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Agent Skill · 研究检索

security(安全)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

security 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,权限为接触敏感数据;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 63,725。

总安装 312周安装 13GitHub Stars 182下载 104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:security(安全)
来源仓库:https://github.com/garagon/nanostack
仓库路径:skills/security
安装命令:
npx skills add https://github.com/garagon/nanostack --skill security
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/garagon/nanostack --skill security

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。

security 用于辅助安全审计、权限检查、凭据风险排查和常见漏洞分析。适合梳理敏感配置、检查依赖风险或生成安全复核清单。通过 npx skills add 命令从指定 GitHub 仓库安装并使用。使用时不能将工具输出直接作为最终结论,涉及密钥或生产系统时应确认最小权限。建议结合项目实际环境核验操作边界和脱敏方式。

来源仓库内路径:skills/security

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/garagon/nanostack --skill security;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

    它需要接触敏感数据,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

security 怎么安装?

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

security 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。

security 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

security 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。

security 用的人多吗?

公开数据是累计安装 312 次,近一周安装 13 次,下载 104 次,GitHub 182 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。

security 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
security

安全 · 本页

312182接触敏感数据未明确通过

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 — Security Audit

You think like an attacker but report like a defender. The real attack surface is rarely the code you wrote. It is the secrets in git history, the dependency you forgot to update, the CI pipeline that leaks tokens, and the AI endpoint without rate limiting. Start there, not at the application logic.

Intensity Mode

ModeFlagScopeConfidence gate
Quick--quickOWASP A01-A03 (top 3) + secrets scan + dependency check9/10 — only verified findings
Standard(default)Full OWASP A01-A10 + STRIDE per component + dependencies7/10 — report anything with evidence
Thorough--thoroughFull OWASP + STRIDE + variant analysis + conflict detection + LLM security check3/10 — flag tentative findings marked as TENTATIVE

Auto-suggest:

  • Pre-commit on small changes → suggest --quick
  • Pre-ship standard feature → --standard (default)
  • Pre-ship auth/payment/infra, or first audit of a codebase → suggest --thorough

Thorough-only features:

  • Variant analysis: When a finding is VERIFIED, search the entire codebase for the same pattern. One confirmed SQL injection means there may be more.
  • Conflict detection: Cross-reference with /review artifacts in .nanostack/review/ for contradictions.
  • TENTATIVE findings: Below confidence gate but worth noting. Mark as TENTATIVE: <description>.

Setup (first run per project)

Read the plan artifact if one exists:

~/.claude/skills/nanostack/bin/find-artifact.sh plan 2

If found:

  • planned_files[] → focus your audit on these files and their dependencies. Deeper analysis on fewer files is better than shallow analysis on everything.
  • risks[] → treat each planned risk as a security hypothesis to verify. If the plan says "AWS SDK version compatibility" is a risk, check for insecure SDK usage patterns.

Then read project config: bin/init-config.sh. Use detected to scope which checks to run (skip Python checks in a Go project). Use preferences.conflict_precedence for cross-skill conflicts.

Then check if security/config.json exists. If not, ask the user to classify the project:

What type of project is this?
1. Public-facing (users/customers on the internet)
2. Internal (employees/team only, no public access)
3. Compliance-driven (fintech, health, regulated)
4. Library/SDK (consumed by other developers)

Store the answer:

// security/config.json
{
  "project_type": "public_facing",
  "conflict_precedence": "security > review > qa",
  "configured_at": "2026-03-25"
}

This determines:

  • Conflict precedence: public_facing → security wins. internal → review wins. compliance → security wins hard.
  • Default intensity: public_facing/compliance → suggest --thorough on first audit. internal/library → --standard.
  • OWASP priority: public_facing → A01, A03, A07 first. internal → A02, A05, A09 first.

If config already exists, read it and skip setup.

Process

1. Detect Stack

Auto-detect everything. Do NOT ask the user.

  • package.json → Node.js (check for next, express, fastify, hono)
  • requirements.txt / pyproject.toml → Python (flask, django, fastapi)
  • go.mod → Go (gin, echo, chi)
  • Database deps: prisma, drizzle, mongoose, sqlalchemy, gorm
  • BaaS: supabase, firebase, convex
  • Auth: next-auth, clerk, passport, lucia, jwt
  • AI/LLM: openai, anthropic, langchain, vercel ai sdk
  • Payments: stripe, paddle
  • Infra: Dockerfile, docker-compose.yml, .github/workflows/

Report one-line: Detected: Next.js 14 + Prisma + Stripe, Docker, GitHub Actions

2. Scan

CORE (always run): secrets, injection, auth, config, dependencies, data-exposure.

CONDITIONAL (only if detected): AI/LLM endpoints, payment webhook verification, Docker misconfig, CI/CD pipeline security, file upload handling.

For extended check patterns, reference the OWASP checklist at security/references/owasp-checklist.md.

Read security/references/owasp-checklist.md for the OWASP A01-A10 framework.

Secrets Scan (CRITICAL — always first)

Search for hardcoded credentials using regex patterns:

PatternWhat
AKIA[0-9A-Z]{16}AWS access key
sk_live_[a-zA-Z0-9]{24,}Stripe live key
sk-proj-[a-zA-Z0-9\-_]{20,}OpenAI project key
sk-ant-[a-zA-Z0-9\-_]{80,}Anthropic key
ghp_[a-zA-Z0-9]{36}GitHub PAT
`-----BEGIN (RSA\EC\OPENSSH) PRIVATE KEY`Private key in code
`(postgres\mysql\mongodb\+srv):\/\/[^:\s]+:[^@\s]+@`DB connection string with password

Context rules: In *.test.*, *.example, README*, or values containing xxx, TODO, placeholder → downgrade to INFO.

Git history check (mandatory):

git log --all --oneline -- '.env' '.env.local' '*.pem' '*.key' 2>/dev/null | head -10

If results: secrets may be in history even if currently gitignored. CRITICAL — credentials must be rotated.

IMPORTANT: Credential redaction. When reporting secrets, NEVER show the full value. First 4 chars + **** (e.g., sk-pr****).

CI/CD Pipeline Security (if .github/workflows/ exists)
CheckWhat to look for
Unpinned actionsuses: action@main instead of uses: action@sha256
pull_request_targetRuns with write access on fork PRs — code injection vector
Secrets in logsecho ${{secrets.*}} or debug mode exposing secrets
Overpermissioned GITHUB_TOKENpermissions: write-all when only contents: read needed
AI/LLM Security (if AI deps detected)
CheckWhat to look for
API keys in client bundleNEXT_PUBLIC_OPENAI, NEXT_PUBLIC_ANTHROPIC
Prompt injectionUser input interpolated into system prompts (prompt + req.body)
Missing rate limitingAI endpoints without rate limiter — attacker runs up your bill
Unsanitized LLM outputLLM response rendered as HTML without escaping

3. False Positive/Negative Traps

Skip these (false positives):

  • .env.example / .env.sample — placeholders, not leaks
  • sk_test_ / pk_test_ — Stripe TEST keys, INFO at most
  • UUIDs as identifiers — unguessable, don't flag
  • React/Angular output — XSS-safe by default, only flag escape hatches
  • eval() in build configs (webpack, vite) — normal tooling
  • 0.0.0.0 binding inside Docker — expected container behavior
  • SQL in migration files — expected patterns

Don't miss these (false negatives):

  • Auth on route but not on data query — IDOR through direct DB access
  • Secrets removed from code but still in git log
  • Rate limiting on login but not on password reset
  • SSRF via URL params hitting cloud metadata (169.254.169.254)
  • dangerouslySetInnerHTML without DOMPurify sanitization

3. STRIDE Threat Model

For each component in the system, evaluate:

ThreatQuestion
SpoofingCan an attacker impersonate a user or service?
TamperingCan data be modified in transit or at rest without detection?
RepudiationCan actions be performed without an audit trail?
Information DisclosureCan sensitive data leak through errors, logs, or side channels?
Denial of ServiceCan the system be overwhelmed or made unavailable?
Elevation of PrivilegeCan a user gain permissions they shouldn't have?

4. Produce Report

Report findings progressively. Don't wait until the end. As each phase completes, output its findings immediately so the user sees work happening.

Open with a summary line:

Security: CRITICAL (0) HIGH (1) MEDIUM (2) LOW (1) = 4 findings. Score: B

Scoring: A = 0 critical, 0 high, ≤3 medium. B = 0 critical, 1-2 high. C = 3+ high. D = 1-2 critical. F = 3+ critical.

Use security/templates/security-report.md for the full structure. Every finding must include:

  • What the vulnerability is (specific, not vague)
  • Where it exists (file path and line number)
  • How to exploit it (proof of concept or clear scenario)
  • Fix with actual code, before and after (not "consider sanitizing input")
  • Severity using the classification below

Always close with What's solid: 2-3 specific things the codebase does well on security. Not filler. If the auth is well implemented, say so and say why.

Severity Classification

SeverityCriteriaExamples
CriticalExploitable remotely, no authentication required, leads to full compromiseRCE, SQL injection with admin access, hardcoded admin credentials
HighExploitable with some conditions, significant impactStored XSS, IDOR exposing sensitive data, privilege escalation
MediumRequires specific conditions or has limited impactCSRF, information disclosure via error messages, missing rate limiting
LowInformational or requires unlikely conditionsMissing security headers, verbose error messages, outdated non-vulnerable dependency

Conflict Detection

Always check for conflicts with prior /review findings if a review artifact exists:

~/.claude/skills/nanostack/bin/find-artifact.sh review 30

Read reference/conflict-precedents.md for known conflict patterns. When detected, mark inline:

### SEC-005: Excessive error detail
**Conflicts with:** REV-003 → RESOLUTION: structured errors (code + generic msg to user, details to logs)

In --quick mode, apply default precedence (security > review) without documenting. In --standard mode, document conflicts inline. In --thorough mode, document conflicts AND flag as BLOCKING until user confirms.

After completing the audit and conflict detection, save the artifact. Run this command now — do not skip it:

~/.claude/skills/nanostack/bin/save-artifact.sh security '<json with phase, mode, summary, findings, conflicts, context_checkpoint including summary, key_files, decisions_made, open_questions>'

Mode Summary

AspectQuickStandardThorough
OWASP scopeA01-A03 onlyFull A01-A10Full + variant analysis
STRIDESkipPer componentPer component + attack trees
Dependenciesnpm audit onlyFull scanFull + license check
Conflict detectionAuto-resolveDocument inlineBLOCKING until resolved
Tentative findingsSkipSkipReport as TENTATIVE
Confidence gate9/107/103/10

Next Step

After the security audit is complete and the artifact is saved:

If AUTOPILOT is active and no critical/high findings: Proceed to next pending skill (/qa or /ship). Show: Autopilot: security grade X (0 critical, 0 high). Running /qa...

If AUTOPILOT is active but critical or high findings found: Stop and ask the user to review. Show the findings and wait. After resolution, continue autopilot.

Otherwise: Tell the user:

Security audit complete. Remaining steps: - /review to run code review (if not done yet) - /qa to test that everything works (if not done yet) - /ship to create the PR (after review, security and qa pass)

Gotchas

  • If you find zero vulnerabilities, say so. A clean audit is a valid result. Don't manufacture findings to justify the scan.
  • Don't inflate severity. Missing security headers on an internal tool is Low, not Medium. Calibrate to actual exploitability.
  • Don't report theoretical vulnerabilities without evidence. "This could be vulnerable to XSS" is not a finding. Show the input path, the sink, and the missing sanitization.
  • Don't skip dependency scanning. Run npm audit, pip audit, go vuln check, or equivalent. Known CVEs in dependencies are the lowest-hanging fruit.
  • Don't ignore configuration. .env.example, docker-compose.yml, CI/CD configs, and cloud IAM policies are part of the attack surface.
  • Don't confuse defense-in-depth with redundancy. Multiple layers of validation at different trust boundaries is correct. Validating the same thing three times in the same function is not.
  • Authentication ≠ Authorization. Checking that a user is logged in does not mean checking that they have permission to access the resource.
  • Secrets in git history are still exposed. Even if a secret was removed in a later commit, it exists in the history. Check with git log -p --all -S 'password\|secret\|key\|token'.
  • Variant analysis is not optional in --thorough. One confirmed finding means the pattern may exist elsewhere. Search for it.

平台分布

Codex

38.22%
按下载量换算40

Claude

28.7%
按下载量换算30

Cursor

18.46%
按下载量换算19

Gemini CLI

9.59%
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

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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