skill-navigator

Provides a highly visual and interactive dashboard for OpenClaw users to easily understand and recall the functionalities of installed skills, featuring a vi...

Security Scan

The skill's code and instructions are consistent with a dashboard/inventory purpose: it scans local SKILL.md files under /home/ubuntu/skills, heuristically maps capabilities, and renders a template—no network requests, no extra credentials, and no elevated persistence requested.

Purpose & Capability

The skill claims to build a visual dashboard of installed skills and its script scans SKILL.md files to extract metadata and compute capability scores. Reading skill metadata under a local skills directory is coherent with the stated purpose. Minor inconsistency: SKILL.md frontmatter name is 'skill-dashboard-visualizer' while the registry slug is 'skill-navigator'—this is likely a naming mismatch but does not change behavior.

Instruction Scope

Instructions explicitly tell the agent to run scripts that read SKILL.md files under /home/ubuntu/skills. That is within the expected scope for an inventory/dashboard tool. Note: the SKILL.md uses an absolute path (/home/ubuntu/skills/skill-dashboard-visualizer/...), which assumes where skills are installed; if the runtime stores skills elsewhere the provided commands may fail. Also the dashboard will surface metadata from other skills' SKILL.md files—if those files accidentally contain secrets, those will be read and included in the generated output.

Install Mechanism

No install spec and no network/downloads are present. The skill is instruction+script-only and uses only standard Python libraries. This is low-risk from an installation standpoint.

Credentials

The skill does not request environment variables, credentials, or config paths. Its access is limited to reading files in /home/ubuntu/skills, which matches the declared purpose.

Persistence & Privilege

always is false and model invocation is allowed (default). The skill does not request permanent presence or modify other skills' configurations. No elevated privileges are requested.

Assessment

This skill appears to do what it says: scan local SKILL.md files and render a dashboard. Before installing or running it, check two things: (1) verify that your runtime actually stores skills at /home/ubuntu/skills or edit the script/commands to the correct path; (2) ensure none of your existing SKILL.md files contain secrets or sensitive config (they usually shouldn't), because the dashboard will read and surface those files' metadata. If you want extra caution, run the script in a sandboxed environment first and inspect its output before giving it broad access to your production skills directory.

Skill Dashboard Visualizer

This skill is designed to enhance the OpenClaw user experience by providing a clear, interactive, and visually appealing dashboard that summarizes the capabilities of all installed skills. It addresses the common pain point of users finding it difficult to remember and utilize the full potential of their diverse skill set.

Core Features

This skill integrates three key functionalities to offer a holistic view and interaction model for managing skills:

1. Visual Dashboard (可视化看板):

   • Purpose: To present a quick, at-a-glance overview of each installed skill.
   • Mechanism: Automatically extracts metadata (name, description) from SKILL.md files. Each skill is represented as a card with an icon, category tags, and a concise summary of its core abilities. The design adheres to a "blue tech style" for a modern and professional aesthetic.

2. Capability Map (能力矩阵):

   • Purpose: To illustrate the collective and individual strengths of installed skills across various domains.
   • Mechanism: Utilizes a radar chart to visualize skill coverage in key capability areas such as Data Processing, Creative Writing, Technical Development, Logical Reasoning, and Communication. This helps users identify skill gaps or overlaps.

3. Contextual Prompting (智能联想提示):

   • Purpose: To proactively suggest relevant skills based on user input, making skill discovery and activation seamless within OpenClaw or other Claw-like applications.
   • Mechanism: Provides a retrieval mechanism for OpenClaw or current Claw-like applications to quickly match the most suitable installed skills when a user asks a question, prompting the user with options like: "您已安装的 [Skill名称] 具备处理此任务的能力，是否启用？"

Usage Instructions

To generate the skill dashboard, follow these steps:

1. Scan Installed Skills: Execute the scan_skills.py script to gather data on all skills present in the /home/ubuntu/skills/ directory. This script parses each SKILL.md file to extract necessary metadata and performs a preliminary heuristic mapping of capabilities.

   python3 /home/ubuntu/skills/skill-dashboard-visualizer/scripts/scan_skills.py
   

   The output will be a JSON array containing information for each skill, including its name, description, and a calculated capabilities score across different dimensions.

2. Generate Dashboard Visualization: Use the dashboard_template.md along with the data obtained from scan_skills.py to render the final dashboard. The template is designed to dynamically populate the visual dashboard, capability map (mermaid radar chart), and contextual prompting examples.

   The dashboard_template.md expects placeholders to be replaced with actual skill data. For the visual dashboard table, iterate through the scanned skill data. For the capability map, aggregate the capability scores from all skills to form a combined radar chart dataset. For contextual prompting, identify the top skills for each capability dimension.

   Example of data integration for the template (conceptual):

   import json
   import os
   
   # Assume skills_data is obtained from scan_skills.py
   # skills_data = json.loads(shell_output_from_scan_skills)
   
   template_path = "/home/ubuntu/skills/skill-dashboard-visualizer/templates/dashboard_template.md"
   with open(template_path, "r", encoding="utf-8") as f:
       template_content = f.read()
   
   # Populate Visual Dashboard table (simplified example)
   dashboard_table_rows = []
   for skill in skills_data:
       icon = "💡" # Placeholder, ideally based on skill type
       name = skill.get("name", "N/A")
       description = skill.get("description", "N/A")
       use_cases = "" # Derive from description or specific tags
       dashboard_table_rows.append(f"| {icon} | **{name}** | {description} | {use_cases} |")
   
   # Replace placeholder in template
   # template_content = template_content.replace("| {{icon}} | **{{name}}** | {{description}} | {{use_cases}} |", "\n".join(dashboard_table_rows))
   
   # Populate Capability Map (simplified aggregation)
   total_data = sum(s["capabilities"].get("Data", 0) for s in skills_data)
   total_creative = sum(s["capabilities"].get("Creative", 0) for s in skills_data)
   total_tech = sum(s["capabilities"].get("Technical", 0) for s in skills_data)
   total_logic = sum(s["capabilities"].get("Logic", 0) for s in skills_data)
   total_comm = sum(s["capabilities"].get("Communication", 0) for s in skills_data)
   
   # template_content = template_content.replace("data: [{{data_score}}, {{creative_score}}, {{tech_score}}, {{logic_score}}, {{comm_score}}]",
   #                                           f"data: [{total_data}, {total_creative}, {total_tech}, {total_logic}, {total_comm}]")
   
   # Further replacements for contextual prompting...
   
   # Final rendered_dashboard_md can then be displayed or saved.
   

Bundled Resources

• scripts/scan_skills.py: A Python script to scan the /home/ubuntu/skills/ directory, parse SKILL.md files, extract metadata, and heuristically map skill capabilities.
• templates/dashboard_template.md: A Markdown template for generating the visual dashboard, including placeholders for skill information, a Mermaid radar chart for capability mapping, and examples for contextual prompting.

Design Considerations

• UI/UX: The dashboard is designed with a "blue tech style" aesthetic, ensuring a clean, modern, and professional look that aligns with user preferences for web applications.
• Extensibility: The scan_skills.py script can be easily extended to include more sophisticated parsing logic or integrate with a more robust capability taxonomy.
• Interactivity: While the initial output is Markdown, the design is conducive to being rendered into an interactive web interface (e.g., using React components for cards and a charting library for the radar graph) for a richer user experience.