Learning Outcome Prediction Agent

What Learning Outcome Prediction Agent is

Learning Outcome Prediction Agent is a subagent published in the ChrisRoyse/610ClaudeSubagents repository on GitHub, which has about 117 stars.

A subagent is a specialist assistant that Claude can hand part of a task to. It is a markdown file whose frontmatter sets a name, a description that tells Claude when to delegate, and optionally the tools and model it may use; the body becomes the subagent's own system prompt.

Because a subagent works in its own context, it keeps the main conversation focused: Claude can send a narrow job, such as a review or a specialised analysis, to Learning Outcome Prediction Agent and get back a compact result.

It is set up to use these tools: Read, Write, Edit, MultiEdit, Grep, Glob, Bash, WebSearch, WebFetch, Task, TodoWrite. Limiting tools is a good sign: the subagent can only do what those tools allow.

How to install Learning Outcome Prediction Agent

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

The repository does not declare an open-source license, so we list only the outline of the file here. Read the full text on GitHub.

• ALWAYS CLOSELY INSPECT THE RESULTS OF SUBAGENTS AND MAKE SURE THEY AREN'T LIEING AND BEING HONEST AND TRUTHFUL.
• Learning Outcome Prediction Agent
• Core Competencies
• Task Breakdown & QA Loop
• Integration Patterns
• Quality Metrics & Assessment Plan
• Success Criteria (100/100 Completion)
• Integration Points
• Use Cases & Deployment Scenarios
• Principle 0 Compliance
• Quality Loop Protocol
• Advanced Learning Analysis Capabilities

Open learning-outcome-prediction-agent.md on GitHub

Before you install

• Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
• Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
• Try it in a test project or a copy of your files before pointing it at real work.
• Pin the version you tested, and review changes before updating.
• Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ