promotion-eval-create

name	promotion-eval-create
description	Create a promotion evaluation template for any system by gathering requirements through structured questions and generating a reusable evaluation skill. Use when users ask to create a promotion check, release readiness evaluation, environment health template, or want to build a custom evaluation workflow for systems beyond Mission Control.
disable-model-invocation	true

Create Promotion Evaluation Template

Core Purpose

Guide the user through a structured interview to build a custom promotion evaluation skill for their system. The output is a complete SKILL.md file they can use to evaluate environment health and release readiness.

Use @skills/promotion-eval-mission-control/SKILL.md as the reference implementation — it demonstrates the structure, phased evaluation approach, verdict logic, and report format that this skill generates for other systems.

Workflow

Step 1: Identify the System

Ask the user:

1. What system or platform are you evaluating? (e.g., Kubernetes cluster, AWS environment, SaaS application, database cluster, CI/CD pipeline)
2. What is the evaluation for? (e.g., release promotion, environment readiness, disaster recovery validation, compliance check)
3. What environment(s) will be evaluated? (e.g., staging, production, specific cluster names)

Step 2: Define Components

Ask the user to list the health components they want to evaluate. Suggest categories based on their system type:

For Kubernetes-based systems:

• Deployments/StatefulSets health
• Pod status and restarts
• Node health and capacity
• Ingress/networking
• PersistentVolume status
• CronJob success rates

For cloud infrastructure (AWS/GCP/Azure):

• Compute instance health
• Database connectivity and replication
• Load balancer targets
• Certificate expiry
• Storage utilization
• Network connectivity

For SaaS/application systems:

• API endpoint health
• Database query performance
• Queue depth and processing rates
• Error rates and latency
• Authentication/SSO status
• Third-party dependency health

For CI/CD pipelines:

• Build success rates
• Test pass rates
• Deployment success rates
• Artifact availability
• Environment provisioning

For each component, ask:

• What tool or API provides the health data? (MCP tool, HTTP endpoint, CLI command, database query)
• What metrics matter? (counts, rates, durations, thresholds)
• What are the PASS/WARN/FAIL thresholds?

Step 3: Define Parameters

Ask about configurable parameters:

• time_window: What lookback period? (default: 24h)
• target: How is the environment identified?
• Any custom parameters specific to their system?

Step 4: Define Verdict Logic

Confirm the overall verdict mapping:

• READY: Which components must PASS?
• CAUTION: Which components can WARN without blocking?
• NOT_READY: Which component failures are blocking?
• Are any components optional (can SKIP without affecting verdict)?

Step 5: Generate the Skill

Using the gathered information, generate a complete SKILL.md following this structure:

---
name: promotion-eval-<system-name>
description: >
  Evaluates <system> health for <purpose>.
  Checks <component list summary>.
  Use for <trigger scenarios>.
allowed-tools: <list of MCP tools or other tools needed>
---

# <System> Promotion Evaluation Skill

## Core Purpose
<One paragraph describing what this evaluation does>

## Parameters
- **time_window**: Lookback period (default: <default>)
- **target**: <how environment is identified>
<any custom parameters>

## Evaluation Procedure
Execute these phases sequentially. After each phase, record component status and findings.

Initialize a running JSON result conforming to the schema:
<JSON template with verdict, components, findings, recommendations>

---

### Phase N: <Component Name>
**Goal**: <what this phase checks>

1. <Step-by-step tool calls or queries>

**Metrics to record**:
- <metric>: <description>

**Verdict logic**:
- PASS: <criteria>
- WARN: <criteria>
- FAIL: <criteria>

---
<repeat for each component>

## Report Generation
<Markdown report template>
<JSON output template>

## Overall Verdict Logic
<Component-to-verdict mapping>

## Error Handling
- If a tool call fails, record the component as SKIP with a note
- Do not let one phase failure block subsequent phases
- Reuse data across phases when possible

Step 6: Review and Refine

Present the generated skill to the user and ask:

• Does the component list look complete?
• Are the thresholds appropriate?
• Should any phases be added or removed?
• Are the tool references correct?

Iterate until the user is satisfied.

Output

Write the final SKILL.md to the user's chosen location (default: current project's .claude/skills/ directory).

If the evaluation uses MCP tools, also note which MCP servers need to be configured.