@agenticup/pi-loop

This Skill addresses the issue where AI often lacks sufficient reasoning depth and tends to miss critical details when handling complex tasks. In practical development, developers frequently need to tackle tasks such as architectural decision-making, multi-step reasoning, code review, or research synthesis, where a single prompt often yields superficial, unsystematic outputs. pi-loop implements a five-stage recursive loop mechanism, enabling the AI to decompose problems, solve them in parallel, conduct cross-reviews, iterate for optimization, and finally synthesize a complete answer—much like a human expert.

Usage is straightforward: simply install the extension in the Pi environment and trigger it via natural language instructions. For example, instruct the AI with "use loop: design an authentication system for a SaaS application." It will automatically enter the first stage: decomposing the task into 8-15 micro-subproblems (e.g., token bucket algorithm selection, Redis backend storage, rate-limiting header formats, etc.), followed by a DRIP reverse check to supplement missing premises. It then uses semaphore-controlled concurrent solving, adaptive voting review (scaling from 1 to 3 reviewers), iterative decomposition of flagged subproblems, and finally synthesizes the result via DRAGON-style conflict detection. The entire process displays real-time progress, with a 20-minute timeout per subproblem and graceful degradation upon failure.

It is particularly well-suited for teams and individuals requiring deep analysis, multi-step reasoning, or complex decision-making—especially those already using Pi as an AI coding agent and regularly handling tasks like architectural design, code review, research synthesis, and competitive analysis. For simple lookups, quick Q&A, or single-file editing, this tool is unnecessary.

It is recommended to consistently use this tool for tasks requiring multi-perspective thinking, cross-domain knowledge integration, or high-reliability demands. Note that each additional level of recursion roughly doubles the cost; the default depth is 2, with a maximum of 3. Adjust based on task importance. Additionally, it has a strong dependency on the Pi environment and cannot run independently on other AI platforms.