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joeseesunRefining vague agent tasks into executable goals
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This project addresses the common issue of AI agent task failures stemming from vague or poorly defined objectives. It targets developers and users interacting with AI agents like Codex, transforming ambiguous requests into precise, actionable /goal commands. The primary benefit is significantly increasing the reliability and success rate of AI-driven tasks by providing structured, executable instructions.
How It Works
The qiaomu-goal-meta-skill acts as a pre-processor for AI agent commands. It takes a user's high-level, often vague, requirement and systematically expands it into a detailed /goal command. This command includes explicit definitions for outcome, verification procedures, operational constraints, scope boundaries, iteration policies, completion criteria, and pause conditions. The skill prioritizes generating a conservative, directly executable default version of the goal, complete with explanations, and offers numbered options for customization, ensuring clarity and reducing ambiguity for the AI.
Quick Start & Requirements
npx skills add joeseesun/qiaomu-goal-meta-skill~/.agents/skills.Highlighted Details
/goal command by default, eliminating the need for manual form-filling.scripts/lint_goal_command.py) to validate generated /goal commands against common issues.Maintenance & Community
The project is authored by 向阳乔木 (Vista8). While direct community links like Discord or Slack are not provided, the author's GitHub and X (formerly Twitter) profiles are available for contact.
Licensing & Compatibility
The project is released under the MIT License, permitting commercial use and modification.
Limitations & Caveats
This skill exclusively generates /goal commands; it does not execute the goals themselves. It is designed not to bypass restrictions related to copyright, accounts, payments, production data, or compliance. It defaults to generating Minimum Viable Product (MVP) level goals, requiring explicit user requests for full product strategy, commercialization, or deployment details. For unfamiliar professional domains, it defaults to a "discovery-first" approach rather than assuming expertise.
1 week ago
Inactive
agentcoinorg