openai-docs-skill
Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI (curl/jq). Use whenever a task involves the OpenAI API (Responses, Chat Completions, Realtime, etc.), OpenAI SDKs, ChatGPT Apps SDK, Codex, MCP integrations, endpoint schemas, parameters, limits, or migrations and you need up-to-date official guidance.
Install with Tessl CLI
npx tessl i github:am-will/codex-skills --skill openai-docs-skill95
Does it follow best practices?
Validation for skill structure
Discovery
100%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 well-crafted skill description that excels across all dimensions. It provides specific capabilities, comprehensive trigger terms that developers would naturally use, explicit 'Use whenever' guidance, and a clearly defined niche that distinguishes it from other documentation or API-related skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple concrete actions and technologies: 'Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI (curl/jq)' with specific domains like 'Responses, Chat Completions, Realtime, SDKs, Codex, MCP integrations, endpoint schemas, parameters, limits, migrations'. | 3 / 3 |
Completeness | Clearly answers both what ('Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI') and when ('Use whenever a task involves the OpenAI API... and you need up-to-date official guidance'). The 'Use whenever' clause provides explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'OpenAI API', 'Chat Completions', 'Realtime', 'OpenAI SDKs', 'ChatGPT Apps SDK', 'Codex', 'MCP integrations', 'endpoint schemas', 'parameters', 'limits', 'migrations'. These are terms developers naturally use when seeking OpenAI documentation. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche: specifically targets OpenAI documentation queries via a specific MCP server. The combination of 'OpenAI Docs MCP server', specific API names, and 'official guidance' creates a unique trigger profile unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill that efficiently teaches Claude how to query OpenAI docs via MCP. It excels at conciseness and actionability with executable examples and clear command references. The main weakness is the workflow section lacks error handling guidance for common failure scenarios like empty search results or network failures.
Suggestions
Add brief error handling guidance to the workflow (e.g., 'If search returns no results, try broader terms or use list to browse')
Consider adding a validation step after fetch to confirm the content is relevant before applying it
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, assuming Claude's competence. No unnecessary explanations of what MCP is or how curl/jq work. Every section serves a clear purpose. | 3 / 3 |
Actionability | Provides concrete, copy-paste ready bash commands with clear examples. The script reference documents all subcommands with their parameters, making it immediately executable. | 3 / 3 |
Workflow Clarity | The 3-step workflow (Discover → Read → Apply) is clear but lacks validation checkpoints. No guidance on what to do if search returns no results or fetch fails, which are common failure modes. | 2 / 3 |
Progressive Disclosure | Well-structured with quick start for immediate use, followed by workflow explanation, then detailed script reference. Appropriate for a skill under 50 lines with no need for external file references. | 3 / 3 |
Total | 11 / 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.
- Reviewed
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