demand-first-review
Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectural extension point that adds or expands shared surface area, especially when consumers are absent, exports are unused or speculative, existing consumers are hack-heavy, forward compatibili
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 CherryHQ/cherry-studio --skill demand-first-review
维护状态
新鲜
今天有推送
风险
可安全尝试
可用元数据中未发现重大风险信号
GitHub 质量
51K
94/100 质量 · 86/100 信任
覆盖标签
审查说明
可用元数据中未发现重大风险信号
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
优秀高置信候选,具有较强的采用度与健康维护信号。
信任
审查后安装适合加入候选清单,但 Agent 在运行前应审查审计说明、安装策略和结果证据。
审计
可安全尝试对安装准备度、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
适合 Agent 安装的候选
在人工审查或沙盒验证后作为首选候选。
Stars
51K 个 GitHub Stars
仓库活跃度
51K 个 Star,4.8K 个 Fork
维护状态
今天有推送
许可证
AGPL-3.0
安装
npx skills add CherryHQ/cherry-studio --skill demand-first-review
安装安全性
标准软件包或运行时安装路径
权限范围
network or browser access, database access
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
低元数据风险
- 可用元数据中未发现重大信任警告
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
- 研究 Agent 工作流
- Claude Code 团队
- 重视 GitHub 采用信号的团队
- 检索来源
适用 Agent
安装决策
- 命令
- npx skills add CherryHQ/cherry-studio --skill demand-first-review
- 策略
- 允许
- 人工审查
- 否
信任与风险
- 信任
- 83/100
- 审计
- 91/100
- 风险级别
- 可安全尝试
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
- 没有内部安全审查的高合规环境
- 当前元数据中未发现重大风险信号
- 可用元数据中未发现重大信任警告
- Production credentials, payments, or irreversible account changes without explicit human review
替代 Skill
Last30days Skill
53.5K Stars
npx skills add mvanhorn/last30days-skill -g
替代 Skill
Academic Research Skills
38.4K Stars
npx skills add Imbad0202/academic-research-skills
替代 Skill
GPT Researcher
28.0K Stars
npx skills add assafelovic/gpt-researcher
替代 Skill
DeepResearch
19.8K Stars
npx skills add Alibaba-NLP/DeepResearch
Agent 安全 v2
83/100 · 常规审查后可安全安装
审计与安全信号良好,公开元数据中没有高风险权限提示。
先查看审计页面,再在沙盒工作流中允许 Agent 安装。
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
数据库访问
Skill 可能检查 Schema、查询数据库或处理持久化存储。
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 cherryhq-demand-first-reviewAgent 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%20demand-first-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20demand-first-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/cherryhq-demand-first-review/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 demand-first-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20demand-first-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/cherryhq-demand-first-review/install
Install command: npx skills add CherryHQ/cherry-studio --skill demand-first-review
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/cherryhq-demand-first-review/install
LLM 文本格式
/api/skills/cherryhq-demand-first-review/install?format=text
寻找替代方案
/api/skills/search?q=demand-first-review&limit=3
Agent 提示词
Use demand-first-review for this task. Review https://www.openagentskill.com/api/skills/cherryhq-demand-first-review/install, then install with: npx skills add CherryHQ/cherry-studio --skill demand-first-reviewRegistry 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
适合 研究 Agent 的首选
将其作为优先候选,再在你的 Agent 环境中验证 README 与安装路径。
栈中角色
首选
主要匹配
研究 Agent
信任标签
可用于生产
安装路径
命令已就绪
适用场景
- 研究 Agent 工作流
- Claude Code 团队
- 重视 GitHub 采用信号的团队
证据
- 50,908 个 GitHub Stars
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 94/100 质量档案
- 6 个 OpenAgentSkill 交互事件
先审查
- 当前元数据中未发现重大风险信号
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次研究 Agent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
审查后安装
适合加入候选清单,但 Agent 在运行前应审查审计说明、安装策略和结果证据。
GitHub 采用度
通过51K 个 GitHub Stars
Star/Fork 活跃度
通过51K 个 Star,4.8K 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过今天有推送
许可证清晰度
通过AGPL-3.0
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
- Large GitHub adoption signal
- 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
- 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
在人工审查或沙盒验证后作为首选候选。
质量档案
优秀 适用于 Agent 工作流的候选
高置信候选,具有较强的采用度与健康维护信号。
工作流匹配
在这些场景使用此 Skill
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
工作流匹配
加入完整工作流
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
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.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
GPT Researcher
Run autonomous deep research over web and local sources
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
概览
--- name: demand-first-review description: Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectural extension point that adds or expands shared surface area, especially when consumers are absent, exports are unused or speculative, existing consumers are hack-heavy, forward compatibility is claimed, or multiple similar APIs may express one demand. ---
# Demand-First Review
## Principle
Audit this causal chain before implementation quality:
`root outcome or invariant → normalized demand → owning layer → contract → consumer`
A call site proves usage, not legitimacy or shape. No call site raises the burden of proof, not an automatic rejection. Real demand may still have the wrong consumer or abstraction.
## Workflow
Apply every step to each added API, channel, parameter, type, field, config, or extension point.
### 1. Reconstruct the demand
List every new surface and exact consumed dimension; one valid consumer does not justify unused fields. Trace current and linked consumers to their user outcome, business rule, or system invariant. Inspect adjacent implementations, then ask: without the current API and history, would the demand remain and would this contract still be natural? Do not rely only on the PR description.
### 2. Audit consumer legitimacy
- **Legitimate**: uses the correct owner and boundary. - **Compensating**: uses the nearest API and adds workarounds because the right capability is missing. - **Legacy-shaped**: reflects obsolete formats, transitional architecture, or history. - **Misplaced**: serves real demand in the wrong layer.
Parsing, retries, sequencing, duplicated state, check-then-act, or cross-layer access signal compensation. Treat these as unmet upstream demand, never endorsement of the current surface.
### 3. Normalize related demands
Strip names, historical formats, and workarounds from demand statements. Cluster by outcome, source of truth, owner, transaction, security, and lifecycle. Consolidate historical or caller-specific differences; keep contracts separate for genuine ownership, permission, atomicity, lifecycle, side-effect, or failure differences. Prefer a stable core with thin adapters, not duplicated workflows or a lowest-common-denominator API.
### 4. Classify evidence
- **Direct**: a legitimate current consumer uses the dimension. - **Committed**: a concrete consumer exists in the same change or linked near-term work. - **Architectural**: a minimal seam must precede consumers to protect a concrete invariant. - **Unsupported speculation**: only a possible future is named, without a concrete scenario, owner, or omission cost.
Direct consumption proves pressure, not placement or shape.
### 5. Test architectural demand
For a surface without a legitimate current consumer, require all five:
1. A concrete consumer class or extension scenario; 2. The owning layer and protected invariant; 3. A causal omission cost, such as boundary violations, duplicated mechanisms, incompatible implementations, security gaps, or migration lock-in; 4. Why the seam must exist before its first consumer; and 5. The smallest stable mechanism that protects the invariant.
Reject "future features", "flexibility", "centralization", "technical constraints", or "migration risk" without linked evidence and a causal failure. If the test fails, defer or remove. If it passes, preserve only the minimal paved road and remove guessed dimensions.
### 6. Check responsibility and overlap
Place behavior by ownership, not line count. Centralize security, permissions, transactions, invariants, and shared policy; leave presentation and caller-specific composition in consumers. Prefer try-the-operation when the owner can enforce atomically.
Compare contracts by semantics, owner, permissions, exposure, atomicity, lifecycle, failure model, and cost. Shared data alone does not prove duplication; reuse only when these are equivalent.
### 7. Decide, then review implementation
Choose one outcome per surface or normalized group:
- **Keep**: demand and shape are justified. - **Narrow**: remove unsupported dimensions. - **Split**: separate a valid core from unrelated concerns. - **Consolidate**: merge surfaces expressing one demand. - **Replace**: keep the demand, change consumer, owner, or abstraction. - **Defer**: do not commit a possible demand yet. - **Remove**: no demand remains or an equivalent contract owns it.
Report root outcome, evidence, consumer legitimacy, essential differences, owner, alternatives, and decision first. Review implementation quality only for survivors.
## Rationalization Guards
| Claim | Response | |---|---| | "The API is clean; the types are elegant." | Quality cannot justify existence. | | "It has consumers." | Verify legitimacy and exact consumption; workarounds endorse nothing. | | "It has no consumers." | Run the five-part architectural test; absence alone decides nothing. | | "The export is unused; add a test." | Tests verify behavior; they do not create demand. | | "The architecture will need it." | Name the invariant, causal omission cost, consumer class, why now, and minimal seam. | | "A technical constraint requires it." | Trace the constraint to root demand; constraints are not axioms. | | "Existence is the architect's call." | Authority neither exempts demand review nor reduces it to a nit. | | "The caller can compute it in one line." | Place policy by ownership and invariants, not code length. | | "The existing API returns the same data." | Compare full semantics before declaring duplication. | | "These consumers differ slightly." | Prove differences are semantic, not historical or caller-specific. | | "It is forward-compatible or additive." | Keep only concrete needs; additive contracts carry permanent cost. |
## Red Flags
Pause and restart from Step 1 when:
- Implementation comments accumulate before stating root demand, evidence, and legitimate consumers. - A call site is treated as proof that the contract belongs here or has the right shape. - Zero current consumption is treated as automatic rejection or permission to accept an architectural claim. - A compensating or hack-heavy consumer is used to freeze its workaround into the shared contract.
## Calibration
- Linked independent modules would otherwise import privileged internals: **keep the minimal registration seam; remove guessed knobs**. - A renderer parses raw errors and retries because no atomic operation exists: **replace the abstraction rather than expand the error taxonomy**.
技术详情
- 版本
- 1.0.0
- 许可证
- AGPL-3.0
- 最近更新
- 2026年8月22日
- 发布时间
- 2026年8月20日
决策摘要
首选
50,908 个 GitHub Stars
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 demand-first-review 准备的场景化草稿,可手动发布到 X。
demand-first-review: Use when reviewing a PR, API, IPC channel, endpoint, parameter, type, config, or architectura... 50.9K stars https://www.openagentskill.com/skills/cherryhq-demand-first-review?ref=x
可选:带安装命令的回复
Listing + install path for demand-first-review: https://www.openagentskill.com/skills/cherryhq-demand-first-review?ref=x Install: npx skills add CherryHQ/cherry-studio --skill demand-first-review
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
- CherryHQ
- 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 CherryHQ,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/cherryhq-demand-first-review)
[](https://www.openagentskill.com/skills/cherryhq-demand-first-review)
[](https://www.openagentskill.com/skills/cherryhq-demand-first-review/audit)
[](https://www.openagentskill.com/skills/cherryhq-demand-first-review)作者
CherryHQ
@cherryhq
平台适配
健康信号
- GitHub Stars
- 50.9K
- 质量评分
- 57/100
- 最近 GitHub 推送
- 2026年8月22日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 6
- 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
审查后安装
- GitHub 采用度51K 个 GitHub Stars通过
- Star/Fork 活跃度51K 个 Star,4.8K 个 Fork; 当前元数据中没有议题活跃度信息通过
- 近期维护今天有推送通过
- 许可证清晰度AGPL-3.0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network or browser surface, database surface信息
相关 Skill
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
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