This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Security audit and vulnerability scanning for AI agent skills before install. Detects prompt injection, dangerous code, exfiltration, credential harvesting, and supply chain risks. Use when evaluating untrusted skills or gating installs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Scan and audit AI agent skills for security risks before installation. Performs static analysis on code files for dangerous patterns, scans markdown files for prompt injection, validates dependency supply chains, checks file system boundaries, and detects obfuscation. Produces a structured PASS / WARN / FAIL verdict with findings categorized by severity and actionable remediation guidance.
Keywords: skill security, AI security, prompt injection, code audit, supply chain, dependency scanning, data exfiltration, credential harvesting, obfuscation detection, pre-install security
os.system, subprocess shell=True, backticks), eval/exec/compile, obfuscation (base64/hex/chr()), network exfiltration, credential harvesting (~/.ssh, ~/.aws), privilege escalation.pip/npm install, low-reputation packages.Before the audit, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|---|---|---|
code_scanner.py | Scan Python scripts for eval/exec, subprocess, network exfiltration, credential harvesting, obfuscation, unsafe imports | python scripts/code_scanner.py <target> --strict --json |
prompt_injection_scanner.py | Scan markdown/text for prompt-injection patterns and hidden directives | python scripts/prompt_injection_scanner.py <target> --strict --json |
supply_chain_checker.py | Check imports/requirements for typosquatting, unpinned versions, inline installs | python scripts/supply_chain_checker.py <target> --strict --json |
All tools take a target (file or directory), and support --strict (any HIGH → FAIL) and --json.
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
requirements.txt and package.jsonThis skill does NOT cover:
skill-tester for runtime validation)dependency-auditor for active CVE scanning)infrastructure-compliance-auditor in ra-qm-team)information-security-manager-iso27001 and gdpr-dsgvo-expert in ra-qm-team)| Skill | Integration | Data Flow |
|---|---|---|
dependency-auditor | Feed audit findings into live CVE scanning for flagged dependencies | Security audit report → dependency-auditor for real-time vulnerability lookup |
ci-cd-pipeline-builder | Embed the audit workflow as a required check in generated CI/CD pipelines | Pipeline template ← audit job YAML from this skill's CI/CD section |
skill-tester | Run dynamic runtime tests on skills that pass static analysis | PASS verdict from this skill → skill-tester for behavioral validation |
infrastructure-compliance-auditor | Extend auditing scope from skill-level to infrastructure-level security controls | Skill audit findings → infrastructure auditor for environment-wide posture review |
env-secrets-manager | Cross-reference credential harvesting findings with secrets management policy | Credential-access flags from audit → env-secrets-manager for policy verification |
pr-review-expert | Surface audit findings as inline PR review comments on flagged lines | Audit report line references → PR review annotations for developer visibility |
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This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.