reporting

name	reporting
description	Generate comprehensive multi-tenant security and operational reports from LimaCharlie. Provides billing summaries, usage roll-ups, detection trends, sensor health monitoring, and configuration audits across multiple organizations. Supports both per-tenant detailed breakdowns and cross-tenant aggregated roll-ups. Built with strict data accuracy guardrails to prevent fabricated metrics. Supports partial report generation when some organizations fail, with transparent error documentation. Time windows always displayed, detection limits clearly flagged, zero cost calculations.
allowed-tools	["Task","Read","Bash","Skill","Write"]

LimaCharlie Reporting Skill

---

LimaCharlie Integration

Prerequisites: Run /init-lc to initialize LimaCharlie context.

LimaCharlie CLI Access

All LimaCharlie operations use the limacharlie CLI directly:

limacharlie <noun> <verb> --oid <oid> --output yaml [flags]

For command help and discovery: limacharlie <command> --ai-help

Critical Rules

Rule	Wrong	Right
CLI Access	Call MCP tools or spawn api-executor	Use Bash("limacharlie ...") directly
Output Format	--output json	--output yaml (more token-efficient)
Filter Output	Pipe to jq/yq	Use --filter JMESPATH to select fields
LCQL Queries	Write query syntax manually	Use limacharlie ai generate-query first
Timestamps	Calculate epoch values	Use date +%s or date -d '7 days ago' +%s
OID	Use org name	Use UUID (call limacharlie org list if needed)

---

Overview

This skill enables AI-assisted generation of comprehensive security and operational reports across LimaCharlie organizations. It provides structured access to billing data, usage statistics, detection summaries, sensor health, and configuration audits. Supports both per-tenant detailed reports and cross-tenant aggregated roll-ups.

Core Philosophy: Accuracy over completeness. This skill prioritizes data accuracy with strict guardrails that make fabricated metrics impossible. Reports clearly document what data is available, what failed, and what limits were applied.

Purpose

• Generate multi-tenant reports across 50+ organizations
• Provide billing and usage summaries for customer invoicing
• Analyze security detection trends across customer base
• Monitor sensor health and deployment status
• Audit organizational configurations
• Track operational metrics for capacity planning
• Support partial report generation with clear error documentation

When to Use This Skill

Use this skill when you need to:

Multi-Tenant MSSP Reports

• "Generate monthly report for all my customers" - Comprehensive overview across all organizations
• "Billing summary for November 2025" - Usage and billing data for invoicing period
• "Show me customer health dashboard" - Sensor status and detection trends across clients
• "Which customers had the most detections this month?" - Security activity ranking
• "Export usage data for all organizations" - Bulk data extraction for analysis

Single Organization Deep Dives

• "Detailed report for Client ABC" - Complete organizational analysis
• "Security posture for organization XYZ" - Detection and rule effectiveness
• "Sensor health for customer PDQ" - Endpoint deployment and status

Billing and Usage Analysis

• "Usage trends across all customers" - Comparative analysis for capacity planning
• "Which orgs are using the most data?" - Resource consumption identification
• "Show subscription status for all clients" - Billing health check

Operational Monitoring

• "How many sensors are offline across all orgs?" - Fleet health monitoring
• "Detection volume trends this month" - Security activity patterns
• "Which customers need attention?" - Issue identification and prioritization

Report Templates

This skill supports structured JSON templates that define input schemas, output schemas, and data sources for each report type. Templates are located in skills/reporting/templates/.

Template	Description	Scope
billing-report.json	Invoice-focused billing data with SKU breakdown	single / all
mssp-executive-report.json	High-level fleet health for MSSP leadership	single / all
customer-health-report.json	Comprehensive customer success tracking	single / all
detection-analytics-report.json	Detection volume, categories, and trends	single / all

Template Structure

Each template defines:

• Input schema: Required and optional parameters with types and validation
• Output schema: Expected JSON structure for structured data consumers
• Data sources: Which API calls populate each field

Using Templates

1. Read the template to understand required inputs
2. Validate user input against the input schema
3. Use the orchestration layer (parallel subagents) for data collection
4. Format output to match the output schema
5. Display results in the console as formatted markdown/tables (default) - HTML output only when user explicitly requests it

Templates ensure consistency across reports and enable:

• Programmatic consumption of report data
• Validation of inputs and outputs
• Clear documentation of data sources
• Reproducible report generation

Critical Prerequisites

Authentication

Ensure you are authenticated to LimaCharlie with access to target organizations:

• User must have permissions across multiple organizations (MSSP/partner account)
• Billing data requires admin/owner role per organization
• Usage statistics accessible with standard read permissions

Understanding Organization IDs (OIDs)

⚠️ CRITICAL: Organization ID (OID) is a UUID (like c7e8f940-1234-5678-abcd-1234567890ab), NOT the organization name.

• Use limacharlie org list to get OID from organization name
• All API calls require the UUID, not the friendly name
• OIDs are permanent identifiers (names can change)

Time Range Requirements

⚠️ MANDATORY: Prompt User for Time Range

Before generating any report that requires detection or event data, you MUST ask the user to confirm or specify the time range using the AskUserQuestion tool:

AskUserQuestion(
  questions=[{
    "question": "What time range should I use for this report?",
    "header": "Time Range",
    "options": [
      {"label": "Last 24 hours", "description": "Most recent day of data"},
      {"label": "Last 7 days", "description": "Past week of activity"},
      {"label": "Last 30 days", "description": "Past month of activity"},
      {"label": "Custom range", "description": "I'll specify exact dates"}
    ],
    "multiSelect": false
  }]
)

If user selects "Custom range", follow up to get specific start/end dates.

Core Requirements:

• All reports MUST specify explicit time ranges
• Time windows MUST be displayed in every report section
• NEVER assume a default time range without user confirmation
• Maximum recommended range: 90 days (API limitations)

⚠️ CRITICAL: Dynamic Timestamp Calculation

NEVER use hardcoded epoch values from examples or documentation!

ALWAYS calculate timestamps dynamically using bash before making API calls:

# Get current Unix timestamp
NOW=$(date +%s)

# Calculate relative time ranges
HOURS_24_AGO=$((NOW - 86400))        # 24 hours = 86400 seconds
DAYS_7_AGO=$((NOW - 604800))          # 7 days = 604800 seconds
DAYS_30_AGO=$((NOW - 2592000))        # 30 days = 2592000 seconds
DAYS_90_AGO=$((NOW - 7776000))        # 90 days = 7776000 seconds

# For specific date ranges (user-provided)
START=$(date -d "2025-11-01 00:00:00 UTC" +%s)
END=$(date -d "2025-11-30 23:59:59 UTC" +%s)

# Display human-readable for confirmation
echo "Time range: $(date -d @$START) to $(date -d @$END)"

Why This Matters:

• The detection API (get_historic_detections) uses Unix epoch timestamps in SECONDS
• Using stale or example timestamps (like those in documentation) returns NO DATA
• The API only returns detections within the specified time window
• Incorrect timestamps = empty results = incorrect reports

Validation Before API Call:

# Verify timestamps are reasonable
if [ $START -gt $END ]; then
  echo "ERROR: Start time is after end time"
  exit 1
fi

if [ $END -gt $NOW ]; then
  echo "WARNING: End time is in the future, using current time"
  END=$NOW
fi

Data Accuracy Guardrails

Principle 1: NEVER Fabricate Data

Absolute Rules:

• ❌ NEVER estimate, infer, or extrapolate data not in API responses
• ❌ NEVER calculate costs (no pricing data in API)
• ❌ NEVER guess at missing fields
• ❌ NEVER assume "standard" values
• ❌ NEVER substitute placeholder data for errors

Always:

• ✅ Show "N/A" or "Data unavailable" for missing fields
• ✅ Display explicit warnings when limits reached
• ✅ Document all errors and failures prominently
• ✅ State "Retrieved X of potentially more" when truncated
• ✅ Link to invoice URLs for billing details

Principle 2: Detection Limit Handling

Default Limit: 5,000 detections per organization

Required Workflow:

1. Query with limit=5000
2. Track retrieved_count
3. Check: limit_reached = (retrieved_count >= 5000)
4. If limit_reached:
   ⚠️ DISPLAY PROMINENT WARNING
   "DETECTION LIMIT REACHED
    Retrieved: 5,000 detections
    Actual count: May be significantly higher

    For complete data:
      - Narrow time range
      - Query specific date ranges
      - Filter by category or sensor"

Never Say:

• ❌ "Total detections: 5,000" (implies this is complete)
• ❌ "Approximately 5,000 detections" (ambiguous)

Always Say:

• ✅ "Retrieved 5,000 detections (limit reached - actual count may be higher)"
• ✅ "Detection sample: First 5,000 of potentially more"

Principle 3: Pricing and Billing

Absolute Rule: ZERO Cost Calculations

What You CAN Show:

• ✅ Usage metrics: events, data output (GB), evaluations, peak sensors
• ✅ Billing metadata: plan name, status, next billing date
• ✅ Invoice links: get-org-invoice-url for actual costs

What You CANNOT Do:

• ❌ Calculate costs based on usage
• ❌ Estimate bills
• ❌ Multiply usage by assumed rates
• ❌ Project future costs
• ❌ Compare plan pricing

Even if user provides rates:

• ❌ Don't perform calculations
• ✅ Show usage metrics
• ✅ Provide invoice link
• ✅ State: "For billing details, see invoice: [URL]"

Principle 4: Time Window Display

MANDATORY in Every Report:

Header (always visible):
  Generated: 2025-11-20 14:45:30 UTC
  Time Window: 2025-11-01 00:00:00 UTC to 2025-11-30 23:59:59 UTC (30 days)
  Organizations: 45 of 50 processed successfully

Per Section:
  ── Usage Statistics ──
  Data Retrieved: 2025-11-20 14:45:35 UTC
  Coverage Period: Nov 1-30, 2025 (30 days)
  Source: get-usage-stats API
  Data Freshness: Daily updates (24hr delay typical)

Principle 5: Error Transparency

Partial Reports Are Acceptable:

• Generate reports even if some organizations fail
• Clearly document which organizations failed and why
• Never silently skip failed organizations
• Provide actionable remediation steps

Error Documentation Template:

⚠️ FAILED ORGANIZATIONS (3 of 50)

Client ABC (oid: c7e8f940-...)
  Status: ❌ Failed
  Error: 403 Forbidden
  Endpoint: get-billing-details
  Reason: Insufficient permissions
  Impact: Billing data unavailable
  Action: Grant billing:read permission
  Timestamp: 2025-11-20 14:32:15 UTC

Available Data Sources

1. Multi-Tenant Discovery

CLI: limacharlie org list

• Endpoint: GET /v1/user/orgs
• OID Required: NO (user-level operation)
• Returns: List of accessible organizations with OIDs and names
• Usage: Starting point for all multi-tenant operations
• Data Freshness: Real-time

Response Structure:

{
  "orgs": [
    {
      "oid": "c7e8f940-1234-5678-abcd-1234567890ab",
      "name": "Client ABC Production",
      "role": "owner"
    },
    {
      "oid": "c7e8f940-5678-1234-dcba-0987654321ab",
      "name": "Client XYZ Security",
      "role": "admin"
    }
  ],
  "total": 2
}

Validation:

• Check orgs array exists and not empty
• Verify each OID is valid UUID format
• Confirm role is "owner", "admin", or "user"

2. Organization Metadata

CLI: limacharlie org info

• Endpoint: GET /v1/orgs/{oid}
• OID Required: YES
• Returns: Organization details, creation date, settings
• Data Freshness: Real-time

Response Structure:

{
  "oid": "c7e8f940-...",
  "name": "Client ABC",
  "created": 1672531200,
  "creator": "[e-mail]"
}

3. Usage Statistics

CLI: limacharlie org stats

• Endpoint: GET /v1/usage/{oid}
• OID Required: YES
• Returns: Daily usage metrics (~90 days historical)
• Data Freshness: Daily aggregation, 24-hour delay typical

Response Structure:

{
  "usage": {
    "2025-11-06": {
      "sensor_events": 131206,
      "output_bytes_tx": 500123456,
      "replay_num_evals": 435847,
      "peak_sensors": 4
    },
    "2025-11-07": {
      "sensor_events": 145821,
      "output_bytes_tx": 523456789,
      "replay_num_evals": 478932,
      "peak_sensors": 4
    }
  }
}

Field Definitions:

• sensor_events: Total events ingested from sensors
• output_bytes_tx: Data transmitted to outputs (in bytes)
• replay_num_evals: D&R rule evaluations performed
• peak_sensors: Maximum concurrent sensors online

Critical Notes:

• API returns ~90 days of data
• MUST filter to requested time range - don't use all 90 days
• Dates are in YYYY-MM-DD format
• output_bytes_tx is in BYTES - convert to GB: divide by 1,073,741,824
• ALWAYS show both: "450 GB (483,183,820,800 bytes)"

Aggregation Rules:

For time range Nov 1-30, 2025:
  1. Filter usage dict to only dates in range
  2. Sum daily values:
     total_events = sum(usage[date]['sensor_events'] for date in range)
  3. Document calculation:
     "Total Events: 1,250,432,100
      Calculation: Sum of daily sensor_events from Nov 1-30, 2025
      Source: get-usage-stats"

4. Billing Details

CLI: limacharlie billing details

• Endpoint: GET /orgs/{oid}/details (billing endpoint)
• OID Required: YES
• Permissions: Requires admin/owner role
• Returns: Subscription info, payment status, billing contact
• Data Freshness: Updated on changes, ~1hr delay

Response Structure:

{
  "plan": "enterprise",
  "status": "active",
  "billing_email": "[e-mail]",
  "payment_method": "card",
  "last_four": "4242",
  "next_billing_date": 1672531200,
  "auto_renew": true
}

Common Error: 403 Forbidden (insufficient permissions)

• Expected for user-role accounts
• Document in failures section
• Continue with partial data

5. Invoice URLs

CLI: limacharlie billing invoice-url

• Endpoint: GET /orgs/{oid}/invoice
• OID Required: YES
• Returns: Direct URL to organization's invoice
• Usage: For actual billing amounts (no cost calculations in reports)

Response Structure:

{
  "url": "https://billing.limacharlie.io/invoice/..."
}

Usage in Reports:

For billing details and charges:
→ View Invoice: https://billing.limacharlie.io/invoice/...

6. Sensor Inventory

CLI: limacharlie sensor list

• Endpoint: GET /v1/sensors/{oid}
• OID Required: YES
• Returns: All sensors with metadata
• Data Freshness: Real-time snapshot
• Large Result Handling: May return resource_link if >100KB

Response Structure (normal):

{
  "sensors": {
    "sensor-id-1": {
      "sid": "sensor-id-1",
      "hostname": "SERVER01",
      "plat": 268435456,
      "arch": 1,
      "enroll": "2024-01-15T10:30:00Z",
      "alive": "2024-11-20 14:22:13",
      "int_ip": "10.0.1.50",
      "ext_ip": "203.0.113.45",
      "oid": "c7e8f940-..."
    }
  },
  "continuation_token": ""
}

Large Result Handling:

For large result sets, pipe CLI output to a file:

# Save large results to file
limacharlie sensor list --oid <oid> --output yaml > /tmp/sensors.yaml

# Or use --filter to extract needed fields directly
limacharlie sensor list --oid <oid> --filter "length(sensors)" --output yaml  # Count
limacharlie sensor list --oid <oid> --filter "sensors.*.hostname" --output yaml  # Hostnames

Field Validation - CRITICAL:

CORRECT Fields to Use:

• ✅ alive: "2025-11-20 14:22:13" (datetime string for last seen)
• ✅ plat: Platform code (int or string)
• ✅ hostname: Sensor hostname
• ✅ sid: Sensor ID
• ✅ int_ip: Internal IP address
• ✅ ext_ip: External IP address

INCORRECT Fields (Common Mistakes):

• ❌ last_seen: Often 0 or missing - DO NOT USE
• ❌ Use alive field instead for offline detection

Offline Sensor Detection:

# Parse alive field (datetime string format: "YYYY-MM-DD HH:MM:SS")
from datetime import datetime, timezone

alive_str = sensor_info.get('alive', '')
if alive_str:
    # Parse: "2025-10-01 17:08:10"
    alive_dt = datetime.strptime(alive_str, '%Y-%m-%d %H:%M:%S')
    alive_dt = alive_dt.replace(tzinfo=timezone.utc)
    last_seen_timestamp = alive_dt.timestamp()

    hours_offline = (current_time - last_seen_timestamp) / 3600

    # Categorize with explicit thresholds:
    if hours_offline < 24:
        category = "Recently offline (< 24 hours)"
    elif hours_offline < 168:  # 7 days
        category = "Offline short term (1-7 days)"
    elif hours_offline < 720:  # 30 days
        category = "Offline medium term (7-30 days)"
    else:
        category = "Offline long term (30+ days)"

Platform Code Translation:

Traditional OS platforms (strings):

• "windows" → Windows
• "linux" → Linux
• "macos" → macOS
• "chrome" → Chrome OS

Numeric platform codes (extensions/adapters):

• Use two-pass pattern analysis
• Collect hostname samples
• Match patterns: ext-, test-, slack-, office365-
• ALWAYS show sample hostnames in report

Example:

Platform: LimaCharlie Extensions (code: 2415919104)
Sample hostnames: ext-strelka-01, ext-hayabusa-02, ext-secureannex-01
Sensor count: 30

7. Online Sensors

CLI: limacharlie sensor list --online

• Endpoint: GET /v1/sensors/online/{oid}
• OID Required: YES
• Returns: List of currently online sensor IDs
• Data Freshness: Real-time

Response Structure:

{
  "sensors": [
    "sensor-id-1",
    "sensor-id-2",
    "sensor-id-3"
  ]
}

Usage:

# Convert to set for O(1) lookup
online_sids = set(response['sensors'])

# Check if sensor is online
is_online = sensor_id in online_sids

# Calculate offline count
total_sensors = 2500
online_count = len(online_sids)
offline_count = total_sensors - online_count

8. Historic Detections

CLI: limacharlie detection list

• Endpoint: GET /v1/insight/{oid}/detections
• OID Required: YES
• Returns: Security detections within time range
• Data Freshness: Near real-time (5-minute delay typical)
• Default Limit: 5,000 per query

Query Parameters:

• start: Unix epoch timestamp (seconds)
• end: Unix epoch timestamp (seconds)
• limit: Maximum detections to retrieve (default: 5000)
• sid: Filter by sensor ID (optional)
• cat: Filter by category (optional)

Response Structure:

{
  "detects": [
    {
      "detect_id": "detect-uuid-123",
      "cat": "suspicious_process",
      "source_rule": "general.encoded-powershell",
      "namespace": "general",
      "ts": 1732108934567,
      "sid": "sensor-xyz-123",
      "detect": {
        "event": {
          "TIMESTAMP": 1732108934567,
          "COMMAND_LINE": "powershell.exe -encodedCommand ...",
          "FILE_PATH": "C:\\Windows\\System32\\..."
        },
        "routing": {
          "sid": "sensor-xyz-123",
          "hostname": "SERVER01"
        }
      }
    }
  ],
  "next_cursor": ""
}

Field Validation - CRITICAL:

CORRECT Fields:

• ✅ source_rule: "namespace.rule-name" (actual rule identifier)
• ✅ cat: Category name
• ✅ ts: Timestamp (MAY be seconds or milliseconds - normalize!)
• ✅ sid: Sensor ID (may be "N/A" for some detections)
• ✅ detect_id: Unique detection identifier

INCORRECT Fields (Common Mistakes):

• ❌ rule_name: Doesn't exist - use source_rule instead
• ❌ severity: NOT in detection records (only in D&R rule config)

Timestamp Normalization (MANDATORY):

ts = detection.get('ts', 0)

# Check magnitude to determine units
if ts > 10000000000:
    # Milliseconds - convert to seconds
    ts = ts / 1000

# Sanity check result
if ts < 1577836800:  # Before 2020-01-01
    # Invalid timestamp
    display = "Invalid timestamp"
elif ts > time.time() + 86400:  # More than 1 day in future
    # Invalid timestamp
    display = "Invalid timestamp"
else:
    # Valid - format for display
    display = datetime.fromtimestamp(ts, tz=timezone.utc).strftime('%Y-%m-%d %H:%M:%S UTC')

Detection Limit Tracking (MANDATORY):

retrieved_count = 0
for detection in detections:
    retrieved_count += 1

limit_reached = (retrieved_count >= query_limit)

if limit_reached:
    # MUST display prominent warning
    warning = f"""
    ⚠️ DETECTION LIMIT REACHED
    Retrieved: {retrieved_count:,} detections
    Actual count: May be significantly higher

    This organization has more detections than retrieved.
    For complete data:
      - Narrow time range (currently: {days} days)
      - Query specific date ranges separately
      - Filter by category or sensor
    """

9. D&R Rules Inventory

CLI: limacharlie dr list

• Endpoint: GET /v1/rules/{oid}?namespace=general
• OID Required: YES
• Returns: Custom D&R rules in general namespace
• Data Freshness: Real-time

Response Structure:

{
  "custom-rule-1": {
    "name": "custom-rule-1",
    "namespace": "general",
    "detect": {
      "event": "NEW_PROCESS",
      "op": "contains",
      "path": "event/COMMAND_LINE",
      "value": "powershell"
    },
    "respond": [
      {
        "action": "report",
        "name": "suspicious_powershell"
      }
    ],
    "is_enabled": true
  }
}

Usage:

• Count total rules: Object.keys(response).length
• Count enabled: Filter where is_enabled === true
• List detection types: Extract event types from detect blocks

10. Outputs Configuration

CLI: limacharlie output list

• Endpoint: GET /v1/outputs/{oid}
• OID Required: YES
• Returns: Configured data outputs (SIEM, storage, webhooks)
• Data Freshness: Real-time

Usage in Reports:

• Count total outputs
• List destination types
• Note any disabled outputs

Workflow Patterns

Architecture Overview

This skill uses a parallel subagent architecture for efficient multi-tenant data collection:

┌─────────────────────────────────────────────────────────────┐
│  reporting (this skill)                                │
│  ├─ Phase 1: Discovery (list orgs via CLI)                   │
│  ├─ Phase 2: Time range validation                          │
│  ├─ Phase 3: Spawn parallel agents ────────────────────┐    │
│  ├─ Phase 4: Aggregate results                         │    │
│  └─ Phase 5: Generate report                           │    │
└────────────────────────────────────────────────────────┼────┘
                                                         │
    ┌────────────────────────────────────────────────────┘
    │
    │  Spawns ONE agent per organization (in parallel)
    │
    ▼
┌───────────┐  ┌───────────┐  ┌───────────┐  ┌───────────┐
│org-       │  │org-       │  │org-       │  │org-       │
│reporter   │  │reporter   │  │reporter   │  │reporter   │
│  Org 1    │  │  Org 2    │  │  Org 3    │  │  Org N    │
└─────┬─────┘  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘
      │              │              │              │
      │  Each agent collects ALL data for its org:
      │  - org info, usage, billing, sensors,
      │  - detections, rules, outputs
      │              │              │              │
      └──────────────┴──────┬───────┴──────────────┘
                            │
                            ▼
                    Structured JSON results
                    returned to parent skill

Benefits of this architecture:

• True parallelism across organizations
• Each agent handles its own error recovery
• Reduced context usage in main skill
• Scalable to 50+ organizations

Output Format Requirements

Default Output: Console (Formatted Markdown)

By default, ALL report data MUST be displayed directly in the console as formatted markdown tables and text. This includes:

• Executive summary with key metrics
• Per-organization breakdowns
• Aggregate statistics and rollups
• All warnings, errors, and data limitations
• Methodology and data provenance sections

Console Output Characteristics:

• Formatted tables using markdown pipe syntax
• Status indicators as text badges: [GREEN], [YELLOW], [RED]
• Numeric formatting with thousand separators
• All data visible without requiring external files

HTML Output: Only When Explicitly Requested

HTML visualization should ONLY be generated when the user explicitly requests it using phrases like:

• "generate as HTML"
• "create HTML dashboard"
• "export to HTML"
• "visual report"
• "interactive dashboard"

If user requests HTML output:

1. First collect and display data in console (so user sees results immediately)
2. Then spawn html-renderer agent to create the HTML file
3. Save to /tmp/{report-name}-{date}.html
4. Open in browser

NEVER automatically generate HTML - console output is always the default.

Pattern 1: Multi-Tenant MSSP Comprehensive Report

Template Reference: templates/mssp-executive-report.json

User Request Examples:

• "Generate monthly MSSP report for all my customers"
• "Show me comprehensive overview across all organizations"
• "Create security and billing summary for November 2025"

Step-by-Step Execution:

┌─ PHASE 1: DISCOVERY ──────────────────────────────┐
│ 1. Use CLI to get org list:                       │
│    limacharlie org list --output yaml              │
│                                                    │
│ 2. Validation:                                    │
│    ✓ Check orgs array exists and not empty        │
│    ✓ Validate each OID is UUID format             │
│    ✓ Count total organizations                    │
│                                                    │
│ 3. User Confirmation (if >20 orgs):              │
│    "Found 50 organizations. Generate report for   │
│     all 50? This may take a few minutes."         │
│                                                    │
│    Options to present:                            │
│    - Yes, process all 50                          │
│    - No, let me filter first                      │
│    - Show me the organization list                │
└────────────────────────────────────────────────────┘

┌─ PHASE 2: TIME RANGE - ASK USER & CALCULATE ──────┐
│                                                    │
│ 4. ⚠️ MANDATORY: Ask user for time range:         │
│                                                    │
│    Use AskUserQuestion tool:                      │
│    - "Last 24 hours"                              │
│    - "Last 7 days"                                │
│    - "Last 30 days"                               │
│    - "Custom range"                               │
│                                                    │
│    If "Custom range", ask for specific dates.     │
│    NEVER assume or default without asking!        │
│                                                    │
│ 5. ⚠️ CRITICAL: Calculate timestamps dynamically: │
│                                                    │
│    ```bash                                        │
│    NOW=$(date +%s)                                │
│    # Based on user selection:                     │
│    # 24h:  START=$((NOW - 86400))                 │
│    # 7d:   START=$((NOW - 604800))                │
│    # 30d:  START=$((NOW - 2592000))               │
│    END=$NOW                                       │
│    ```                                            │
│                                                    │
│    NEVER use hardcoded epoch values!              │
│    Stale timestamps = NO DATA returned!           │
│                                                    │
│ 6. Validation Checks:                             │
│    ✓ Start timestamp < End timestamp              │
│    ✓ End timestamp <= Current time                │
│    ✓ Range is reasonable (<= 90 days)            │
│    ✓ Timestamps are Unix epoch in SECONDS         │
│                                                    │
│ 7. Display for user confirmation:                 │
│    "Time Range:                                    │
│     - Start: [calculated date] UTC                │
│     - End: [calculated date] UTC                  │
│     - Duration: X days                            │
│     - Unix: [start_epoch] to [end_epoch]"         │
└────────────────────────────────────────────────────┘

┌─ PHASE 3: SPAWN PARALLEL AGENTS ──────────────────┐
│ 7. Spawn org-reporter agents IN PARALLEL:   │
│                                                    │
│    CRITICAL: Send ALL Task calls in a SINGLE      │
│    message to achieve true parallelism:           │
│                                                    │
│    Task(                                          │
│      subagent_type="lc-essentials:org-reporter",  │
│      prompt="Collect reporting data for org       │
│        'Client ABC' (OID: uuid-1)                 │
│        Time Range:                                │
│        - Start: 1730419200                        │
│        - End: 1733011199                          │
│        Detection Limit: 5000"                     │
│    )                                              │
│    Task(                                          │
│      subagent_type="lc-essentials:org-reporter",  │
│      prompt="Collect reporting data for org       │
│        'Client XYZ' (OID: uuid-2)..."             │
│    )                                              │
│    ... (one Task per organization)                │
│                                                    │
│ 8. Each agent returns structured JSON:           │
│    {                                              │
│      "org_name": "...",                           │
│      "oid": "...",                                │
│      "status": "success|partial|failed",          │
│      "data": { usage, billing, sensors, ... },    │
│      "errors": [...],                             │
│      "warnings": [...]                            │
│    }                                              │
│                                                    │
│ 9. Wait for all agents to complete               │
└────────────────────────────────────────────────────┘

┌─ PHASE 4: AGGREGATE RESULTS ──────────────────────┐
│ 10. Categorize agent results:                    │
│     success_orgs = [] (status == "success")       │
│     partial_orgs = [] (status == "partial")       │
│     failed_orgs = [] (status == "failed")         │
│                                                    │
│ 11. Multi-Org Aggregation:                       │
│     Aggregate across SUCCESS + PARTIAL orgs:      │
│     - Sum total_events (from usage)               │
│     - Sum total_output_bytes (convert to GB)      │
│     - Sum total_evaluations                       │
│     - Sum peak_sensors                            │
│     - Count total sensors                         │
│     - Count total detections (track limits)       │
│                                                    │
│     Document for each aggregate:                  │
│     - Formula used                                │
│     - Orgs included count                         │
│     - Orgs excluded (and why)                     │
│     - Time range covered                          │
└────────────────────────────────────────────────────┘

┌─ PHASE 5: REPORT GENERATION (CONSOLE OUTPUT) ─────┐
│ ⚠️ DEFAULT: Display ALL data in console as        │
│    formatted markdown. HTML only if requested.    │
│                                                    │
│ 17. Console Report Structure:                     │
│                                                    │
│     A. HEADER (mandatory metadata)                │
│        ═══════════════════════════════════════    │
│        MSSP Comprehensive Report                  │
│        Generated: 2025-11-20 14:45:30 UTC         │
│        Time Window: Nov 1-30, 2025 (30 days)      │
│        Organizations: 45 of 50 successful         │
│        ═══════════════════════════════════════    │
│                                                    │
│     B. EXECUTIVE SUMMARY                          │
│        - High-level metrics (successful orgs)     │
│        - Critical warnings and alerts             │
│        - Failed organization count                │
│        - Detection limit warnings                 │
│                                                    │
│     C. AGGREGATE METRICS                          │
│        Total Across 45 Organizations              │
│        (Excluded: 5 orgs - see failures section)  │
│                                                    │
│        - Total Sensor Events: 1,250,432,100       │
│          Calculation: Sum of daily sensor_events  │
│          from Nov 1-30, 2025                      │
│                                                    │
│        - Total Data Output: 3,847 GB              │
│          (4,128,394,752,000 bytes)                │
│          Calculation: Sum of daily output_bytes_tx│
│          ÷ 1,073,741,824                          │
│                                                    │
│        - Peak Sensors: 12,450                     │
│          Calculation: Sum of max peak_sensors     │
│                                                    │
│     D. PER-ORGANIZATION DETAILS                   │
│        For each successful organization:          │
│                                                    │
│        ── Client ABC ─────────────────────────    │
│        OID: c7e8f940-1234-5678-abcd-...           │
│        Data Retrieved: 2025-11-20 14:45:35 UTC    │
│                                                    │
│        Usage Statistics (Nov 1-30, 2025):         │
│          - Sensor Events: 42,150,000              │
│          - Data Output: 125 GB (134,217,728,000 B)│
│          - D&R Evaluations: 1,200,450             │
│          - Peak Sensors: 250                      │
│                                                    │
│        Sensor Inventory:                          │
│          - Total Sensors: 250                     │
│          - Online: 245 (98%)                      │
│          - Offline: 5 (2%)                        │
│          - Platforms: Windows (150), Linux (100)  │
│                                                    │
│        Detection Summary:                         │
│          Retrieved: 5,000 detections              │
│          ⚠️ LIMIT REACHED - actual count higher   │
│          Top Categories:                          │
│            - suspicious_process: 1,250            │
│            - network_threat: 890                  │
│            - malware: 450                         │
│                                                    │
│        Billing Status:                            │
│          - Plan: Enterprise                       │
│          - Status: Active ✓                       │
│          - Next Billing: Dec 1, 2025              │
│          - Invoice: [URL]                         │
│                                                    │
│     E. FAILED ORGANIZATIONS SECTION               │
│        ⚠️ FAILED ORGANIZATIONS (5 of 50)          │
│                                                    │
│        Client XYZ (oid: c7e8f940-...)             │
│          Status: ❌ Failed                        │
│          Error: 403 Forbidden                     │
│          Endpoint: get-billing-details            │
│          Reason: Insufficient billing permissions │
│          Impact: Billing data unavailable         │
│          Available: Usage stats, sensor inventory │
│          Action: Grant billing:read permission    │
│          Timestamp: 2025-11-20 14:32:15 UTC       │
│                                                    │
│     F. DETECTION LIMIT WARNINGS                   │
│        ⚠️ Organizations at Detection Limit:       │
│                                                    │
│        15 of 45 organizations exceeded the 5,000  │
│        detection limit. Actual counts are higher. │
│                                                    │
│        Organizations affected:                    │
│          - Client A: 5,000 retrieved ⚠️           │
│          - Client B: 5,000 retrieved ⚠️           │
│          - [... 13 more]                          │
│                                                    │
│        Recommendation: For complete detection     │
│        data, narrow time ranges or query specific │
│        date ranges for these organizations.       │
│                                                    │
│     G. METHODOLOGY SECTION                        │
│        Data Sources:                              │
│          - limacharlie org list: Organization discovery │
│          - limacharlie org stats: Daily metrics   │
│          - limacharlie billing details: Sub info  │
│          - limacharlie sensor list: Endpoint inv  │
│          - limacharlie sensor list --online: Live │
│          - limacharlie detection list: Security   │
│          - limacharlie dr list: Custom rules    │
│                                                    │
│        Query Parameters:                          │
│          - Detection limit: 5,000 per org         │
│          - Time range: Nov 1-30, 2025             │
│          - Date filtering: Applied to usage stats │
│                                                    │
│        Calculations:                              │
│          - Bytes to GB: value ÷ 1,073,741,824     │
│          - Aggregations: Sum across successful    │
│            organizations only                     │
│          - Timestamps: Normalized from mixed      │
│            seconds/milliseconds format            │
│                                                    │
│        Data Freshness:                            │
│          - Usage stats: Daily (24hr delay)        │
│          - Detections: Near real-time (5min)      │
│          - Sensor status: Real-time               │
│          - Billing: Updated on changes (~1hr)     │
│                                                    │
│     H. FOOTER                                     │
│        ═══════════════════════════════════════    │
│        Report completed: 2025-11-20 14:50:15 UTC  │
│        Execution time: 4 minutes 45 seconds       │
│                                                    │
│        For questions or issues:                   │
│        Contact: [e-mail]            │
│                                                    │
│        Disclaimer: Usage metrics shown are from   │
│        LimaCharlie APIs. For billing and pricing, │
│        refer to individual organization invoices. │
│        ═══════════════════════════════════════    │
└────────────────────────────────────────────────────┘

Progress Reporting During Execution:
  Display progress as orgs are processed:

  "Generating MSSP Report for 50 Organizations...

   [1/50] Client ABC... ✓ Success (2.3s)
   [2/50] Client XYZ... ✓ Success (1.8s)
   [3/50] Client PDQ... ⚠️ Billing permission denied
   [4/50] Client RST... ✓ Success (2.1s)
   [5/50] Client MNO... ❌ Failed: 500 Server Error
   ...
   [50/50] Client ZZZ... ✓ Success (1.9s)

   Collection Complete:
     ✓ Successful: 45 organizations
     ⚠️ Partial: 2 organizations (some data unavailable)
     ❌ Failed: 3 organizations

   Generating report structure..."

IMPORTANT: Console Output is Complete

The above console output displays ALL collected data. Do NOT automatically proceed to HTML generation.

Only generate HTML if user explicitly requests it (e.g., "export as HTML", "create dashboard"). When HTML is requested:

1. The console output above should already be displayed
2. Then spawn html-renderer to create the visual dashboard
3. This is an ADDITIONAL output, not a replacement

Pattern 2: Billing-Focused Summary Report

Template Reference: templates/billing-report.json

User Request Examples:

• "Billing summary for all customers this month"
• "Show me subscription status across organizations"
• "Usage report for invoicing"

Workflow:

1. Read billing-report.json template for input/output schemas

2. Validate inputs against template:
   - Required: year (integer), month (1-12)
   - Optional: scope (single/all), oid (UUID), format (json/markdown)

3. Get orgs: limacharlie org list --output yaml

4. Spawn org-reporter agents in parallel (same as Pattern 1)
   - Agents collect ALL data (billing focus is in report generation)

5. Aggregate results focusing on billing data:
   - Extract only: usage, billing, invoice_url from each agent result
   - Skip: detections, rules, detailed sensor data

6. Format output per template schema:
   - metadata: generated_at, period, scope, tenant_count
   - data.tenants: per-tenant billing with SKUs
   - data.rollup: aggregate totals (when scope=all)
   - warnings/errors arrays

7. Generate Billing-Focused Report:
   - Usage metrics per org (NO cost calculations)
   - Subscription status
   - Invoice links
   - Billing permission issues flagged

Pattern 3: Single Organization Deep Dive

Template Reference: templates/customer-health-report.json (with scope=single)

User Request Examples:

• "Detailed report for Client ABC"
• "Complete security analysis for organization XYZ"
• "Show me everything for [org name]"

Workflow:

1. Read customer-health-report.json template for structure

2. Get orgs: limacharlie org list --output yaml
   - Filter to find OID for the specified org name

3. Spawn ONE org-reporter agent for that organization:
   Task(
     subagent_type="lc-essentials:org-reporter",
     prompt="Collect reporting data for org 'Client ABC' (OID: uuid)
       Time Range: [start] to [end]
       Detection Limit: 5000"
   )

4. Generate Detailed Single-Org Report per template:
   - Full usage breakdown
   - Complete sensor inventory with platforms
   - Detection breakdown by category
   - All D&R rules listed
   - Output configurations
   - Any errors or warnings from collection
   - Attention items requiring follow-up

Pattern 4: Detection Analytics Report

Template Reference: templates/detection-analytics-report.json

User Request Examples:

• "Detection trends across all customers"
• "Which rules are firing most?"
• "Show detection categories breakdown for last 7 days"

Workflow:

1. Read detection-analytics-report.json template for schemas

2. Ask user for time range (7/14/30 days)

3. Get orgs: limacharlie org list --output yaml

4. Spawn org-reporter agents in parallel:
   - Each agent collects detection data for its org

5. Aggregate detection data:
   - Extract: detection_count, categories, severities, top_hosts
   - Build: top_categories, top_tenants rankings
   - Track: limit_reached flags

6. Format output per template:
   - metadata: time_window with calculated timestamps
   - data.tenants: per-tenant detection breakdown
   - data.rollup: aggregate totals
   - data.top_categories: cross-tenant category ranking
   - data.top_tenants: volume ranking with limit flags

7. Prominently display limit warnings for affected orgs

Validation Checkpoints

AI must validate at these critical points:

Before Spawning Agents

✓ Organization list retrieved successfully
✓ Each OID is valid UUID format (not org name)
✓ USER WAS ASKED to confirm time range (MANDATORY)
✓ Timestamps calculated DYNAMICALLY using $(date +%s)
✓ Timestamps are reasonable (start < end, not future)
✓ Limit values are positive integers
✓ Date ranges <= 90 days (warn if larger)
✓ User confirmed processing for large org counts (>20)
✓ Time range displayed to user before proceeding

After Agent Results

✓ Each agent returned valid JSON structure
✓ Check agent status: "success", "partial", or "failed"
✓ Required data fields present in successful results
✓ Errors array populated for any failures
✓ Warnings array checked for detection limits, etc.

Before Calculations

✓ Data types correct (numbers are numbers)
✓ Values within expected ranges
✓ No division by zero
✓ No negative values where impossible
✓ Timestamps pass sanity checks

Before Presenting Data

✓ All numbers formatted with thousand separators
✓ Units clearly labeled (GB, bytes, count, etc.)
✓ Timestamps in consistent format (UTC)
✓ Warnings included where required
✓ Metadata sections complete

Common Mistakes to Avoid

Based on previous implementation learnings:

❌ Detection Data Mistakes

WRONG: detection['rule_name']
RIGHT: detection.get('source_rule', detection.get('cat', 'unknown'))

WRONG: detection['severity']
RIGHT: Severity not in detection records - only in D&R rule config

❌ Timestamp Mistakes

WRONG: Using hardcoded epoch values from documentation examples
RIGHT: ALWAYS calculate dynamically: NOW=$(date +%s); START=$((NOW - 604800))

WRONG: Assuming a default time range without asking user
RIGHT: Use AskUserQuestion to confirm time range before querying

WRONG: Using timestamps from skill examples (e.g., 1730419200)
RIGHT: Calculate current time and subtract: $(($(date +%s) - 86400))

WRONG: Assuming all timestamps are seconds
RIGHT: Check magnitude, normalize if > 10000000000 (milliseconds)

WRONG: Ignoring invalid timestamps
RIGHT: Validate range (2020-01-01 to now+1day), flag invalid

WRONG: Not confirming calculated timestamps with user
RIGHT: Display "Time range: [date] to [date]" before running queries

❌ Sensor Status Mistakes

WRONG: last_seen field
RIGHT: alive field (datetime string format)

WRONG: Estimating offline duration
RIGHT: Parse alive timestamp, calculate exact hours/days

❌ Platform Mistakes

WRONG: Showing raw numeric codes without context
RIGHT: Pattern analysis + sample hostnames

Example:
  WRONG: "Platform: 2415919104 (30 sensors)"
  RIGHT: "Platform: LimaCharlie Extensions (code: 2415919104)
          Sample hostnames: ext-strelka-01, ext-hayabusa-02
          Sensor count: 30"

❌ Usage Stats Mistakes

WRONG: Using all 90 days from API without filtering
RIGHT: Filter to requested time range only

WRONG: Showing only GB
RIGHT: Show both - "450 GB (483,183,820,800 bytes)"

WRONG: Calculating costs
RIGHT: Show usage only, link to invoice for costs

❌ Multi-Org Aggregation Mistakes

WRONG: Including failed orgs in totals
RIGHT: Sum successful orgs only, document exclusions

WRONG: "Total detections: 75,000"
RIGHT: "Total retrieved: 75,000 (15 orgs hit 5K limit - actual higher)"

Error Handling Reference

Error Severity Levels

CRITICAL (Stop entire report):

• User authentication failure
• list-user-orgs fails (can't discover orgs)
• No organizations accessible

HIGH (Skip org, document prominently):

• 403 Forbidden on critical endpoints
• 500 Internal Server Error (after retry)
• Invalid OID format

MEDIUM (Use fallback, note in report):

• Sensor list unavailable
• Detection query timeout
• Empty result sets

LOW (Note in methodology):

• resource_link handling (normal for large results)
• Pagination required
• Data freshness >24hr

Error Response Template

{
  "org_oid": "c7e8f940-...",
  "org_name": "Client ABC",
  "endpoint": "get-billing-details",
  "error_type": "403 Forbidden",
  "error_message": "Insufficient permissions",
  "timestamp": "2025-11-20T14:45:30Z",
  "impact": "Billing details unavailable",
  "remediation": "Grant billing:read permission",
  "severity": "HIGH",
  "retry_attempted": false,
  "partial_data_available": true,
  "partial_data_sections": ["usage", "sensors", "detections"]
}

Report Quality Checklist

Before presenting any report, verify:

✓ Header Completeness

• Generation timestamp (UTC)
• Time window start/end (UTC)
• Duration in days
• Success/failure org counts

✓ Data Accuracy

• All timestamps normalized
• Units clearly labeled
• No cost calculations
• Detection limits flagged
• Aggregations documented

✓ Error Transparency

• Failed orgs listed with details
• Error codes and messages shown
• Impact statements clear
• Remediation actions provided

✓ Methodology Documentation

• API endpoints listed
• Query parameters documented
• Calculation formulas shown
• Data freshness stated
• Exclusions explained

✓ Warnings Present

• Detection limit warnings
• Partial data notices
• Permission issues flagged
• Data freshness alerts

Example Output Snippets

Executive Summary Example

═══════════════════════════════════════════════════════════
MSSP Comprehensive Report - November 2025

Generated: 2025-11-20 14:45:30 UTC
Time Window: 2025-11-01 00:00:00 UTC to 2025-11-30 23:59:59 UTC
Duration: 30 days
Organizations Processed: 45 of 50 (90% success rate)
═══════════════════════════════════════════════════════════

EXECUTIVE SUMMARY

Fleet Overview:
  • Total Sensors: 12,450 (across 45 successful organizations)
  • Online: 11,823 (95%)
  • Offline: 627 (5%)

Security Activity:
  • Detections Retrieved: 127,450
  • ⚠️ 15 organizations hit detection limit (actual counts higher)
  • Top Categories: suspicious_process (32%), network_threat (28%)

Usage Metrics (45 organizations):
  • Total Events: 1,250,432,100
  • Data Output: 3,847 GB
  • D&R Evaluations: 45,230,890

Issues Requiring Attention:
  ⚠️ 5 organizations failed (see Failures section)
  ⚠️ 15 organizations exceeded detection limits
  ⚠️ 627 sensors offline (5% of fleet)

Failed Organization Example

⚠️ FAILED ORGANIZATIONS (5 of 50)

─────────────────────────────────────────────────────────
Organization: Client XYZ Security Operations
OID: c7e8f940-aaaa-bbbb-cccc-ddddeeeeffffggg
Status: ❌ Partial Failure
─────────────────────────────────────────────────────────

Failed Endpoint: get-billing-details
  Error Code: 403 Forbidden
  Error Message: Insufficient permissions to access billing data
  Timestamp: 2025-11-20 14:32:15 UTC

Impact:
  ✗ Billing details unavailable
  ✗ Subscription status unknown
  ✗ Invoice link unavailable
  ✗ Next billing date unknown

Available Data:
  ✓ Organization metadata
  ✓ Usage statistics (Nov 1-30, 2025)
  ✓ Sensor inventory (125 sensors)
  ✓ Detection summary (1,847 detections)
  ✓ D&R rules configuration

Action Required:
  Grant the following permission to this organization:
  • Permission: billing:read
  • Scope: Organization level
  • Required Role: Admin or Owner

  Without this permission, billing data will remain unavailable
  in future reports for this organization.

Data Inclusion:
  ✓ Usage metrics INCLUDED in aggregate totals
  ✗ Billing status EXCLUDED from billing summaries
─────────────────────────────────────────────────────────

Detection Limit Warning Example

⚠️ DETECTION LIMIT WARNINGS

The following organizations exceeded the 5,000 detection retrieval
limit. Actual detection counts are higher than shown.

Organizations Affected (15 of 45):

  Client ABC Corp
    Retrieved: 5,000 detections ⚠️ LIMIT REACHED
    Time Range: Nov 1-30, 2025 (30 days)
    Actual Count: Unknown (exceeds 5,000)
    Recommendation: Query in 7-day increments for complete data

  Client XYZ Industries
    Retrieved: 5,000 detections ⚠️ LIMIT REACHED
    Time Range: Nov 1-30, 2025 (30 days)
    Actual Count: Unknown (exceeds 5,000)
    Recommendation: Query in 7-day increments for complete data

  [... 13 more organizations]

Impact on Report:
  • Detection counts shown are MINIMUM values
  • Actual totals across all organizations are higher
  • Category distributions may be skewed (sample bias)
  • Aggregate detection count is underreported

Recommendations:
  1. For complete data, narrow time ranges:
     - Instead of 30 days, query 7-day periods
     - Aggregate results manually

  2. Filter by category for targeted analysis:
     - Query specific categories separately
     - Combine results for complete picture

  3. Consider increasing limit (max: 50,000):
     - Higher limits increase API response time
     - May hit other infrastructure limits

Support and Troubleshooting

Common Issues

Issue: "Too many organizations to process"

• Solution: Filter to subset, or process in batches
• Workaround: Generate multiple reports for org groups

Issue: "Detection limit hit for all orgs"

• Solution: Narrow time range (30 days → 7 days)
• Workaround: Query by week, aggregate manually

Issue: "Billing permission errors"

• Solution: Grant billing:read to organizations
• Workaround: Generate usage-only reports

Issue: "Large sensor list timing out"

• Solution: Use --filter to extract summary fields, or pipe CLI output to file
• Workaround: Extract summary only (count, platforms)

Getting Help

For issues with this skill:

1. Verify authentication and permissions
2. Check API endpoint availability
3. Validate time ranges and parameters
4. Review error messages for specific guidance
5. Contact LimaCharlie support: [e-mail]

Skill Maintenance

This skill should be updated when:

• New LimaCharlie API endpoints become available
• Data structure changes in API responses
• New reporting requirements emerge
• Additional guardrails needed based on issues

Last Updated: 2025-11-20 Version: 1.0.0