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Agent Skill · 研究检索

report(report 工具)

report 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

report 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为操作浏览器;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 66,577。

总安装 261周安装 11GitHub Stars 9下载 92核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:report(report 工具)
来源仓库:https://github.com/florianbuetow/claude-code
仓库路径:skills/report
安装命令:
npx skills add https://github.com/florianbuetow/claude-code --skill report
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/florianbuetow/claude-code --skill report

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息。report 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词快速定位候选结果。可结合来源仓库继续核验具体用法。安装前建议确认权限和维护状态。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

注意是否会触发联网或文件读写。

来源仓库内路径:skills/report

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/florianbuetow/claude-code --skill report;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

report 怎么安装?

在终端执行 npx skills add https://github.com/florianbuetow/claude-code --skill report。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

report 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

report 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

report 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

report 用的人多吗?

公开数据是累计安装 261 次,近一周安装 11 次,下载 92 次,GitHub 9 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

report 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
report

report 工具 · 本页

2619操作浏览器未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Security Report Generation

Generate human-readable security reports from accumulated findings, scanner results, and analysis data. Supports multiple output formats and includes executive summaries, findings tables, architecture diagrams, scanner coverage analysis, and remediation progress tracking.

Supported Flags

Read ../../shared/schemas/flags.md for the full flag specification.

FlagReport Behavior
--scopeDetermines which findings to include. Default full (all findings).
--depth quickFindings table and severity counts only.
--depth standardFull report: executive summary, findings table, remediation status.
--depth deepStandard + architecture diagram, attack paths, scanner coverage analysis.
--depth expertDeep + risk scoring, trend analysis, compliance mapping, remediation timeline.
--severityOnly include findings at or above this severity in the report.
--formatDefault md. Supports md, html, json, sarif.
--outputCustom filename for the report. Default: .appsec/reports/report-<timestamp>.<ext>.

Workflow

Step 1: Gather Data

Collect all available security data:

  1. Findings: Read .appsec/findings.json. Parse each finding object from the array.
  2. Fixed history: Read .appsec/fixed-history.json if it exists.
  3. Scanner results: Check for raw scanner output in .appsec/scanners/.
  4. Model data: Check .appsec/model/ for threat model artifacts.
  5. Previous reports: Check .appsec/reports/ for trend comparison.

Filter findings by --scope and --severity flags.

Step 2: Compute Statistics

Calculate aggregate metrics:

  1. Total findings by severity: critical, high, medium, low.
  2. Findings by status: open, fix-applied, verified-fixed, fix-failed, false-positive.
  3. Findings by category: Group by CWE, OWASP category, or tool.
  4. Remediation rate: Percentage of findings that have been fixed and verified.
  5. Mean time to fix: Average time between finding creation and verified-fix (if timestamps available).
  6. Scanner coverage: Which scanners ran, which were missing, which categories were checked.

Step 3: Generate Executive Summary

Write a concise (3-5 sentence) executive summary covering:

  1. Overall security posture assessment (critical/concerning/moderate/good).
  2. Most significant risk(s) found.
  3. Remediation progress.
  4. Recommended priority action.

Example:

The application has a **concerning** security posture with 2 critical and 5 high
severity findings across authentication and injection categories. The most
significant risk is unauthenticated access to the admin API (AC-001), which
allows full data exfiltration. 40% of findings have been remediated. Priority
action: fix AC-001 and INJ-003 before the next release.

Step 4: Build Findings Table

Create a sortable findings table:

| ID | Severity | Title | File | CWE | Status | OWASP |
|----|----------|-------|------|-----|--------|-------|
| AC-001 | CRITICAL | Unauthenticated admin API | src/routes/admin.ts:15 | CWE-306 | Open | A01 |
| INJ-003 | CRITICAL | SQL injection in search | src/db/search.ts:42 | CWE-89 | Fix Applied | A03 |
| ... |

Sort by: severity (critical first), then status (open first).

Step 5: Generate Architecture Diagram

At --depth deep and above, generate a Mermaid diagram showing:

  1. Components: Services, databases, external APIs, message queues.
  2. Trust boundaries: Internet-facing vs internal, authenticated vs public.
  3. Finding locations: Annotate components with finding counts and max severity.
  4. Data flows: Show where sensitive data travels between components.
graph TB
    subgraph "Internet (Untrusted)"
        User[User Browser]
    end
    subgraph "DMZ"
        LB[Load Balancer]
        API[API Server<br/>2 CRITICAL, 3 HIGH]
    end
    subgraph "Internal"
        DB[(Database)]
        Cache[(Redis)]
    end
    User --> LB --> API
    API --> DB
    API --> Cache

Step 6: Map Attack Paths

At --depth deep and above, identify and document attack paths -- chains of findings that combine to enable a more severe attack:

### Attack Path 1: Unauthenticated Data Exfiltration
1. AC-001: No auth on admin API (CRITICAL)
2. INJ-003: SQL injection in search endpoint (CRITICAL)
   Combined impact: Unauthenticated attacker can extract entire database
   via admin search endpoint with injected query.

Step 7: Scanner Coverage Analysis

Document which security scanners were used and their coverage:

### Scanner Coverage

| Scanner | Status | Categories Covered |
|---------|--------|--------------------|
| semgrep | Ran | Injection, Auth, SSRF |
| gitleaks | Ran | Secrets |
| npm audit | Ran | Dependencies |
| trivy | Not available | Container, IaC |
| bandit | Not applicable | Python (not in stack) |

Coverage: 6/10 OWASP categories checked by at least one scanner.
Gaps: A04 (Insecure Design), A05 (Misconfig), A08 (Integrity), A09 (Logging)

Step 8: Remediation Progress

Track fix progress over time:

### Remediation Progress

Total findings: 15
- Open: 7 (47%)
- Fix applied (unverified): 3 (20%)
- Verified fixed: 4 (27%)
- False positive: 1 (7%)

By severity:
- Critical: 0/2 fixed (0%) -- URGENT
- High: 2/5 fixed (40%)
- Medium: 2/5 fixed (40%)
- Low: 1/3 fixed (33%)

Step 9: Write Report File

Assemble all sections and write to the output file.

Format-Specific Behavior
FormatOutput
md (default)Write to .appsec/reports/report-<timestamp>.md. Sections: Executive Summary, Findings Table, Architecture Diagram, Attack Paths, Scanner Coverage, Remediation Progress, Appendix.
jsonAggregate findings object from ../../shared/schemas/findings.md with report metadata.
sarifSARIF 2.1.0 compliant output for GitHub Security tab. Schema: sarif-schema-2.1.0.json, driver name appsec.
htmlMarkdown report wrapped in minimal HTML with inline CSS. Mermaid diagrams rendered via script tag.

Output Format

The report itself is the primary output. Findings within the report follow ../../shared/schemas/findings.md.

  • metadata.tool: "report"

Finding ID prefix: This skill does not generate new findings. It aggregates existing ones.

Pragmatism Notes

  • If no findings exist, generate a clean report stating no issues found and which analysis has been performed.
  • SARIF output should validate against the SARIF 2.1.0 schema. Test with sarif-tools or GitHub upload if available.
  • Architecture diagrams are best-effort based on available code analysis. Label them as approximate.
  • Do not fabricate statistics. If timestamps are missing, omit time-based metrics rather than guessing.
  • For large codebases with many findings, group by component/service rather than listing all in one flat table.
  • Always note the scope and depth used, so readers understand the report's coverage limitations.

平台分布

Codex

33.37%
按下载量换算31

Claude

31.87%
按下载量换算29

Cursor

20.6%
按下载量换算19

Gemini CLI

10.5%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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