Sensor Health Reporting Skill

This skill orchestrates parallel sensor health checks across multiple LimaCharlie organizations for fast, comprehensive fleet reporting.

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

When to Use

Use this skill when the user asks about:

• Connectivity Issues: "Show me sensors online but not sending data"
• Offline Sensors: "List sensors that haven't been online for 7 days"
• Data Availability: "Which sensors have no events in the last hour?"
• Fleet Health: "Find all offline sensors across my organizations"
• Cross-Org Reports: "Show me sensor health across all my orgs"

What This Skill Does

This skill orchestrates sensor health reporting by:

1. Getting the list of user's organizations
2. Spawning parallel lc-essentials:sensor-health-reporter agents (one per org)
3. Aggregating results from all agents
4. Presenting a unified report

Key Advantage: By running one agent per organization in parallel, this skill can check dozens of organizations simultaneously, dramatically reducing execution time.

How to Use

Step 1: Parse User Query

Identify the key parameters:

• Time window: Last hour, 7 days, 30 days, etc.
• Status filter: Online, offline, all sensors
• Data availability: Has events, no events, sparse events
• Scope: All orgs (default) or specific orgs

Step 2: Get Organizations

Use the LimaCharlie CLI to get the user's organizations:

limacharlie org list --output yaml

Step 3: Spawn Parallel Agents

For each organization, spawn a lc-essentials:sensor-health-reporter agent in parallel:

Task(
  subagent_type="lc-essentials:sensor-health-reporter",
  prompt="Check sensors in organization '{org_name}' (OID: {oid}) that are online but have not sent telemetry in the last {timeframe}."
)

CRITICAL: Spawn ALL agents in a SINGLE message with multiple Task tool calls to run them in parallel:

<message with multiple Task blocks>
  Task 1: Check org 1
  Task 2: Check org 2
  Task 3: Check org 3
  ...
</message>

Do NOT spawn them sequentially - that defeats the purpose of parallelization.

Step 4: Aggregate Results

Once all agents return:

1. Parse each agent's findings
2. Count total problematic sensors across all orgs
3. Group by organization
4. Identify patterns or anomalies

Step 5: Generate Report

Present a unified report with:

• Executive Summary: Total sensors with issues across all orgs
• Per-Org Breakdown: Findings from each organization
• Context: What the findings mean
• Recommendations: Optional suggestions

Example Workflow

User Query: "Show me sensors online but not reporting events in the last hour"

Step 1: Get current timestamp and calculate 1 hour ago

current=$(date +%s)
one_hour_ago=$(date -d '1 hour ago' +%s)

Step 2: Get org list

limacharlie org list --output yaml

Step 3: Spawn parallel agents (example with 3 orgs)

# Single message with 3 Task calls
Task(subagent_type="lc-essentials:sensor-health-reporter", prompt="Check org1...")
Task(subagent_type="lc-essentials:sensor-health-reporter", prompt="Check org2...")
Task(subagent_type="lc-essentials:sensor-health-reporter", prompt="Check org3...")

Step 4: Aggregate

Org1: 5 sensors with no data
Org2: 12 sensors with no data
Org3: 0 sensors with issues
Total: 17 sensors

Step 5: Present report

## Sensors Online But Without Events (Last Hour)

**Total: 17 sensors across 2 organizations**

### org1 (5 sensors)
- sensor-id-1
- sensor-id-2
...

### org2 (12 sensors)
- sensor-id-6
...

### Analysis
These sensors are connected but not generating events...

Time Window Calculations

Use bash to calculate timestamps:

# Current time
date +%s

# X hours ago
date -d 'X hours ago' +%s

# X days ago
date -d 'X days ago' +%s

# X weeks ago
date -d 'X weeks ago' +%s

Performance Tips

1. Always spawn agents in parallel - Use a single message with multiple Task calls
2. Limit scope if needed - For quick checks, allow user to specify specific orgs
3. Sub-agents define their own model - No need to specify model in Task calls
4. Handle errors gracefully - If one org fails, continue with others
5. Cache org list - If doing multiple related queries, reuse the org list

Error Handling

If an agent fails:

• Log the error for that organization
• Continue processing other organizations
• Include error summary in final report
• Don't let one org failure block the entire report

Report Format Template

## {Query Title}

**Summary**: {Total count} sensors across {N} organizations

### {Org Name 1} ({count} sensors)
- {sensor-id-1}
- {sensor-id-2}
...

### {Org Name 2} ({count} sensors)
- {sensor-id-x}
...

### Organizations with No Issues
- {Org Name 3}
- {Org Name 4}

### Analysis
{Context about findings}

### Recommendations
{Optional suggestions}

Important Constraints

• Parallel Execution: ALWAYS spawn agents in parallel (single message, multiple Tasks)
• OID Format: Organization ID is a UUID, not the org name
• Time Limits: Data availability checks must be <30 days
• Model: Sub-agents define their own model in frontmatter
• Error Tolerance: Continue with partial results if some orgs fail

Related Skills

• sensor-tasking - For sending commands to sensors (live response, data collection)
• sensor-coverage - For comprehensive asset inventory and coverage gap analysis

Related CLI Commands

• limacharlie org list - Get organizations
• limacharlie sensor list --online --oid <oid> - Get online sensor list (used by agent)
• limacharlie event retention --sid <sid> --oid <oid> - Check data timeline (used by agent)
• limacharlie sensor list --oid <oid> - Get all sensors (used by agent for offline checks)