klingai-hello-world
Create your first Kling AI video generation with a minimal working example. Use when learning Kling AI or testing your setup. Trigger with phrases like 'kling ai hello world', 'first kling video', 'klingai quickstart', 'test klingai'.
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/klingai-pack/skills/klingai-hello-world/SKILL.mdQuality
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 description that clearly communicates its purpose as a quickstart/hello-world skill for Kling AI video generation. It excels at completeness and trigger term quality with explicit 'Use when' and 'Trigger with' clauses. The main weakness is that the specificity of capabilities could be improved by listing more concrete actions beyond 'create a minimal working example'.
Suggestions
Add more specific concrete actions to improve specificity, e.g., 'Generates a sample video using the Kling AI API, walks through authentication setup, and verifies output.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI video generation) and a general action (create a minimal working example), but doesn't list multiple specific concrete actions like what steps are involved or what outputs are produced. | 2 / 3 |
Completeness | Clearly answers both 'what' (create your first Kling AI video generation with a minimal working example) and 'when' (use when learning Kling AI or testing your setup), with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes natural trigger phrases users would actually say: 'kling ai hello world', 'first kling video', 'klingai quickstart', 'test klingai'. These cover common variations of how someone would invoke this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche — a hello-world/quickstart for Kling AI video generation. The trigger terms are highly distinctive ('kling ai hello world', 'klingai quickstart') and unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid quickstart skill with fully executable code examples and good reference tables for troubleshooting and API responses. Its main weaknesses are including two full language examples inline (reducing conciseness) and lacking explicit error handling/validation checkpoints around the task creation step and polling timeout. The content is well-structured but could benefit from splitting the Node.js example to a separate file.
Suggestions
Add error checking after the task creation POST request (e.g., verify response code is 0 and data contains task_id) and add a polling timeout to prevent infinite loops — this would improve workflow clarity.
Move the Node.js example to a separate file (e.g., NODEJS_EXAMPLE.md) and link to it, keeping SKILL.md focused on one primary example for better conciseness and progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill provides two full language examples (Python and Node.js) which adds bulk. The overview sentence 'This skill walks through the complete create-poll-download cycle' is slightly unnecessary. The response shape section and status table are useful but the Node.js example could be referenced separately rather than inline, making this moderately efficient but not lean. | 2 / 3 |
Actionability | Both Python and Node.js examples are fully executable, copy-paste ready code with concrete API endpoints, model names, auth setup, and polling logic. The response shape, status values table, and troubleshooting table provide specific, actionable details. | 3 / 3 |
Workflow Clarity | The three-step create-poll-download workflow is clearly embedded in the code with comments (Step 1, Step 2), and the polling loop handles both success and failure states. However, there are no explicit validation checkpoints — no verification of the initial task creation response (checking for errors/non-200), no timeout on the polling loop, and no guidance on what to do if the request itself fails before getting a task_id. | 2 / 3 |
Progressive Disclosure | The skill references external resources and a prerequisite skill (klingai-install-auth), which is good. However, having two full language examples inline makes the file longer than necessary — the Node.js example could be in a separate file. The structure with tables and sections is decent but the content is somewhat monolithic for its length. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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