ark-controller-development
Guidance for developing the Ark Kubernetes operator. Use when modifying Go types, CRDs, controllers, or webhooks. Helps with CRD generation and Helm chart sync issues.
84
83%
Does it follow best practices?
Impact
80%
2.96xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a solid skill description that clearly identifies its niche (Ark Kubernetes operator development) and provides explicit trigger conditions. The trigger terms are well-chosen for the target audience. The main weakness is that the capability descriptions could be more concrete—listing specific actions rather than general categories of work.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Ark Kubernetes operator) and mentions some actions like modifying Go types, CRDs, controllers, webhooks, CRD generation, and Helm chart sync, but these are more categories of work than concrete specific actions (e.g., doesn't say 'generate CRD manifests from Go structs' or 'sync Helm values.yaml with CRD changes'). | 2 / 3 |
Completeness | Clearly answers both 'what' (guidance for developing the Ark Kubernetes operator, helps with CRD generation and Helm chart sync issues) and 'when' (explicitly states 'Use when modifying Go types, CRDs, controllers, or webhooks'). | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms that a developer would use: 'Go types', 'CRDs', 'controllers', 'webhooks', 'CRD generation', 'Helm chart sync', 'Kubernetes operator', and 'Ark'. These cover the key terms a user working in this domain would naturally mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific project name 'Ark Kubernetes operator' and the combination of Go types, CRDs, controllers, webhooks, and Helm chart sync. This is unlikely to conflict with other skills unless there are multiple Kubernetes operator skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, actionable skill for Ark operator development with clear workflows, concrete commands, and useful worked examples. Its main weakness is verbosity — several sections explain general software engineering principles (code reuse, avoiding magic numbers, testing importance) at a level of detail unnecessary for Claude, and the content could benefit from splitting detailed guidance into referenced files. The completion checklist and CRD generation flow are particularly well done.
Suggestions
Trim the 'Leverage Existing Code' and 'Avoid Magic Numbers' sections to just the project-specific rules and examples, removing general rationale Claude already knows (e.g., 'Hand-rolled code drifts, duplicates tests, and becomes maintenance burden').
Consider extracting the testing framework table, coverage requirements, and worked examples into a separate TESTING.md reference file to improve progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and project-specific, but some sections are verbose for an audience of Claude — e.g., the 'Leverage Existing Code' section with its philosophical rationale ('Hand-rolled code drifts, duplicates tests...') and the lengthy worked example explaining why not to reimplement a WWW-Authenticate parser. The 'Avoid Magic Numbers' section also explains a well-known principle at length. These could be tightened significantly. | 2 / 3 |
Actionability | The skill provides concrete, executable bash commands for every workflow (make manifests, make build, make lint, chainsaw test), specific directory paths, table-driven guidance for test placement, and worked code examples showing good vs. bad patterns. Guidance is copy-paste ready and specific to the project. | 3 / 3 |
Workflow Clarity | Multi-step processes are clearly sequenced (CRD generation flow, after modifying types, before opening a PR) with explicit validation checkpoints (make lint before push is 'non-negotiable', validate → fix → rebuild). The completion checklist provides a comprehensive verification loop. The CRD generation flow diagram clearly shows the pipeline with validation steps. | 3 / 3 |
Progressive Disclosure | The skill is well-structured with clear sections and a table of key directories, but it's a fairly long monolithic file (~150 lines of substantive content) with no references to supporting files. The worked examples, testing framework table, and magic numbers guidance could be split into separate reference files. The only external reference is to a 'chainsaw skill' which is appropriate. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- mckinsey/agents-at-scale-ark
- Commit
6b7c761
- Reviewed
Table of Contents
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