Decompose long sequential tasks into minimal single-step subtasks with voting.
npx skills add https://github.com/metodn/temp03 --skill maker-methodology
Please help me install this Agent Skill. Skill: maker-methodology Source: https://github.com/metodn/temp03/tree/main/.claude/skills/maker-methodology Command: npx skills add https://github.com/metodn/temp03 --skill maker-methodology
This Skill provides a structured methodology (MAKER) to solve extremely long sequential tasks by decomposing into single-step subtasks, orchestrating multiple lightweight agents, and using voting plus red-flagging to minimize errors. It helps teams design scalable problem-solving workflows that reduce cognitive load and time to solution.
To apply this methodology, ask your AI to follow the MAKER loop, starting with problem decomposition and then iterative voting until completion.
High-intent search queries and answers about installing and using this skill.
Task decomposition reduces error accumulation by splitting complex problems into minimal single-step subtasks. MAKER methodology structures this by defining a task interface (state, actions, completion checks) and orchestrating multiple agents through voting to reach consensus on each step, minimizing mistakes in long workflows.
First-to-ahead-by-k voting achieves consensus among multiple agents by selecting the first solution that reaches a majority lead. This approach corrects individual agent errors early in long sequential tasks, improving reliability without requiring all agents to agree on every decision.
Use MAKER for long-form reasoning, multi-step planning, or code regeneration tasks where a single monolithic solver degrades over many sequential decisions. It's suited to problems with observable partial states like Sudoku-like planning, code refactoring across files, route planning, and data pipeline assembly.
Red-flagging detects and discards unreliable or malformed results before they propagate downstream. Combined with voting, it filters out erroneous agent outputs early, reducing cascading failures in multi-step sequential workflows.
Yes, MAKER applies to any multi-agent system using LLMs or lightweight agents. The methodology requires implementing a task interface (initialize_state, get_current_state, get_possible_actions, apply_action, is_complete, get_progress, format_for_llm) to orchestrate agents through voting and red-flagging.
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choam2426Coordinate multi-agent voting with challenger participation and structured decision outcomes.
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