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AB postman-openapi-converter

Convert OpenAPI 3.x or Swagger 2.0 specs (YAML or JSON) into complete, import-ready Postman Collection v2.1 JSON files. Use this skill whenever the user provides or references an OpenAPI spec, Swagger file, openapi.yaml, swagger.json, or uses phrases like "convert my OpenAPI spec", "import swagger to Postman", "turn this spec into a collection", or "generate Postman requests from my API spec". Also triggers when the user pastes YAML or JSON that begins with `openapi:`, `swagger:`, or contains `paths:` with HTTP method keys. Always prefer this skill over the general collection generator when the input is a structured spec file.

LambdaTest/agent-skills Agent Skills author: LambdaTest 1 file body ≈ 1 217 tokens Open the sourcegithub.com↗ analyzed 33 h ago

Convert OpenAPI 3.x or Swagger 2.0 specs (YAML or JSON) into complete, import-ready Postman Collection v2.1 JSON files. Use this skill whenever the user…

As a process B 69/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting

GeneratorWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "languages"

    Process rating: all ten parameters 69/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 32 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1217 tokens
    • 100Running it twice. Mutating operations check current state
    • low 10 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 634: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.