菜单
Review LLM prompts against the prompt-engineering skill's gap-calibration principles, reporting issues without modifying files. Use when reviewing prompt quality, auditing a prompt, evaluating a system prompt, or checking whether prompt issues are high-confidence and fixable.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/doodledood/manifest-dev --skill review-prompt命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Manifest builder. Turns shared understanding into a verifiable Manifest with Deliverables, Acceptance Criteria, Global Invariants, and an Initial Approach. Use when planning features, scoping refactors, debugging complex issues, or when the user asks to define, scope, plan, spec out, make a manifest, or break down a task.
Manifest executor. Works through Deliverables verifying every Acceptance Criterion and Global Invariant. Use when executing a manifest, running a plan, implementing a defined task, or when the user asks to run, execute, implement, or ship a manifest-backed plan.
Review a change along one specific quality dimension — bugs, design, simplicity, maintainability, testability, test quality, type safety, contracts, operational readiness, docs, prose value, change intent, or CLAUDE.md adherence. Loads exactly one dimension reference and audits the diff against it, returning a structured PASS/FAIL report with severities. Use when verifying a change before merge, auditing a diff for a named concern, or running a manifest acceptance gate.
正在显示 SKILL.md
基于 SOC 职业分类
| name | review-prompt |
| description | Review LLM prompts against the prompt-engineering skill's gap-calibration principles, reporting issues without modifying files. Use when reviewing prompt quality, auditing a prompt, evaluating a system prompt, or checking whether prompt issues are high-confidence and fixable. |
| user-invocable | true |
Review LLM prompts. Report findings without modifying files — this skill diagnoses; it never edits the prompt.
First: invoke the prompt-engineering skill to load the principles. Evaluate the prompt against them.
Input: if no prompt is given (file path or inline text), ask before analyzing — don't assume.
Report format:
Strengths — what's working, what the author should preserve.
Issues:
| Issue | Severity | Fix | Tag |
|---|---|---|---|
| {description} | High / Medium / Low | {concrete recommendation} | NEEDS_USER_INPUT or AUTO_FIXABLE |
Priority: the highest-impact change first.
Severity:
Tag (parsed as control flow by /auto-optimize-prompt — do not rename or repurpose):
NEEDS_USER_INPUT — only the author can resolve: missing context, unclear intent, ambiguity the model can't infer.AUTO_FIXABLE — a clear fix exists per the loaded principles.Only flag high-confidence issues. Low-confidence findings are noise — skip style preferences, minor wording, and unverified hunches.