超级目标 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
超级目标 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
超级目标 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 克隆仓库 git clone https://github.com/morphaxl/ultragoal cd ultragoal # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 ultragoal --help # 基本运行 ultragoal [options] <input> # 详细使用说明请查阅文档 # https://github.com/morphaxl/ultragoal
# ultragoal 配置说明 # 查看配置选项 ultragoal --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export ULTRAGOAL_CONFIG="/path/to/config.yml"
Tell Claude what you want once. It works until the job is verifiably done — and it gets smarter every time.
npx ultragoal
An interactive installer walks you through it: choose Claude Code, Codex, or both. Claude Code remains the default for non-interactive installs (--yes) and can install to this project by default (it lands in .claude/settings.json, so teammates get it through git) or machine-wide with --global. If you pick Claude Code, the installer can also pre-configure the repo: the seven working-style questions, .ultragoal/, and the managed CLAUDE.md block. --codex installs the Codex hook-backed goal loop plugin; --all installs both; uninstall removes the selected plugins and marketplace entries. Prefer the Claude route directly? Inside Claude Code:
/plugin marketplace add shamilkayal/ultragoal
/plugin install ultragoal@ultragoal
Want it available in every project on your machine instead of just this one?
npx ultragoal --global
Fable 5 already handles long-horizon work — why add a harness? Not to make the model capable; the loop primitives are native and the model is built for them. Two things survive that fact. First, the published workflow still assumes expertise: in Anthropic's own experiments the engineer hand-writes the nine-criteria rubric, knows to spawn a fresh-context verifier, and runs a memory discipline — ultragoal does those for you. Second, one problem is structural, not a capability gap: a worker grading its own work fails in every model generation, which is why Anthropic's guidance reaches for an independent verifier with Fable 5 specifically. The gate makes that separation mechanical instead of habitual.
Do I need to know how to prompt? No — that's the point. You bring what only you know (what you want, who it's for, what must not break); ultragoal writes the expert-grade brief for itself. You review a plan in plain English, never author a prompt.
Versus ralph-loop? Ralph re-feeds the same prompt until a promise appears. Ultragoal adds the parts the article argues matter: a rubric with per-item check commands, an independent verifier, persistent cross-session goals, and enforced distillation into memory.
Versus Managed Agents "Outcomes"? Anthropic's API-side Managed Agents now offer Outcomes — a hosted define-rubric → iterate → independent-grader loop. Same thesis, different home: Outcomes runs server-side per agent request; ultragoal runs in your repo, on any Claude Code (or Codex) session, writes everything as diffable markdown you own, spans sessions and days, and compounds through git-shared memory — plus it authors the rubric for you from a ramble instead of asking you to supply one. If your workload lives in the Managed Agents API, use Outcomes; if it lives in your editor and CI, this is that loop, portable.
Does the verifier have its own context, or does it grade in the same conversation? Its own. The verifier is a separate subagent with a fresh context window and no access to the worker's reasoning — it only sees the goal file and what it learns by re-running the checks itself. (It does share the Claude Code process and permissions; for absolute isolation on high-stakes work, run /ultragoal:verify from a separate headless session as documented in that skill.)
How do I change my setup answers later? They're just markdown: edit .ultragoal/config.md directly (flip verification to off, change scope, anything), or re-run /ultragoal:setup to be re-asked interactively. Changes apply to the next goal you arm.
Does it spend a lot of tokens? The gate itself is free (no model call). The loop spends what the work needs — that's the point of goal-directed runs. Budgets cap the blast radius — pick the depth tier when arming (a quick pass for your first goal) to calibrate. And for execution-heavy goals there's an economy dial, offered beside depth and rigor when it can actually change the bill: the session model stays orchestrator and advisor — interview, spec, decisions, diff review, verification — while cheaper Sonnet executor subagents do the implementation, escalating up when stuck. It's Anthropic's published plan-big-execute-small pattern (their numbers: a Fable 5 orchestrator with Sonnet 5 workers keeps 96% of Fable's quality at 46% of the price; the inverse advisor shape, ~92% at ~63%), and ultragoal is unusually well-placed to run it: cheap executors are only safe when the checking around them is strong, and the rubric + independent verifier are exactly that checking. The verifier is never downgraded.
Can I run it unattended? Yes — that's the recommended mode: npx ultragoal run "<brief>" launches at full autonomy, and --headless runs the loop to completion with no UI at all. The discipline lives in the rubric, the verifier, and the budget — not in you approving each tool call.
Uninstall? npx ultragoal uninstall removes the Claude and/or Codex plugin plus marketplace entries it can find; your .ultragoal/ state stays — it's yours. Add --codex or --claude to target one surface, and --purge to delete a repo's state too.
高质量的开源AI工作流项目,实现自动化任务执行
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,超级目标 在Agent工作流赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | ultragoal |
| 原始描述 | 开源AI工作流:Tell Claude Code what you want once. It works until the job is verifiably done —。⭐12 · Shell |
| Topics | ai-agentsautomationclaude-code |
| GitHub | https://github.com/morphaxl/ultragoal |
| License | MIT |
| 语言 | Shell |
收录时间:2026-06-16 · 更新时间:2026-06-20 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端