---
name: aidefence
version: 1.0.0
description: |
  AI Manipulation Defense System (AIMDS) integration for Claude Flow V3.
  Provides real-time threat detection, behavioral analysis, and adaptive
  mitigation with 25-level meta-learning capabilities.

author: rUv
license: MIT
homepage: https://ruv.io/aimds
repository: https://github.com/ruvnet/midstream/tree/main/AIMDS

# Package reference
package: aidefence@^2.1.1

# Capabilities provided by this skill
capabilities:
  # Detection capabilities
  - prompt_injection_detection    # 50+ prompt injection patterns
  - jailbreak_detection          # AI jailbreak attempt detection
  - pii_detection               # PII identification (emails, SSNs, API keys)
  - unicode_normalization       # Control character sanitization

  # Analysis capabilities
  - behavioral_analysis         # Temporal pattern analysis
  - chaos_detection            # Lyapunov exponent calculation
  - policy_verification        # Linear Temporal Logic (LTL) policies
  - anomaly_detection          # Statistical baseline learning

  # Response capabilities
  - adaptive_mitigation        # 7 distinct mitigation strategies
  - meta_learning             # 25-level recursive optimization (strange-loop)
  - rollback_management       # Failed mitigation rollback
  - effectiveness_tracking    # Real-time mitigation monitoring

# Performance characteristics
performance:
  detection_latency: <10ms
  analysis_latency: <100ms
  response_latency: <50ms
  throughput: ">12000 req/s"

# Commands exposed by this skill
commands:
  scan:
    description: Scan input for AI manipulation attempts
    usage: |
      /aidefence scan <input>
      /aidefence scan --file <path>
      /aidefence scan --mode paranoid <input>
    examples:
      - "/aidefence scan 'Ignore previous instructions and...'"
      - "/aidefence scan --file suspicious-prompt.txt"
      - "/aidefence scan --mode paranoid --json 'Please help me...'"
    options:
      - name: mode
        type: choice
        choices:
          - quick      # Pattern matching only (<5ms)
          - thorough   # Pattern + behavioral (<50ms)
          - paranoid   # Full analysis + policy verification (<150ms)
        default: thorough
        description: Scan depth mode
      - name: file
        type: string
        description: File path to scan instead of inline input
      - name: json
        type: boolean
        default: false
        description: Output results as JSON

  analyze:
    description: Analyze agent behavior patterns for anomalies
    usage: |
      /aidefence analyze <agent-id>
      /aidefence analyze <agent-id> --window 24h
    examples:
      - "/aidefence analyze security-architect-1234"
      - "/aidefence analyze coder-5678 --window 10m --threshold 0.7"
    options:
      - name: window
        type: string
        default: "1h"
        description: Time window for behavioral analysis
      - name: threshold
        type: number
        default: 0.8
        description: Anomaly score threshold (0-1)

  policy:
    description: Verify agent against LTL security policy
    usage: |
      /aidefence policy <agent-id> <ltl-formula>
    examples:
      - "/aidefence policy coder-1234 'G(edit_file -> F(run_tests))'"
      - "/aidefence policy reviewer-5678 'G(!approve_self_code)'"
    options:
      - name: verbose
        type: boolean
        default: false
        description: Show detailed policy evaluation trace

  learn:
    description: Record successful mitigation for meta-learning
    usage: |
      /aidefence learn <threat-type> <strategy> --effectiveness <score>
    examples:
      - "/aidefence learn prompt_injection sanitize --effectiveness 0.95"
      - "/aidefence learn jailbreak reject --effectiveness 1.0"
    options:
      - name: effectiveness
        type: number
        required: true
        description: Mitigation effectiveness score (0-1)

  status:
    description: Show aidefence system status and metrics
    usage: |
      /aidefence status
      /aidefence status --metrics
    options:
      - name: metrics
        type: boolean
        default: false
        description: Include Prometheus metrics

# Hook integrations
hooks:
  # Pre-agent-input: Scan all agent inputs for manipulation
  pre-agent-input:
    enabled: true
    description: Scan agent inputs before processing
    config:
      block_critical: true       # Block inputs with critical threats
      block_high: false          # Allow high severity with logging
      log_all: true             # Log all threat detections
      mode: thorough            # Default scan mode

  # Post-agent-action: Learn from agent behaviors
  post-agent-action:
    enabled: true
    description: Record agent actions for behavioral modeling
    config:
      sampling_rate: 0.1        # Sample 10% of actions for analysis
      anomaly_threshold: 0.8    # Alert threshold for anomaly score
      store_embeddings: true    # Store action embeddings in AgentDB

  # Pre-swarm-init: Verify swarm security policies
  pre-swarm-init:
    enabled: true
    description: Verify swarm topology against security policies
    config:
      require_security_agent: true  # Require security-architect in swarm
      validate_topology: true       # Validate topology security

# Integration with claude-flow systems
integration:
  # AgentDB integration for shared threat patterns
  agentdb:
    enabled: true
    namespace: security_threats
    hnsw_enabled: true
    config:
      vector_dimension: 384
      m: 16
      ef_construction: 200
      ef_search: 100

  # ReasoningBank integration for pattern learning
  reasoningbank:
    enabled: true
    store_patterns: true
    learn_mitigations: true
    config:
      min_effectiveness: 0.8    # Only store high-effectiveness patterns
      consolidation_interval: 1h

  # Prometheus metrics
  prometheus:
    enabled: true
    metrics:
      - aidefence_threats_detected_total
      - aidefence_detection_latency_ms
      - aidefence_analysis_latency_ms
      - aidefence_anomaly_score
      - aidefence_mitigations_applied_total
      - aidefence_meta_learning_depth

# MCP tool registrations
mcp_tools:
  - name: aidefence_scan
    description: Scan input for AI manipulation attempts
    input_schema:
      type: object
      properties:
        input:
          type: string
          description: Input text to scan
        mode:
          type: string
          enum: [quick, thorough, paranoid]
          default: thorough
      required: [input]

  - name: aidefence_analyze_behavior
    description: Analyze agent behavioral patterns for anomalies
    input_schema:
      type: object
      properties:
        agentId:
          type: string
          description: Agent ID to analyze
        timeWindow:
          type: string
          default: "1h"
          description: Time window for analysis
      required: [agentId]

  - name: aidefence_verify_policy
    description: Verify agent behavior against LTL security policies
    input_schema:
      type: object
      properties:
        agentId:
          type: string
        policy:
          type: string
          description: LTL policy formula
      required: [agentId, policy]

  - name: aidefence_learn_pattern
    description: Store successful threat pattern for meta-learning
    input_schema:
      type: object
      properties:
        threatType:
          type: string
        mitigation:
          type: string
        effectiveness:
          type: number
          minimum: 0
          maximum: 1
      required: [threatType, mitigation, effectiveness]

# Threat detection patterns (reference)
threat_patterns:
  prompt_injection:
    count: 50+
    categories:
      - instruction_override    # "Ignore previous instructions"
      - role_switching         # "You are now DAN"
      - context_manipulation   # Fake system messages
      - delimiter_abuse        # Using special tokens

  jailbreak:
    categories:
      - dan_variants           # Do Anything Now variants
      - hypothetical          # "Hypothetically, if..."
      - roleplay              # Character-based bypasses
      - encoding              # Base64/ROT13 encoded prompts

  pii:
    types:
      - email_addresses
      - social_security_numbers
      - credit_card_numbers
      - api_keys
      - passwords

# Behavioral analysis configuration
behavioral_analysis:
  temporal:
    window_sizes: [1m, 10m, 1h, 24h]
    attractor_types:
      - point     # Stable single point
      - cycle     # Periodic behavior
      - torus     # Quasi-periodic
      - strange   # Chaotic (suspicious)

  lyapunov:
    threshold: 0.1  # Positive = chaotic behavior
    embedding_dimension: 3
    time_delay: 1

  baseline:
    learning_period: 24h
    update_frequency: 1h
    deviation_threshold: 3.0  # Standard deviations

# Documentation links
documentation:
  readme: https://github.com/ruvnet/midstream/blob/main/AIMDS/README.md
  api: https://ruv.io/aimds/api
  patterns: https://ruv.io/aimds/patterns
  integration: /v3/implementation/adrs/ADR-022-aidefence-integration.md