CloudThinker Language

CloudThinker Language is the structured prompt syntax that works identically across every agent, in chat, Slack, and automations. Mention an agent, tag the output you want, then write your instruction in plain language.

Syntax

Every prompt follows one canonical form:

@agent #tool instruction

Component	Required	Description
`@agent`	Yes	Routes the request to a specific agent. Mention several agents for cross-domain work.
`#tool`	No	Output format or capability. Place it immediately after the mention; combine tags for multiple outputs.
`instruction`	Yes	What you want, in your own words — include scope, timeframe, thresholds, and constraints inline.

Agent mentions

Mention	Role	Best for
`@alex`	Cloud Engineer	Cost analysis, resource optimization, multi-cloud infrastructure
`@oliver`	Security Engineer	Audits, vulnerability scans, IAM, compliance
`@tony`	Database Engineer	Query optimization, performance tuning, backups
`@kai`	Kubernetes Engineer	Cluster management, pod optimization, scaling
`@anna`	General Manager	Multi-agent coordination, executive reporting

See the agent reference for each agent’s full capabilities.

Tool tags

Tag	Output	Example
`#dashboard`	Interactive visualizations for monitoring and presentations	`@alex #dashboard monthly cost trends with forecast`
`#report`	Detailed analysis documents with findings and evidence	`@oliver #report SOC 2 compliance assessment`
`#recommend`	Prioritized, actionable recommendations with implementation steps	`@tony #recommend index changes for slow queries`
`#alert`	Monitoring notifications on the conditions you define	`@kai #alert on pod OOMKilled events`
`#chart`	Standalone data visualizations	`@alex #chart spending by service`
`#kb`	Knowledge base search across your organization’s docs	`@alex #kb cloud governance policies`

Combine tags when you want more than one output: @alex #dashboard #recommend cost optimization opportunities.

Context modifiers

Add context inside the instruction to narrow scope and improve accuracy.

Type	Examples
Timeframe	`over the last 30 days`, `since January`, `in the last 24 hours`
Scope	`in us-east-1`, `on the production cluster`, `on the orders database`
Threshold	`with CPU below 15%`, `costing over $1,000/month`, `P95 above 500ms`
Exclusion	`excluding production-tagged resources`, `except backup buckets`
Priority	`prioritizing quick wins`, `critical severity first`

Prompt patterns

Pattern	Form	Example
Analyze	`@agent #tool analyze <target> <context>`	`@alex analyze EC2 spending by instance family over the last quarter`
Audit	`@agent #tool audit <target> for <criteria>`	`@oliver audit S3 buckets for public access and encryption`
Optimize	`@agent #recommend <target> <constraints>`	`@kai #recommend HPA policies for variable workloads`
Investigate	`@agent investigate <issue> <scope>`	`@tony investigate slow query performance on the orders table`
Coordinate	`@anna coordinate <project> with <agents>`	`@anna coordinate quarterly review with @alex @oliver @tony @kai`

In Slack, the same syntax works with a bot prefix: @CloudThinker alex what's our AWS spend this week? — see Slack integration.

Examples

Direct requests — pick the agent, tag the output, state the target:

@alex #dashboard AWS spending by service and region
@oliver #report SOC 2 compliance assessment with evidence
@tony #recommend index changes for queries slower than 500ms

Stack context modifiers to make results precise and safe to act on:

@alex #recommend right-sizing for EC2 instances in us-east-1 with CPU below 15% over the last 30 days, excluding production-tagged resources

Mention multiple agents for cross-domain problems:

@alex @tony investigate the cost spike on database infrastructure last week
@anna coordinate security remediation with @oliver leading and @alex @kai supporting

Refine progressively — start broad, then drill into what the agent finds:

@alex #dashboard cloud spending trends
@alex #chart November cost spike broken down by service
@alex #recommend RDS optimization prioritizing underutilized instances

Be specific. @alex #recommend right-sizing for EC2 in us-east-1 with CPU below 15% gets an actionable answer; @alex check our servers gets a clarifying question.

Agents

Each agent’s expertise, capabilities, and prompt patterns in depth

Capabilities

What #dashboard, #report, #recommend, and other tool tags produce

Operations Hub

Pre-built operations ready to run with this syntax

Slack Integration

Use the same syntax in Slack with the @CloudThinker prefix

Start here

Core concepts

Syntax

Agent mentions

Tool tags

Context modifiers

Prompt patterns

Examples

Agents

Capabilities

Operations Hub

Slack Integration

​Syntax

​Agent mentions

​Tool tags

​Context modifiers

​Prompt patterns

​Examples

​Related

Agents

Capabilities

Operations Hub

Slack Integration

Syntax

Agent mentions

Tool tags

Context modifiers

Prompt patterns

Examples

Related