Add test

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):
     """
@@ -33,20 +33,23 @@ def publish_skill(review_result, config):
     branch_name = f"skill/{skill_name}-{ts}"
 
     with tempfile.TemporaryDirectory() as tmpdir:
-        repo_dir = os.path.join(tmpdir, "agent-skills")
-
         # Clone repo
+        repo_dir = os.path.join(tmpdir, "agent-skills")
         result = subprocess.run(
-            ["git", "clone", "--branch", "main", "--depth", "1", clone_url, repo_dir],
+            ["git", "clone", "--branch", "main", "--single-branch", 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", "--depth", "1", clone_url, repo_dir],
+                ["git", "clone", 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)
@@ -56,80 +59,34 @@ def publish_skill(review_result, config):
         skill_dir = os.path.join(repo_dir, "skills", skill_name)
         os.makedirs(skill_dir, exist_ok=True)
 
-        # Write skill files
+        # Write 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)
 
-        # 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
+        # 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
         )
 
-        # 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, branch_name],
-            cwd=repo_dir, capture_output=True, text=True, timeout=30
+            ["git", "push", "-u", auth_url, f"main:{branch_name}"],
+            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",
@@ -140,19 +97,19 @@ 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\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"
+                    f"**Review:** {review_result.get('reason', '')}\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",
@@ -170,6 +127,7 @@ 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

@@ -0,0 +1,21 @@
+{
+  "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

@@ -0,0 +1,21 @@
+{
+  "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

@@ -0,0 +1,21 @@
+{
+  "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

@@ -0,0 +1,21 @@
+{
+  "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

@@ -0,0 +1,21 @@
+{
+  "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

@@ -1,77 +0,0 @@
----
-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

@@ -1,6 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-# 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

@@ -1,24 +0,0 @@
-{
-  "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

@@ -1,9 +0,0 @@
-# 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

@@ -1,98 +0,0 @@
----
-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

@@ -1,6 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-# 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

@@ -1,29 +0,0 @@
-{
-  "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

@@ -1,9 +0,0 @@
-# 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

@@ -1,84 +0,0 @@
----
-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

@@ -1,6 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-# 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

@@ -1,24 +0,0 @@
-{
-  "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

@@ -1,9 +0,0 @@
-# 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

@@ -1,90 +0,0 @@
----
-name: research-pipeline
-version: 1.0.0
-description: Fetch a Wikipedia page, summarise its content 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` and `blacknode`: Run `from _bootstrap
-  import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
-- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
-  API key is set.'
-- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
-- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
-  value=''URL of the Wikipedia page''); 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'')`'
-- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
-  fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
-- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
-  print(f''Summary written to: {result}'')`'
-outputs:
-- Path of 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 its content 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
-1. Install required dependencies: `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` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`
-2. Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.
-3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
-4. Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); 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')`
-5. Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`
-6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
-
-## Implementation Details
-
-```python
-from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
-```
-
-```python
-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')
-```
-
-## Inputs
-
-- URL of the Wikipedia page
-
-## Outputs
-
-- Path of the summary file
-
-## Failure Modes
-
-- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty
-
-## Source
-
-Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
-Confidence: 0.95

skills/research-pipeline/commands.md

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

skills/research-pipeline/examples.md

@@ -1,10 +0,0 @@
-# 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` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn` → Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set. → Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
-# Outputs: Path of the summary file
-```

skills/research-pipeline/metadata.json

@@ -1,26 +0,0 @@
-{
-  "name": "research-pipeline",
-  "version": "1.0.0",
-  "goal": "Fetch a Wikipedia page, summarise its content 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` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`",
-    "Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.",
-    "Step 3: Create a graph instance: Initialize `g = bn.Graph()`.",
-    "Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); 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')`",
-    "Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`",
-    "Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
-  ],
-  "outputs": [
-    "Path of the summary file"
-  ],
-  "failure_modes": [
-    "If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow can be adapted to fetch and summarise any Wikipedia page or similar content source.",
-  "source_repo": "https://github.com/temiroff/Blacknode.git",
-  "score": 1.0
-}

skills/research-pipeline/tests.md

@@ -1,9 +0,0 @@
-# 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/test-skill/SKILL.md

@@ -0,0 +1 @@
+# Test

skills/unifai-workflow-execution/SKILL.md

@@ -1,62 +0,0 @@
----
-name: unifai-workflow-execution
-version: 1.0.0
-description: Execute a multi-agent AI workflow defined in YAML or through the UI's
-  drag-and-drop editor.
-inputs:
-- name: blueprint_path
-  description: Path to the blueprint file (YAML) defining the multi-agent workflow.
-- name: execution_mode
-  description: 'Execution mode: ''local'' or ''distributed''.'
-steps:
-- step_name: Load Blueprint
-  description: Parse and validate the blueprint file to ensure it conforms to expected
-    structure.
-- step_name: Initialize Execution Engine
-  description: Set up the execution engine based on the selected mode ('local' or
-    'distributed').
-- step_name: Execute Workflow
-  description: Run the multi-agent workflow, streaming node-by-node output as NDJSON
-    over HTTP.
-- step_name: Stream Results
-  description: Render and stream results in real time to clients subscribing to the
-    event stream.
-outputs:
-- name: execution_results
-  description: The output of the executed workflow, streamed as NDJSON over HTTP.
-tags: []
-metadata:
-  source_repo: https://github.com/redhat-community-ai-tools/UnifAI.git
-  extracted_at: ''
-  confidence: 0.9
----
-
-# unifai-workflow-execution
-
-Execute a multi-agent AI workflow defined in YAML or through the UI's drag-and-drop editor.
-
-## Steps
-
-1. {'step_name': 'Load Blueprint', 'description': 'Parse and validate the blueprint file to ensure it conforms to expected structure.'}
-2. {'step_name': 'Initialize Execution Engine', 'description': "Set up the execution engine based on the selected mode ('local' or 'distributed')."}
-3. {'step_name': 'Execute Workflow', 'description': 'Run the multi-agent workflow, streaming node-by-node output as NDJSON over HTTP.'}
-4. {'step_name': 'Stream Results', 'description': 'Render and stream results in real time to clients subscribing to the event stream.'}
-
-## Inputs
-
-- {'name': 'blueprint_path', 'description': 'Path to the blueprint file (YAML) defining the multi-agent workflow.'}
-- {'name': 'execution_mode', 'description': "Execution mode: 'local' or 'distributed'."}
-
-## Outputs
-
-- {'name': 'execution_results', 'description': 'The output of the executed workflow, streamed as NDJSON over HTTP.'}
-
-## Failure Modes
-
-- {'mode_name': 'Invalid Blueprint', 'description': 'Blueprint file is not valid YAML or does not conform to expected structure.'}
-- {'mode_name': 'Execution Engine Initialization Failure', 'description': 'Failed to initialize the execution engine due to configuration issues or missing dependencies.'}
-
-## Source
-
-Extracted from: [https://github.com/redhat-community-ai-tools/UnifAI.git](https://github.com/redhat-community-ai-tools/UnifAI.git)
-Confidence: 0.9

skills/unifai-workflow-execution/commands.md

@@ -1,6 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-# Examples: unifai-workflow-execution
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: {'name': 'blueprint_path', 'description': 'Path to the blueprint file (YAML) defining the multi-agent workflow.'}, {'name': 'execution_mode', 'description': "Execution mode: 'local' or 'distributed'."}
-# Process: {'step_name': 'Load Blueprint', 'description': 'Parse and validate the blueprint file to ensure it conforms to expected structure.'} → {'step_name': 'Initialize Execution Engine', 'description': "Set up the execution engine based on the selected mode ('local' or 'distributed')."} → {'step_name': 'Execute Workflow', 'description': 'Run the multi-agent workflow, streaming node-by-node output as NDJSON over HTTP.'}
-# Outputs: {'name': 'execution_results', 'description': 'The output of the executed workflow, streamed as NDJSON over HTTP.'}
-```

skills/unifai-workflow-execution/metadata.json

@@ -1,53 +0,0 @@
-{
-  "name": "unifai-workflow-execution",
-  "version": "1.0.0",
-  "goal": "Execute a multi-agent AI workflow defined in YAML or through the UI's drag-and-drop editor.",
-  "inputs": [
-    {
-      "name": "blueprint_path",
-      "description": "Path to the blueprint file (YAML) defining the multi-agent workflow."
-    },
-    {
-      "name": "execution_mode",
-      "description": "Execution mode: 'local' or 'distributed'."
-    }
-  ],
-  "steps": [
-    {
-      "step_name": "Load Blueprint",
-      "description": "Parse and validate the blueprint file to ensure it conforms to expected structure."
-    },
-    {
-      "step_name": "Initialize Execution Engine",
-      "description": "Set up the execution engine based on the selected mode ('local' or 'distributed')."
-    },
-    {
-      "step_name": "Execute Workflow",
-      "description": "Run the multi-agent workflow, streaming node-by-node output as NDJSON over HTTP."
-    },
-    {
-      "step_name": "Stream Results",
-      "description": "Render and stream results in real time to clients subscribing to the event stream."
-    }
-  ],
-  "outputs": [
-    {
-      "name": "execution_results",
-      "description": "The output of the executed workflow, streamed as NDJSON over HTTP."
-    }
-  ],
-  "failure_modes": [
-    {
-      "mode_name": "Invalid Blueprint",
-      "description": "Blueprint file is not valid YAML or does not conform to expected structure."
-    },
-    {
-      "mode_name": "Execution Engine Initialization Failure",
-      "description": "Failed to initialize the execution engine due to configuration issues or missing dependencies."
-    }
-  ],
-  "confidence": 0.9,
-  "explanation": "This workflow is designed to execute multi-agent AI workflows defined in YAML blueprints or through the UI's drag-and-drop editor, providing real-time streaming of results.",
-  "source_repo": "https://github.com/redhat-community-ai-tools/UnifAI.git",
-  "score": 1.0
-}

skills/unifai-workflow-execution/tests.md

@@ -1,9 +0,0 @@
-# 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