High Risk
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
H:4 D:1 A:1 C:1
⚠️ Hazard Flags
FS_READ_WORKSPACE FS_WRITE_WORKSPACE FS_WRITE_SYSTEM
📋 Capabilities
Execution
- ❌ Shell execution
- ❌ Code execution
- ❌ Install dependencies
- ❌ Persistence
- Privilege: user
Filesystem
- ✅ Read workspace
- ✅ Write workspace
- ❌ Read home
- ❌ Write home
- ❌ Read system
- ❌ Delete
Network
- Egress: none
- ❌ Ingress
Credentials
- ❌ Environment vars
- ❌ Credential files
- ❌ Browser data
- ❌ Keychain
Actions
❌ send messages❌ post public❌ purchase❌ transfer money❌ deploy❌ delete external
🔒 Containment
Level: maximum
Recommended:
- LOG_ACTIONS: Audit trail for all actions
⚡ Risks
Command injection risk: COMMAND_INJECTION_EVAL, SQL_INJECTION_STRING_FORMAT, MCP_SQL_BLIND critical
Mitigation: Avoid eval(), exec(), and compile(). Use safer alternatives like ast.literal_eval()
Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION low
Mitigation: Provide clear, detailed description of skill functionality
Want a deeper analysis?
This report was generated by static analysis. Get an LLM-powered deep review with behavioral reasoning and attack surface mapping.
🧠 Deep Analysis — $5.00🚨 Incident Response
Kill switch: Stop the agent process
Containment: Review logs for unexpected actions
Recovery: Depends on skill capabilities
📄 Raw SSDS JSON click to expand
{
"meta": {
"document_id": "ssds:auto:\"senior-data-engineer\":2.1.1",
"ssds_version": "0.2.0",
"scanner_version": "0.4.0+fe6fd9123d50",
"created_at": "2026-03-28T09:11:15.918Z",
"created_by": {
"agent": "safeagentskills-cli/generate-ssds"
},
"language": "en",
"notes": "Auto-generated SSDS. Manual review recommended."
},
"skill": {
"name": "Senior Data Engineer",
"version": "2.1.1",
"format": "agent_skill",
"description": "Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.",
"publisher": "ClawHub",
"source": {
"channel": "clawhub",
"slug": "senior-data-engineer",
"owner": "alirezarezvani",
"downloads": 1526,
"stars": 0
},
"artifact": {
"sha256": "adf683cb2a81823b8b633e124f054925e30abd9694655edcfed2683b7628676f",
"hash_method": "files_sorted"
}
},
"capabilities": {
"execution": {
"can_exec_shell": false,
"can_exec_code": false,
"privilege_level": "user",
"can_install_deps": false,
"can_persist": false
},
"filesystem": {
"reads_workspace": true,
"reads_user_home": false,
"reads_system": false,
"writes_workspace": true,
"writes_user_home": false,
"writes_system": true,
"can_delete": false
},
"network": {
"egress": "none",
"ingress": false
},
"credentials": {
"reads_env_vars": false,
"reads_credential_files": false,
"reads_browser_data": false,
"reads_keychain": false
},
"services": [],
"actions": {
"can_send_messages": false,
"can_post_public": false,
"can_purchase": false,
"can_transfer_money": false,
"can_deploy": false,
"can_delete_external": false
},
"prompt_injection_surfaces": [],
"content_types": [
"general"
]
},
"hazards": {
"hdac": {
"H": 4,
"D": 1,
"A": 1,
"C": 1
},
"flags": [
"FS_READ_WORKSPACE",
"FS_WRITE_WORKSPACE",
"FS_WRITE_SYSTEM"
],
"custom_flags": [
{
"code": "SOCIAL_ENGINEERING",
"name": "Social Engineering Risk",
"description": "SOCIAL_ENG_VAGUE_DESCRIPTION: Skill description is too vague or missing"
},
{
"code": "COMMAND_INJECTION",
"name": "Command Injection Risk",
"description": "COMMAND_INJECTION_EVAL, SQL_INJECTION_STRING_FORMAT, MCP_SQL_BLIND: Dangerous code execution functions that can execute arbitrary code"
}
],
"confidence": {
"level": "medium",
"basis": [
"static_analysis"
],
"notes": "Detected 2 security patterns (4 vendored rule hits). Review recommended."
},
"rationale": {
"H": "H4: Critical: Privilege escalation or malware detected",
"D": "D1: Limited data access",
"A": "A1: Local side effects only",
"C": "C1: General content"
}
},
"containment": {
"level": "maximum",
"required": [],
"recommended": [
{
"control": "LOG_ACTIONS",
"reason": "Audit trail for all actions"
}
],
"uncontained_risk": "Risk level depends on manual review of actual capabilities."
},
"risks": {
"risks": [
{
"risk": "Command injection risk: COMMAND_INJECTION_EVAL, SQL_INJECTION_STRING_FORMAT, MCP_SQL_BLIND",
"severity": "critical",
"mitigation": "Avoid eval(), exec(), and compile(). Use safer alternatives like ast.literal_eval()"
},
{
"risk": "Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION",
"severity": "low",
"mitigation": "Provide clear, detailed description of skill functionality"
}
],
"limitations": [
"Static analysis only - runtime behavior not verified"
]
},
"incident_response": {
"kill_switch": [
"Stop the agent process"
],
"containment": [
"Review logs for unexpected actions"
],
"recovery": [
"Depends on skill capabilities"
]
},
"evidence": [
{
"evidence_id": "EV:file-1",
"type": "file_excerpt",
"title": "_meta.json",
"file_path": "_meta.json"
},
{
"evidence_id": "EV:file-2",
"type": "file_excerpt",
"title": "SKILL.md",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:file-3",
"type": "file_excerpt",
"title": "scripts/pipeline_orchestrator.py",
"file_path": "scripts/pipeline_orchestrator.py"
},
{
"evidence_id": "EV:file-4",
"type": "file_excerpt",
"title": "scripts/etl_performance_optimizer.py",
"file_path": "scripts/etl_performance_optimizer.py"
},
{
"evidence_id": "EV:file-5",
"type": "file_excerpt",
"title": "scripts/data_quality_validator.py",
"file_path": "scripts/data_quality_validator.py"
},
{
"evidence_id": "EV:cisco-1",
"type": "file_excerpt",
"title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
"file_path": "SKILL.md"
},
{
"evidence_id": "EV:cisco-2",
"type": "file_excerpt",
"title": "COMMAND_INJECTION_EVAL [CRITICAL] scripts/pipeline_orchestrator.py:335: compile(code, '<string>', 'exec')",
"file_path": "scripts/pipeline_orchestrator.py"
},
{
"evidence_id": "EV:cisco-3",
"type": "file_excerpt",
"title": "SQL_INJECTION_STRING_FORMAT [CRITICAL] scripts/pipeline_orchestrator.py:611: 'sql': f'SELECT * FROM {table}' + (",
"file_path": "scripts/pipeline_orchestrator.py"
},
{
"evidence_id": "EV:cisco-4",
"type": "file_excerpt",
"title": "MCP_SQL_BLIND [HIGH] scripts/etl_performance_optimizer.py:768: df.withColumn(\"salted_key\", concat(col(\"key\"), lit(\"_\"), (rand() * 10).cast(\"int",
"file_path": "scripts/etl_performance_optimizer.py"
}
]
}