gza-plan-improve
Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an implementation-ready plan
供给资产档案
研究与知识工作
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
场景
研究 Agent
I need my agent to research a topic, compare sources, and produce a concise report.
适配 Agent
Claude Code + CLI + Codex
适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。
安装
就绪
npx skills add mhawthorne/gza --skill gza-plan-improve
维护状态
新鲜
距上次推送 1 天
风险
需审查
Low GitHub adoption signal
GitHub 质量
11
57/100 质量 · 73/100 信任
覆盖标签
审查说明
Low GitHub adoption signal · Quality score needs review
Agent 采用评分卡
一眼查看信任、审计与安装准备度
这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。
质量
有潜力有用的候选项,但采用前应与替代方案比较。
信任
仅限沙盒有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
审计
需审查对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。
OpenAgentSkill 信任评分 v5
安装前需人工审查
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
Stars
11 个 GitHub Stars
仓库活跃度
11 个 Star,1 个 Fork
维护状态
距上次推送 1 天
许可证
MIT
安装
npx skills add mhawthorne/gza --skill gza-plan-improve
安装安全性
标准软件包或运行时安装路径
权限范围
Shell 或命令执行
Agent 结果
暂未有 Agent 结果数据
文档
README/SKILL.md 上下文充分
风险摘要
生产前审查
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 11 GitHub stars
- Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata
安装准备度
安装路径可用
- 安装路径可用
- 仓库证据可用
- 已声明许可证
- 暂无 Agent 验证结果证据
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
适用任务
- Browser automation 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
- Navigate pages
适用 Agent
安装决策
- 命令
- npx skills add mhawthorne/gza --skill gza-plan-improve
- 策略
- 审查
- 人工审查
- 是
信任与风险
- 信任
- 65/100
- 审计
- 76/100
- 风险级别
- 需审查
结果闭环
- 端点
- /api/agent/outcome
- 事件 ID
- resolve
- 结果
- 5
不适用场景
- 需要厂商支持 SLA 的团队
- production agents without a repository review
- Low GitHub adoption signal
- 高风险权限提示:Shell 或命令执行
- Quality score needs review
Agent 安全 v2
48/100 · 避免自动安装
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
高
Shell 或命令执行
Skill 元数据引用了终端、CLI、Shell、子进程或命令执行工作流。
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
文件系统访问
Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。
- 高风险权限提示:Shell 或命令执行
- Low GitHub adoption signal
安装目标
在你的 Agent 工作流中安装此 Skill
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install mhawthorne-gza-plan-improveAgent 解析计划
让 Agent 在安装前验证匹配度。
Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。
打开 JSON
/api/agent/resolve?task=Use%20gza-plan-improve%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20gza-plan-improve%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/mhawthorne-gza-plan-improve/install
Agent 应检查
- 从 Resolve API 检查任务匹配与替代方案。
- 检查审计评分、信任评分和安全策略警告。
- 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。
复制提示词
Task: Use gza-plan-improve in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20gza-plan-improve%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mhawthorne-gza-plan-improve/install
Install command: npx skills add mhawthorne/gza --skill gza-plan-improve
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 交接
把安装路径交给 Agent,而不是再给一个目录页。
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
安装交接
/api/skills/mhawthorne-gza-plan-improve/install
LLM 文本格式
/api/skills/mhawthorne-gza-plan-improve/install?format=text
寻找替代方案
/api/skills/search?q=gza-plan-improve&limit=3
Agent 提示词
Use gza-plan-improve for this task. Review https://www.openagentskill.com/api/skills/mhawthorne-gza-plan-improve/install, then install with: npx skills add mhawthorne/gza --skill gza-plan-improveRegistry 元数据
用于自动选择 Skill 的 Agent 可读档案。
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
Agent 决策面板
Fallback candidate for Browser automation
先用此 Skill 做原型验证,并保留备选方案。
栈中角色
备选候选
主要匹配
Browser automation
信任标签
先做原型验证
安装路径
命令已就绪
适用场景
- Browser automation 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
证据
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 57/100 质量档案
- 4 个 OpenAgentSkill 交互事件
先审查
- Low GitHub adoption signal
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次Browser automation任务。
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
信任档案
仅限沙盒
有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
GitHub 采用度
修复11 个 GitHub Stars
Star/Fork 活跃度
修复11 个 Star,1 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过距上次推送 1 天
许可证清晰度
通过MIT
积极信号
- AI 审查已通过
- 安装路径可用
- 仓库证据可用
- 近期维护的仓库
- 安装命令未发现明显高风险模式
- 结果闭环已就绪,但需要首次真实 Agent 运行
安装前审查
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 11 GitHub stars
- Stars/forks activity: 11 stars, 1 forks; issue activity unavailable in current metadata
- 暂未有真实 Agent 结果报告
- 无人值守安装前需要人工审查
建议操作
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
质量档案
有潜力 适用于 Agent 工作流的候选
有用的候选项,但采用前应与替代方案比较。
工作流匹配
在这些场景使用此 Skill
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Verify behavior
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
工作流匹配
加入完整工作流
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
Frontend and UI
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
MoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
概览
--- name: gza-plan-improve description: Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an implementation-ready plan allowed-tools: Read, Bash(uv run gza show:*), Bash(uv run gza log:*), AskUserQuestion version: 1.0.0 public: true ---
# Gza Plan Improve
Refine a draft plan through a deliberate question loop. Use this when the user has a rough plan, an incomplete completed plan task, or a draft that needs sharper scope, acceptance criteria, sequencing, risks, and test strategy before implementation begins.
## Inputs
Accept one of these inputs:
- Preferred: a full prefixed plan task ID (for example, `gza-1234`) - Also supported: pasted draft plan text - Optional: extra constraints, related task IDs, or notes about what feels weak
If the user provides neither a full prefixed plan task ID nor draft plan text, ask for the current draft or plan task first.
Use the full prefixed task ID for all `gza` commands.
## Goal
Produce an improved plan, not just a score.
The skill should: - identify the highest-leverage gaps in the current draft - ask concise questions to close those gaps - confirm assumptions explicitly instead of guessing - rewrite the plan into a cleaner, more implementation-ready shape - call out any remaining blockers or open questions
This is different from `/gza-plan-review`: - `/gza-plan-review` decides `Go` / `No-go` - `/gza-plan-improve` actively helps the user strengthen the plan first
## Process
### Step 1: Gather the current plan and context
If the input is a full prefixed plan task ID, inspect it with:
```bash uv run gza show <TASK_ID> uv run gza log <TASK_ID> ```
Use that output to extract: - task type and status - original prompt - current plan/report content - nearby context from logs that explains uncertainty, blockers, or assumptions
If the task is not found or is not a `plan` task, stop and explain the mismatch.
If the input is draft text instead of a task ID, use the provided draft as the working plan.
### Step 2: Diagnose the weakest parts first
Evaluate the draft against these plan dimensions:
1. Problem framing - Is the user problem or objective specific? - Does the draft explain why the work matters?
2. Scope and boundaries - What is explicitly in scope? - What is explicitly out of scope? - Which files, modules, systems, or surfaces are likely affected?
3. Acceptance criteria - What observable outcomes define success? - Are edge cases and failure modes named? - Would an implementer know when the work is done?
4. Risks and unknowns - What could cause rework, delay, or the wrong design choice? - Which unknowns need decisions, investigation, or validation?
5. Dependencies and sequencing - Are prerequisites, approvals, related tasks, or external systems identified? - Is the execution order clear enough to avoid backtracking?
6. Test strategy - Which tests or verification modes are required? - Which regressions must be guarded against?
Rank the gaps and focus on the smallest set of questions that will most improve the plan.
### Step 3: Run a targeted question loop
Use AskUserQuestion to ask concise, high-value follow-up questions.
Rules for the question loop: - Ask only what materially improves the plan - Prefer 1 to 4 questions per round - Ask about the biggest uncertainty first - Confirm assumptions explicitly when the draft implies something but does not state it - Stop asking once the remaining gaps are minor or clearly flagged as open questions
Good question themes: - exact success criteria - scope boundaries and non-goals - risky edge cases - sequencing and dependency order - test expectations - operator-facing docs/help/config impact when relevant
### Step 4: Rewrite the plan
Produce a revised plan with clear headings and direct language. Prefer a structure like:
```text Plan: <short title>
Objective - <what problem is being solved>
Scope - In scope: <items> - Out of scope: <items>
Assumptions / Inputs - <assumptions confirmed with user>
Acceptance Criteria 1. <testable success condition> 2. <testable success condition>
Implementation Outline 1. <step> 2. <step> 3. <step>
Risks / Unknowns - <risk + mitigation or follow-up>
Dependencies - <task/system/approval + status>
Test Strategy - <unit/integration/e2e/manual verification as relevant>
Open Questions - <only unresolved items that genuinely remain> ```
Do not preserve vague wording from the original draft if it can be made concrete.
### Step 5: Close with readiness and next action
After presenting the improved plan, summarize:
- what materially changed - any blockers or unresolved questions that still matter - whether the plan now looks ready for `/gza-plan-review` or direct implementation follow-up
If the plan came from a task and is now strong enough, recommend:
```bash uv run gza show <TASK_ID> uv run gza log <TASK_ID> ```
and then `/gza-plan-review` for a final quality gate if needed.
If the plan is still too ambiguous after refinement, say so plainly and list the missing decisions.
## Important notes
- Keep the interaction collaborative and specific; avoid broad brainstorming unless the user asks for it. - Prefer rewriting the plan over merely criticizing it. - Do not invent technical constraints, dependencies, or acceptance criteria that were not supported by the draft or user answers. - If the user is really trying to create a new gza task rather than improve a plan draft, prefer `/gza-task-draft`. - If the user wants a final `Go` / `No-go` decision on a completed plan task, prefer `/gza-plan-review`.
技术详情
- 版本
- 1.0.0
- 许可证
- MIT
- 最近更新
- 2026年8月21日
- 发布时间
- 2026年8月21日
决策摘要
备选候选
仓库近期活跃
Agent 验证证据
Agent 验证证据
来自解析、审查、安装和一次小范围运行后的结果报告。
- 成功率
- —
- 近期失败
- —
- 结果
- 0
- 输出质量
- —
- 失败
- 0
- 不相关
- 0
- 安装次数
- 0
- 风险拦截
- 0
- 需要配置
- 0
- 生产环境
- 0
暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。
增长闭环
分享工具包
为 gza-plan-improve 准备的场景化草稿,可手动发布到 X。
gza-plan-improve: Refine a draft plan by asking targeted questions, resolving gaps, and rewriting it into an im... 11 stars https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve?ref=x
可选:带安装命令的回复
Listing + install path for gza-plan-improve: https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve?ref=x Install: npx skills add mhawthorne/gza --skill gza-plan-improve
收录来源
Registry 收录
此列表来自公开来源,维护者认领获批前不会标记为官方。
- 创作者
- mhawthorne
- 收录方
- OpenAgentSkill 社区索引
归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。
认领此 Skill所有者认领
认领此 Skill 页面
这条 Registry 收录 列表归属于 mhawthorne,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。
创作者外链工具包
将证据徽章加入你的 README
在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve)
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve)
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve/audit)
[](https://www.openagentskill.com/skills/mhawthorne-gza-plan-improve)作者
mhawthorne
@mhawthorne
平台适配
健康信号
- GitHub Stars
- 11
- 质量评分
- 31/100
- 最近 GitHub 推送
- 2026年8月21日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 4
- 复制安装命令
- 0
- 跳转点击
- 0
社区信号
告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。
信任与安全
仅限沙盒
- GitHub 采用度11 个 GitHub Stars修复
- Star/Fork 活跃度11 个 Star,1 个 Fork; 当前元数据中没有议题活跃度信息修复
- 近期维护距上次推送 1 天通过
- 许可证清晰度MIT通过
- README/SKILL.md 完整度元数据包含足够的用法与工作流上下文通过
- 依赖与运行时风险命令执行范围信息
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