local-sensitive

ziwon's avatarfrom ziwon

Routes to local LLM for sensitive operations. Use when user says "로컬에서 처리해줘", "외부로 보내지 마", "민감한 데이터야", "보안 감사해줘", "credential 분석", "시크릿 확인", or handles sensitive data that should not leave the machine.

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[Security Operations]

When & Why to Use This Skill

This Claude skill enhances data privacy and security by routing sensitive operations to local LLMs instead of external APIs. It prevents data exfiltration by ensuring that credentials, secrets, and proprietary code are processed entirely on-machine using models like Qwen and GPT-OSS, making it an essential tool for security-conscious development and compliance.

Use Cases

  • Analyzing source code for hardcoded secrets, API keys, or credentials without exposing them to external cloud services.
  • Performing security audits and compliance checks on sensitive configuration files such as .env, YAML, or Kubernetes manifests locally.
  • Conducting private code reviews for proprietary logic where data residency and strict privacy are mandatory requirements.
  • Securely parsing sensitive configuration data to update secret management platforms like Infisical without exposing tokens in logs.
namelocal-sensitive
descriptionRoutes to local LLM for sensitive operations. Use when user says "로컬에서 처리해줘", "외부로 보내지 마", "민감한 데이터야", "보안 감사해줘", "credential 분석", "시크릿 확인", or handles sensitive data that should not leave the machine.
allowed-toolsBash(.claude/scripts/local-llm.sh:*), Bash(.claude/scripts/infisical-update.sh:*), Bash(kubectl get:*), Read, Grep, Glob

Local LLM for Sensitive Operations

Route security-sensitive tasks to local LLM to prevent data exfiltration.

When to Use

  • Analyzing secrets or credentials
  • Security audit of sensitive code
  • Private code review
  • Compliance checking with sensitive data
  • Any task where data should not leave the machine

Available Models

Model Endpoint Best For
qwen3-coder localhost:8003 Code analysis, generation
gpt-oss localhost:8004 General tasks, tool use

How to Use

.claude/scripts/local-llm.sh qwen3-coder "Review this code for secrets: <code>"
.claude/scripts/local-llm.sh gpt-oss "Analyze security of this config"

Security Guidelines

  1. Never send sensitive data to external APIs
  2. Use this skill when file paths contain: secret, credential, .env, key
  3. Prefer qwen3-coder for code-related tasks
  4. Prefer gpt-oss for general analysis

Example Workflow

  1. User asks: "Check if there are any hardcoded secrets in this file"
  2. Read the file locally
  3. Send to local LLM for analysis
  4. Report findings without exposing actual secret values

Infisical Secret Updates

For updating Infisical secrets with sensitive config data (token never exposed):

# 1. Parse sensitive YAML/JSON with local LLM
.claude/scripts/local-llm.sh qwen3-coder "Parse this config and output JSON: <config>"

# 2. Save to temp file
cat > /tmp/parsed.json << 'EOF'
{"KEY": "value"}
EOF

# 3. Safe update (token fetched internally, never printed)
.claude/scripts/infisical-update.sh safe-parse \
  infisical-app-secrets namespace \
  json /tmp/parsed.json "/path"

# 4. Clean up
rm /tmp/parsed.json

See infisical-manager skill for detailed workflow.