Explicit structural requirements reduce ambiguity. Providing exact column names, ordering, and formatting expectations helps Claude consistently generate Markdown tables that downstream users or systems can reliably consume.

A project-scoped skill is available to everyone working in that repository and stays synchronized through version control. The skill directory name exposes it as /migrate-component.

The dependency must be enforced by orchestration rather than left to probabilistic tool selection.
Anthropic documents that tool_choice: {"type": "tool", "name": "..."} forces Claude to invoke the specified tool. By contrast, auto allows Claude to decide whether and which tool to call, while any requires some tool but does not force a particular one.
Option A therefore establishes a deterministic two-stage workflow. The first API turn forces extract_metadata, producing the DOI and other structured paper details. The application validates and stores that result. A subsequent turn then exposes or permits verify_doi and lookup_citations, passing the extracted DOI as explicit state. This design converts an implicit tool dependency into an application-controlled execution graph.

Option D addresses the observed inconsistency at the model-behavior level. A schema can require that materials be a string, but it cannot teach Claude which lexical form the application considers canonical or demonstrate when a source phrase should populate the field. Anthropic identifies examples as one of the most reliable ways to steer output format, structure, and consistency, recommending several relevant and diverse examples that mirror the real task in its prompting best practices. Complete input-output pairs show both recognition and normalization:
for example, a description containing "60% cotton, 40% polyester" can consistently map to the chosen "cotton/polyester blend" representation. They can also include difficult cases where material information is embedded indirectly in prose. Temperature zero reduces sampling variability but does not repair an underspecified transformation rule. A more capable model likewise lacks the missing formatting convention. Making the field required is dangerous because documents may legitimately omit materials; it can force unsupported values and increase hallucinations. Few-shot examples therefore supply the missing decision boundary while preserving truthful absence handling. The examples should be evaluated on held-out descriptions and supplemented by deterministic post-processing if downstream systems require an exact controlled vocabulary.

The failure is caused by an underspecified extraction policy rather than an inadequate JSON structure. "Extract all skills mentioned" does not define whether coordinated phrases should be split, whether inferred capabilities qualify, or what level of specificity constitutes one skill. Few- shot examples can demonstrate all three decisions using concrete inputs and expected outputs.
Anthropic identifies examples as one of the most effective ways to improve output accuracy and consistency because they communicate behavior more precisely than abstract instructions alone.
Representative examples can show that "Python and SQL" becomes two entries, that an unstated skill must not be inferred from a job title, and that broad descriptions should not be decomposed into dozens of speculative micro-skills.