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AI EngineeringTypeScript

Cross-Task Learner

Cross-Task Learner is an AI Engineering skill for Claude Code, published by jmagly in aiwg.

209 stars32 forkson jmagly/aiwgAdded 2026/09/04+1% in starsRepository updated 2026/09/03

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agentic-codingai-frameworkanthropicautonomous-agentsclaude-codecodexcopilotcursordeveloper-toolsfine-tuningmulti-agentmulti-platformorchestrationprompt-engineeringsdlctraining-datawarpworkflow-automation

Install in seconds

Install Cross-Task Learner

Copy Cross-Task Learner into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/agent-loop/skills/cross-task-learner ~/.claude/skills/cross-task-learner

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/jmagly/aiwg.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: agentic/code/addons/agent-loop/skills/cross-task-learner/SKILL.md in jmagly/aiwg
Installs to: ~/.claude/skills/cross-task-learner
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2970 skills

What Cross-Task Learner does

Cross-Task Learner extracts reusable patterns from completed agent loops and injects relevant ones into new loops. It helps agents reuse fixes, success paths, anti-patterns, and code templates across tasks.

Cross-Task Learner is cataloged under AI Engineering on DirSkills. Cross-Task Learner comes from a repository tagged agentic-coding, ai-framework, anthropic, autonomous-agents and claude-code.

Documentation

README

Excerpt

Cross-Task Learner Skill

Enable agent loops to learn from similar past tasks and share discovered patterns across multiple concurrent or sequential loops.

Research Foundation: REF-013 MetaGPT - 159% improvement with shared state

Version 2.0: Multi-loop awareness with loop_id tracking

Overview

This skill provides two core capabilities:

Pattern Extraction - On loop completion, extract reusable patterns from execution history
Pattern Injection - On loop start, inject relevant patterns from previous loops

Benefits

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Cross-Task Learner

What else does jmagly publish alongside Cross-Task Learner?
Cross-Task Learner is one of 25 skills that DirSkills catalogs from jmagly/aiwg, the repository it ships in. Its siblings there include Agent Loop, Agent Loop and Agent Loop External. Each one is a separate skill with its own page in this directory, installs the same way Cross-Task Learner does, and is maintained by jmagly in that same repository. The rest of the collection is listed on the jmagly/aiwg page.
How does Cross-Task Learner compare to other AI Engineering skills?
Cross-Task Learner ranks #2710 by stars among the 2970 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Cross-Task Learner against them. Open each page to compare what they document and how they install.

More from jmagly/aiwg

Cross-Task Learner is one of 25 skills cataloged on DirSkills from jmagly/aiwg.

See all 25 skills →

Agent Loop

Agent Loop detects requests for iterative autonomous task execution and routes them to the right loop executor. Use it when a user wants to keep trying, fix until green, or run an explicit agent loop.

Agent Loop

Agent Loop executes a task in repeated cycles until verifiable completion criteria are met. Use it when you want an agent to keep trying, learn from failures, and report the final status.

Agent Loop External

Agent Loop External launches a long-running agent loop outside the current session with saved state. Use it for crash recovery, parallel runs, and CI/CD-backed tasks that need resumable execution.

Agent Persistence

Agent Persistence defines reusable human-in-the-loop gates for persistent agent loops. Use it when you need authorization for destructive actions, policy overrides, or recovery escalation.

Al External

Al External launches long-running iterative loops outside the AI session with saved state. Use it for tasks that need crash recovery, parallel runs, or CI/CD integration.

Al Memory

Al Memory lets you list, query, and clear semantic memory entries from loop iterations. Use it to review lessons learned, failure patterns, and success patterns across runs.

More AI Engineering skills

Cross-Task Learner is one of 2970 AI Engineering skills cataloged on DirSkills, ranked #2710 by stars among them.

Browse all AI Engineering skills →

Architecture Decision Records

Architecture Decision Records captures architectural decisions made during Claude Code sessions as structured ADRs, recording context, alternatives, and rationale. Use it when significant technical choices are made so future developers understand why the codebase is shaped the way it is.

AI-First Engineering

AI-First Engineering defines process, review, and architecture standards for teams where AI agents produce most implementation output. Use it to set ownership rules, quality gates, and evaluation criteria for agent-generated code.

Agentic OS

Agentic OS codifies a persistent multi-agent operating system architecture for Claude Code. Use it when building a multi-agent workflow with file-based memory, slash commands, and scheduled automation that survives session restarts.