MegaSquirt Tuner

· v1.0.2

Low Risk

Megasquirt ECU tuning and calibration using TunerStudio. Use when working with Megasquirt engine management systems for: (1) VE table tuning and fuel map optimization, (2) Ignition timing maps and spark advance, (3) Idle control and warmup enrichment, (4) AFR target tuning and closed-loop feedback, (5) Sensor calibration (TPS, MAP, CLT, IAT, O2), (6) Acceleration enrichment and deceleration fuel cut, (7) Boost control and launch control setup, (8) Datalog analysis and troubleshooting, (9) Base engine configuration and injector setup, (10) MSQ tune file analysis and safety review, (11) Any Megasquirt/TunerStudio ECU tuning tasks.

H:1 D:4 A:0 C:1

⚠️ Hazard Flags

FS_READ_WORKSPACE NET_EGRESS_ANY PI_WEB

📋 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: any
❌ 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

Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION low

Mitigation: Provide clear, detailed description of skill functionality

Data exfiltration patterns: DATA_EXFIL_SENSITIVE_FILES high

Mitigation: Do not access credential files or sensitive system files

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🚨 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:megasquirt-tuner:1.0.2",
    "ssds_version": "0.2.0",
    "scanner_version": "0.4.0+fe6fd9123d50",
    "created_at": "2026-03-05T13:43:45.959Z",
    "created_by": {
      "agent": "safeagentskills-cli/generate-ssds"
    },
    "language": "en",
    "notes": "Auto-generated SSDS. Manual review recommended."
  },
  "skill": {
    "name": "MegaSquirt Tuner",
    "version": "1.0.2",
    "format": "agent_skill",
    "description": "Megasquirt ECU tuning and calibration using TunerStudio. Use when working with Megasquirt engine management systems for: (1) VE table tuning and fuel map optimization, (2) Ignition timing maps and spark advance, (3) Idle control and warmup enrichment, (4) AFR target tuning and closed-loop feedback, (5) Sensor calibration (TPS, MAP, CLT, IAT, O2), (6) Acceleration enrichment and deceleration fuel cut, (7) Boost control and launch control setup, (8) Datalog analysis and troubleshooting, (9) Base engine configuration and injector setup, (10) MSQ tune file analysis and safety review, (11) Any Megasquirt/TunerStudio ECU tuning tasks.",
    "publisher": "unknown",
    "source": {
      "channel": "local"
    },
    "artifact": {
      "sha256": "1b1c1c9256f3d49c45628fd5b2e1dc49345383e7e4a8216c53d43ca340d4962c",
      "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": false,
      "writes_user_home": false,
      "writes_system": false,
      "can_delete": false
    },
    "network": {
      "egress": "any",
      "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": [
      "web"
    ],
    "content_types": [
      "general"
    ]
  },
  "hazards": {
    "hdac": {
      "H": 1,
      "D": 4,
      "A": 0,
      "C": 1
    },
    "flags": [
      "FS_READ_WORKSPACE",
      "NET_EGRESS_ANY",
      "PI_WEB"
    ],
    "custom_flags": [
      {
        "code": "SOCIAL_ENGINEERING",
        "name": "Social Engineering Risk",
        "description": "SOCIAL_ENG_VAGUE_DESCRIPTION: Skill description is too vague or missing"
      },
      {
        "code": "DATA_EXFILTRATION",
        "name": "Data Exfiltration Risk",
        "description": "DATA_EXFIL_SENSITIVE_FILES: Accessing sensitive system or credential files"
      }
    ],
    "confidence": {
      "level": "medium",
      "basis": [
        "static_analysis"
      ],
      "notes": "Detected 2 security patterns (2 vendored rule hits). Review recommended."
    },
    "rationale": {
      "H": "H1: Filesystem reads detected",
      "D": "D4: Critical: Credential theft or data exfiltration",
      "A": "A0: No side effects detected",
      "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": "Social engineering indicators: SOCIAL_ENG_VAGUE_DESCRIPTION",
        "severity": "low",
        "mitigation": "Provide clear, detailed description of skill functionality"
      },
      {
        "risk": "Data exfiltration patterns: DATA_EXFIL_SENSITIVE_FILES",
        "severity": "high",
        "mitigation": "Do not access credential files or sensitive system files"
      }
    ],
    "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": "README.md",
      "file_path": "README.md"
    },
    {
      "evidence_id": "EV:file-2",
      "type": "file_excerpt",
      "title": "scripts/analyze_msq.py",
      "file_path": "scripts/analyze_msq.py"
    },
    {
      "evidence_id": "EV:file-3",
      "type": "file_excerpt",
      "title": "SKILL.md",
      "file_path": "SKILL.md"
    },
    {
      "evidence_id": "EV:file-4",
      "type": "file_excerpt",
      "title": "_meta.json",
      "file_path": "_meta.json"
    },
    {
      "evidence_id": "EV:cisco-1",
      "type": "file_excerpt",
      "title": "DATA_EXFIL_SENSITIVE_FILES [HIGH] scripts/analyze_msq.py:64: with open(filepath, 'r') as f:",
      "file_path": "scripts/analyze_msq.py"
    },
    {
      "evidence_id": "EV:cisco-2",
      "type": "file_excerpt",
      "title": "SOCIAL_ENG_VAGUE_DESCRIPTION [LOW] SKILL.md:1: ---",
      "file_path": "SKILL.md"
    }
  ]
}