Compose technology-specific agent identity and patterns. Invoke before spawning agents (Developer, SSE, QA, Tech Lead, RE, Investigator) to enhance expertise based on project stack. Returns composed specialization block with token budgeting.
The specialization-loader skill is designed to compose technology-specific identities and patterns for agents based on the detected project stack. It is invoked before spawning specialized agents such as Developers, SSEs, QA Experts, Tech Leads, Requirements Engineers, or Investigators. This skill enables the tailoring of agent behavior by providing specialized knowledge based on the project's tech stack, ensuring that the agents are aligned with the project's requirements. The skill integrates with the orchestration system, enhancing the expertise of the agents with token budgeting and version-aware adaptation.
The main capabilities of the specialization-loader skill include the parsing of input context, which can either be provided as text or read from a session-specific JSON file. It ensures that the correct version of technology stack information is extracted, and it applies version guards to adapt to different tech versions. Additionally, the skill reads the project context from a predefined file or falls back to inline detection of versioning information from common configuration files like package.json, pyproject.toml, and others. The goal is to compose a specialization block that helps the agents execute tasks more effectively and accurately, based on the project's tech specifications.
Target users for this skill include developers, DevOps engineers, and other team members who are responsible for orchestrating the deployment and configuration of specialized agents within a project. This skill helps ensure that agents perform their tasks with the appropriate context and expertise by leveraging the technology stack in use, making it an essential tool in a project with complex or varied requirements.
Invoke the specialization-loader skill before spawning specialized agents such as Developers, SSEs, QA Experts, Tech Leads, Requirements Engineers, or Investigators. Ensure that the specializations array is not empty and that skills_config.json has specializations.enabled set to true.
If project_context.json is missing, the skill will perform fallback detection by reading common configuration files (like package.json, pyproject.toml, etc.) to detect versioning information and create a temporary context.
If the session_id is not provided, the skill will output an error and stop, as it cannot save the skill output to the database without the session_id.
Yes, the skill supports a variety of technology stacks, including Node.js, Python, Go, Java, and others. It detects versions from common configuration files to adapt to different environments.
If the orchestrator does not provide a Testing Mode, the skill will default to 'full' testing mode.
Quick Setup:
.claude/skills/Repository
mehdic/bazinga