ilm-alan-claude-loop

Software architecture patterns skill for designing maintainable, testable applications with clear separation of concerns.

200 skills

Collection of plugins for code analysis, architecture review, SOLID principles, and distributed systems design.

113 skills

Master repository for tech-specific plugins covering SDKs, frameworks, and platforms.

104 skills

Comprehensive development environment featuring specialized AI agents for code review, security analysis, and technical leadership. Designed for engineering teams managing complex codebases.

100 skills

In-code documentation tool for generating README files, JSDoc comments, and code organization guides automatically.

66 skills

Curated collection of high-quality Claude Code plugins built for production development workflows.

63 skills

Interactive self-referential AI loops for iterative development. Claude works on the same task repeatedly, seeing its previous work, until completion.

3 commands · hooks

Interactive self-referential AI loops for iterative development, implementing the Ralph Wiggum technique. Claude works on the same task repeatedly, seeing its previous work, until completion.

3 commands · hooks

Guides developers from brand discovery through iterative design to deployment-ready HTML via Stitch.

1 skill

Build, launch, grade, and schedule Claude Managed Agents (CMA) in your own Anthropic account — a plugin re-implementation of Anthropic's launch-your-agent reference skill (Apache-2.0; independent, not a fork). Every session starts with a goal (./my-agent/goal.json, opt-in SessionStart hook, driven by /cs-goal); loop_compiler.py compiles it into a bounded grade->iterate loop (max_iterations 1..20), a recurring cron scheduled-deployment loop, or a single-pass interview->stage->launch workflow. 6 skills: orchestrator (context: fork goal router) + interview + stage-launch + grade-iterate + run-without-you + wrap-up. 18 stdlib-only deterministic scaffolders (no network/API calls; live launches emitted as BYOK curl that never prints the key), 4 agents, 8 commands, hooks, 5 references, 4 assets. Validators enforce CMA limits. Distinct from engineering/agent-harness (generic domain loop) and write-a-skill (authors Claude Code skills, not CMAs).

6 skills · 8 commands · 4 agents · hooks

Brainstorm, plan, debug, review, and compound learnings with AI agents

39 skills

Skills for adding DeepEval evaluations, tracing, datasets, Confident AI reports, and iterative improvement loops to AI applications.

3 skills