oh-my-pi

Coordinate split-view main-agent and sub-agent workflows using a shared file-based command queue. Works with ANY CLI IDE that can read/write files and invoke skills. One instance assumes the main-agent role (coordinator); N other instances take on sub-agent roles (workers). Use when the user wants to run a side sub-agent in parallel to the main agent, dispatch research or exploration tasks to sub-agents, poll for sub-agent results, or manage multi-agent split-view sessions. Trigger on "run side agent", "launch sub-agent", "main-agent mode", "sub-agent mode", "split view workflow", "coordinate sub-agent", or when setting up agent-to-agent task delegation.

This skill should be used when the user asks to "compare candidate patches", "rank multiple solutions", "choose between implementations", "verify two drafts", "pick the best trajectory", "use llm-as-a-verifier", or wants repeated criteria-decomposed LLM verification instead of a single free-form judgment.

Run the QuickScope stage of qs-parallelprd: turn a messy objective, findings list, repo issue, or research-backed gap analysis into an evidence-rooted A-to-Z lane plan with root cause, lane ownership, dependencies, parallel windows, serialization rules, effort, and targeted acceptance gates.

A relentless interview to sharpen a plan or design.

Interview the user relentlessly about a plan or design. Use when the user wants to stress-test a plan before building, or uses any 'grill' trigger phrases.

Compact the current conversation into a handoff document for another agent to pick up.

Teach the user a new skill or concept, within this workspace.

Reference for writing and editing skills well — the vocabulary and principles that make a skill predictable.

Use when designing, configuring, or running Oh My Pi subagent orchestration: one visible endpoint over task/quick_task/oracle/reviewer/explore agents, deterministic route planning, parallel waves, recursive delegation caps, independent verification, and final synthesis without exposing internal routing. Also use when invoking the verifier-extension `subagent_orchestrator_plan` tool to compute an OMP-native route plan.

Run fmh (fusion-meta-harness) — verifier fusion, evaluate-verifier, improve-verifier, promote, run-task. Orchestrates multi-agent candidate generation, rubric scoring, synthesis, and reliability gates for the pi-llm-as-verifier project.

This skill should be used when the user asks to "compare candidate patches", "rank multiple solutions", "choose between implementations", "verify two drafts", "pick the best trajectory", "use llm-as-a-verifier", or wants repeated criteria-decomposed LLM verification instead of a single free-form judgment.

This skill should be used when the user wants to spawn subagents with different models, configure heterogeneous candidate backends, implement dynamic model routing, or run multi-model fusion workflows. Covers mock, local, premium API (Anthropic/OpenAI), budget API (Kimi/MiniMax/Qwen), routing layers (9Router), and CLI backends.

Optimize the description prompts an AI agent reads to learn its built-in tools (the `.md` files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool prompt vs what stays in code. Use when auditing, trimming, writing, or reviewing tool prompts, deciding what schema field descriptions already cover, or testing schema-vs-prompt overlap before deleting prompt lines.