Add Skill: code-review-agent

Extracted from: https://github.com/itszhaoziyan-n/AgentKit.git
Score: 1.0

skills/code-review-agent/SKILL.md

@@ -0,0 +1,76 @@
+---
+name: code-review-agent
+version: 1.0.0
+description: Automate code review process using a multi-step workflow with human-in-the-loop
+  approval.
+inputs:
+- Repository diff or code changeset (string)
+- User ID (string)
+steps:
+- 'Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`'
+- 'Step 2: Invoke the graph with initial parameters including the repository diff
+  and user ID, and set thread_id as a configurable parameter'
+- 'Step 3: The graph processes the input through various steps until completion or
+  human approval is needed'
+outputs:
+- Review result (dictionary containing messages, issues, etc.)
+tags: []
+metadata:
+  source_repo: https://github.com/itszhaoziyan-n/AgentKit.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# code-review-agent
+
+Automate 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
+```
+
+**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 - Defines the code review graph and its invocation method.`
+
+## Steps
+
+1. Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`
+2. Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter
+3. Step 3: The graph processes the input through various steps until completion or human approval is needed
+
+## Implementation Details
+
+```python
+graph = build_graph()
+thread_id = str(uuid.uuid4())
+result = graph.invoke(...)
+```
+
+## Inputs
+
+- Repository diff or code changeset (string)
+- User ID (string)
+
+## Outputs
+
+- Review result (dictionary containing messages, issues, etc.)
+
+## Failure Modes
+
+- Specific failure scenario with mitigation: If the graph invocation fails due to an unexpected state, it will halt and require manual intervention.
+
+## Source
+
+Extracted from: [https://github.com/itszhaoziyan-n/AgentKit.git](https://github.com/itszhaoziyan-n/AgentKit.git)
+Confidence: 0.95

skills/code-review-agent/commands.md

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

skills/code-review-agent/examples.md

@@ -0,0 +1,10 @@
+# Examples: code-review-agent
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: Repository diff or code changeset (string), User ID (string)
+# Process: Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph` → Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter → Step 3: The graph processes the input through various steps until completion or human approval is needed
+# Outputs: Review result (dictionary containing messages, issues, etc.)
+```

skills/code-review-agent/metadata.json

@@ -0,0 +1,24 @@
+{
+  "name": "code-review-agent",
+  "version": "1.0.0",
+  "goal": "Automate code review process using a multi-step workflow with human-in-the-loop approval.",
+  "inputs": [
+    "Repository diff or code changeset (string)",
+    "User ID (string)"
+  ],
+  "steps": [
+    "Step 1: Build the code review graph using `build_graph()` from `agentkit.workflow.code_review.graph`",
+    "Step 2: Invoke the graph with initial parameters including the repository diff and user ID, and set thread_id as a configurable parameter",
+    "Step 3: The graph processes the input through various steps until completion or human approval is needed"
+  ],
+  "outputs": [
+    "Review result (dictionary containing messages, issues, etc.)"
+  ],
+  "failure_modes": [
+    "Specific failure scenario with mitigation: If the graph invocation fails due to an unexpected state, it will halt and require manual intervention."
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow is reusable for any code review process that requires a multi-step analysis with human-in-the-loop approval.",
+  "source_repo": "https://github.com/itszhaoziyan-n/AgentKit.git",
+  "score": 1.0
+}

skills/code-review-agent/tests.md

@@ -0,0 +1,9 @@
+# Tests: code-review-agent
+
+## 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

@@ -1,27 +1,26 @@
 ---
 name: research-pipeline
 version: 1.0.0
-description: Fetch a Wikipedia page, summarise it using an AI agent, and write the
+description: Fetch a Wikipedia page, summarise its content using an AI agent, and
-  summary to a file.
+  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)
+- 'Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap
-  and blacknode'
+  import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
-- 'Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`'
+- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
-- 'Step 3: Create a Graph instance `g`'
+  API key is set.'
-- 'Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)'
+- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
-- 'Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect
+- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
-  it to the Literal node'
+  value=''URL of the Wikipedia page''); fetcher = g.node(''HTTPGet''); summarise =
-- 'Step 6: Add an LLMAgent node with system prompt ''You are a technical writer. Summarise
+  g.node(''LLMAgent'', system=''You are a technical writer. Summarise the text in
-  the text in 3 bullet points.'' and model NIM_MODEL (`summarise`), connecting its
+  3 bullet points.'', model=NIM_MODEL); writer = g.node(''FileWrite'', path=''summary.txt'')`'
-  input to the output of `fetcher`'
+- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
-- 'Step 7: Add a FileWrite node to write the summary to a file named ''summary.txt''
+  fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
-  (`writer`), connecting its input to the output of `summarise`'
+- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
-- 'Step 8: Cook the graph starting from the writer node and print the path where the
+  print(f''Summary written to: {result}'')`'
-  summary is written'
 outputs:
-- Path to the summary file
+- Path of the summary file
 tags: []
 metadata:
   source_repo: https://github.com/temiroff/Blacknode.git
@@ -31,7 +30,7 @@ metadata:
 
 # research-pipeline
 
-Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.
+Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.
 
 ## Setup
 
@@ -43,8 +42,8 @@ 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. Ensure NVIDIA NIM API key is set in the environment or editor
-1. Install required dependencies using `pip install -r requirements.txt`
+1. Install required dependencies: `pip install -r requirements.txt`
 
 ## Key Files
 
@@ -52,39 +51,25 @@ pip install anthropic>=0.25 docker>=7.1 openai>=1.0
 
 ## Steps
 
-1. Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode
+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 using `require_nim_api_key()`
+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 `g`
+3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
-4. Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)
+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: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node
+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: 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`
+6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
-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
+from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
-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')
-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')
+url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
-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
@@ -93,11 +78,11 @@ print(f'Summary written to: {result}')
 
 ## Outputs
 
-- Path to the summary file
+- Path of the summary file
 
 ## Failure Modes
 
-- If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly
+- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty
 
 ## Source
 

skills/research-pipeline/examples.md

@@ -5,6 +5,6 @@
 ```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`
+# 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 to the summary file
+# Outputs: Path of the summary file
 ```

skills/research-pipeline/metadata.json

@@ -1,28 +1,26 @@
 {
   "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.",
+  "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 (NIM_MODEL, require_nim_api_key) and blacknode",
+    "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 using `require_nim_api_key()`",
+    "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 `g`",
+    "Step 3: Create a graph instance: Initialize `g = bn.Graph()`.",
-    "Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)",
+    "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: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node",
+    "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: 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 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
-    "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"
+    "Path of the summary file"
   ],
   "failure_modes": [
-    "If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly"
+    "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 text from a URL using an AI agent and save the summary to a file.",
+  "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
 }