- Network issues during API calls could lead to incomplete data collection or processing failures
+
+## Source
+
+Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
+Confidence: 0.95

skills/multi-agent-sequential-workflow/commands.md

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

skills/multi-agent-sequential-workflow/examples.md

@@ -0,0 +1,10 @@
+# Examples: multi-agent-sequential-workflow
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: User query with location
+# Process: Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination. → Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher. → Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
+# Outputs: Structured travel guide with key sections, Final client response
+```

skills/multi-agent-sequential-workflow/metadata.json

@@ -0,0 +1,24 @@
+{
+  "name": "multi-agent-sequential-workflow",
+  "version": "1.0.0",
+  "goal": "Gather and process information from multiple agents to generate a comprehensive travel guide.",
+  "inputs": [
+    "User query with location"
+  ],
+  "steps": [
+    "Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.",
+    "Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.",
+    "Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section."
+  ],
+  "outputs": [
+    "Structured travel guide with key sections",
+    "Final client response"
+  ],
+  "failure_modes": [
+    "Network issues during API calls could lead to incomplete data collection or processing failures"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.",
+  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
+  "score": 1.0
+}

skills/multi-agent-sequential-workflow/tests.md

@@ -0,0 +1,9 @@
+# Tests: multi-agent-sequential-workflow
+
+## Test Checklist
+
+- [ ] Workflow has at least 3 steps
+- [ ] All inputs are defined
+- [ ] All outputs are defined
+- [ ] Failure modes are documented
+- [ ] Skill can be loaded without errors

skills/research-pipeline/SKILL.md

@@ -0,0 +1,105 @@
+---
+name: research-pipeline
+version: 1.0.0
+description: Fetch a Wikipedia page, summarise it using an AI agent, and write the
+  summary to a file.
+inputs:
+- URL of the Wikipedia page
+steps:
+- 'Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key)
+  and blacknode'
+- 'Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`'
+- 'Step 3: Create a Graph instance `g`'
+- 'Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)'
+- 'Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect
+  it to the Literal node'
+- 'Step 6: Add an LLMAgent node with system prompt ''You are a technical writer. Summarise
+  the text in 3 bullet points.'' and model NIM_MODEL (`summarise`), connecting its
+  input to the output of `fetcher`'
+- 'Step 7: Add a FileWrite node to write the summary to a file named ''summary.txt''
+  (`writer`), connecting its input to the output of `summarise`'
+- 'Step 8: Cook the graph starting from the writer node and print the path where the
+  summary is written'
+outputs:
+- Path to the summary file
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# research-pipeline
+
+Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install anthropic>=0.25 docker>=7.1 openai>=1.0
+```
+
+**Setup steps:**
+
+1. Ensure NVIDIA NIM API key is set in the environment or editor UI
+1. Install required dependencies using `pip install -r requirements.txt`
+
+## Key Files
+
+- `examples/research_pipeline.py - Contains the research pipeline workflow`
+
+## Steps
+
+1. Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode
+2. Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`
+3. Step 3: Create a Graph instance `g`
+4. Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)
+5. Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node
+6. Step 6: Add an LLMAgent node with system prompt 'You are a technical writer. Summarise the text in 3 bullet points.' and model NIM_MODEL (`summarise`), connecting its input to the output of `fetcher`
+7. Step 7: Add a FileWrite node to write the summary to a file named 'summary.txt' (`writer`), connecting its input to the output of `summarise`
+8. Step 8: Cook the graph starting from the writer node and print the path where the summary is written
+
+## Implementation Details
+
+```python
+from _bootstrap import NIM_MODEL, require_nim_api_key
+import blacknode as bn
+```
+
+```python
+g = bn.Graph()
+url = g.node('Literal', value='https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles=Houdini_(software)&format=json&formatversion=2&origin=*')
+fetcher = g.node('HTTPGet')
+summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL)
+writer = g.node('FileWrite', path='summary.txt')
+```
+
+```python
+url.out('value') >> fetcher.inp('url')
+fetcher.out('text') >> summarise.inp('prompt')
+summarise.out('text') >> writer.inp('text')
+```
+
+```python
+result = g.cook(writer, 'path')
+print(f'Summary written to: {result}')
+```
+
+## Inputs
+
+- URL of the Wikipedia page
+
+## Outputs
+
+- Path to the summary file
+
+## Failure Modes
+
+- If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95

skills/research-pipeline/commands.md

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

skills/research-pipeline/examples.md

@@ -0,0 +1,10 @@
+# Examples: research-pipeline
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: URL of the Wikipedia page
+# Process: Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode → Step 2: Require NVIDIA NIM API key using `require_nim_api_key()` → Step 3: Create a Graph instance `g`
+# Outputs: Path to the summary file
+```

skills/research-pipeline/metadata.json

@@ -0,0 +1,28 @@
+{
+  "name": "research-pipeline",
+  "version": "1.0.0",
+  "goal": "Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.",
+  "inputs": [
+    "URL of the Wikipedia page"
+  ],
+  "steps": [
+    "Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode",
+    "Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`",
+    "Step 3: Create a Graph instance `g`",
+    "Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)",
+    "Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node",
+    "Step 6: Add an LLMAgent node with system prompt 'You are a technical writer. Summarise the text in 3 bullet points.' and model NIM_MODEL (`summarise`), connecting its input to the output of `fetcher`",
+    "Step 7: Add a FileWrite node to write the summary to a file named 'summary.txt' (`writer`), connecting its input to the output of `summarise`",
+    "Step 8: Cook the graph starting from the writer node and print the path where the summary is written"
+  ],
+  "outputs": [
+    "Path to the summary file"
+  ],
+  "failure_modes": [
+    "If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow can be adapted to fetch and summarise any text from a URL using an AI agent and save the summary to a file.",
+  "source_repo": "https://github.com/temiroff/Blacknode.git",
+  "score": 1.0
+}

skills/research-pipeline/tests.md

@@ -0,0 +1,9 @@
+# Tests: research-pipeline
+
+## Test Checklist
+
+- [ ] Workflow has at least 3 steps
+- [ ] All inputs are defined
+- [ ] All outputs are defined
+- [ ] Failure modes are documented
+- [ ] Skill can be loaded without errors

skills/unifai-workflow-execution/SKILL.md

@@ -0,0 +1,96 @@
+---
+name: unifai-workflow-execution
+version: 1.0.0
+description: Execute a multi-agent workflow on the UnifAI platform using a specified
+  blueprint and user prompt.
+inputs:
+- blueprint_id or blueprint_name
+- user_shortcut
+- user_question
+steps:
+- 'Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id
+  method)'
+- 'Step 2: Create a new session from the blueprint (create_session method)'
+- 'Step 3: Submit the session for background execution with the user prompt (submit_session
+  method)'
+- 'Step 4: Poll session status until execution completes (poll_session_status method)'
+outputs:
+- session_id
+- workflow_id
+tags: []
+metadata:
+  source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# unifai-workflow-execution
+
+Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install requests urllib3
+```
+
+**Setup steps:**
+
+1. Install required dependencies using pip install requests urllib3
+1. Ensure the environment variables are set correctly (BLUEPRINT_ID, BLUEPRINT_NAME, USER_SHORTCUT, POLLING_INTERVAL, UNIFAI_BASE_URL)
+
+## Key Files
+
+- `scripts/execution_workflow.py - Main script for workflow execution`
+
+## Steps
+
+1. Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method)
+2. Step 2: Create a new session from the blueprint (create_session method)
+3. Step 3: Submit the session for background execution with the user prompt (submit_session method)
+4. Step 4: Poll session status until execution completes (poll_session_status method)
+
+## Implementation Details
+
+```python
+resolve_blueprint_id(client: UnifAIClient) -> str
+{...}
+# Resolve the blueprint ID from either direct ID or name lookup.
+```
+
+```python
+create_session(client: UnifAIClient, blueprint_id: str) -> str
+{...}
+# Create a new session from the blueprint.
+```
+
+```python
+submit_session(client: UnifAIClient, session_id: str) -> dict
+{...}
+# Submit the session for background execution with the user prompt.
+```
+
+## Inputs
+
+- blueprint_id or blueprint_name
+- user_shortcut
+- user_question
+
+## Outputs
+
+- session_id
+- workflow_id
+
+## Failure Modes
+
+- Blueprint name not found or not unique - error during blueprint resolution
+- Session creation fails - error from API response
+- Session submission fails - error from API response
+- Polling session status fails - error from API response
+
+## Source
+
+Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
+Confidence: 0.95

skills/unifai-workflow-execution/commands.md

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

skills/unifai-workflow-execution/examples.md

@@ -0,0 +1,10 @@
+# Examples: unifai-workflow-execution
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: blueprint_id or blueprint_name, user_shortcut, user_question
+# Process: Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method) → Step 2: Create a new session from the blueprint (create_session method) → Step 3: Submit the session for background execution with the user prompt (submit_session method)
+# Outputs: session_id, workflow_id
+```

skills/unifai-workflow-execution/metadata.json

@@ -0,0 +1,30 @@
+{
+  "name": "unifai-workflow-execution",
+  "version": "1.0.0",
+  "goal": "Execute a multi-agent workflow on the UnifAI platform using a specified blueprint and user prompt.",
+  "inputs": [
+    "blueprint_id or blueprint_name",
+    "user_shortcut",
+    "user_question"
+  ],
+  "steps": [
+    "Step 1: Resolve the blueprint ID from either direct ID or name lookup (resolve_blueprint_id method)",
+    "Step 2: Create a new session from the blueprint (create_session method)",
+    "Step 3: Submit the session for background execution with the user prompt (submit_session method)",
+    "Step 4: Poll session status until execution completes (poll_session_status method)"
+  ],
+  "outputs": [
+    "session_id",
+    "workflow_id"
+  ],
+  "failure_modes": [
+    "Blueprint name not found or not unique - error during blueprint resolution",
+    "Session creation fails - error from API response",
+    "Session submission fails - error from API response",
+    "Polling session status fails - error from API response"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is specific to the UnifAI platform and its multi-agent system, but can be adapted for similar systems with a similar architecture.",
+  "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
+  "score": 1.0
+}

skills/unifai-workflow-execution/tests.md

@@ -0,0 +1,9 @@
+# Tests: unifai-workflow-execution
+
+## Test Checklist
+
+- [ ] Workflow has at least 3 steps
+- [ ] All inputs are defined
+- [ ] All outputs are defined
+- [ ] Failure modes are documented
+- [ ] Skill can be loaded without errors