Boosting Large Language Model Domain Expertise with Claude Skills

Background

Applying general‑purpose large language models (LLMs) to vertical industry scenarios faces a core challenge: the models generate probabilistic text but lack the hard logical constraints required by domains such as finance compliance or legal reasoning. Traditional approaches—domain fine‑tuning, retrieval‑augmented generation (RAG), and prompt engineering—inject more information but still rely on the model to infer and obey constraints, leading to "information ≠ logic" and "soft suggestions ≠ hard enforcement".

Claude Skills Overview

Claude Skills proposes a different path by representing domain knowledge as execution constraints rather than raw text. The framework provides three design patterns that developers can embed in Skills scripts:

Progressive Disclosure : Dynamically activates only the knowledge relevant to the current context, preventing knowledge mixing.

Pydantic Validation : Defines output schemas and runtime checks to guarantee that generated data meets domain‑specific formats and business rules.

State Machine : Encodes the required workflow as a finite‑state transition graph, forcing the model to follow the correct sequence of steps.

These mechanisms are not built‑in features of the Skills runtime; they are patterns that developers implement within the Skills folder structure (SKILL.md, optional scripts, resources).

Three‑Layer Knowledge Model

Domain expertise is decomposed into three layers:

Fact Layer – static knowledge such as regulations or statutes.

Process Layer – ordered execution steps (e.g., KYC → risk assessment → decision).

Judgment Layer – decision logic that combines facts and process outcomes.

Encoding each layer with the three mechanisms yields a system where the LLM only sees the information it needs, validates its output against strict schemas, and cannot skip mandatory steps.

Implementation Examples

Financial Compliance Scenario

In a compliance workflow, high‑risk customers must undergo enhanced due‑diligence (EDD). The Skills script uses progressive disclosure to load basic_kyc.md for low‑risk cases and enhanced_due_diligence.md for high‑risk cases:

# compliance_skill/risk_based_kyc.py
class ComplianceSkill:
    def get_instructions(self, context):
        if context.risk_level == "LOW":
            return self.load_instructions("basic_kyc.md")
        elif context.risk_level == "HIGH":
            return self.load_instructions("enhanced_kyc.md")

Pydantic schemas enforce that a high‑risk transaction cannot be approved directly:

class TransactionReview(BaseModel):
    customer_id: str = Field(..., min_length=8)
    risk_score: float = Field(..., ge=0, le=100)
    approval_status: Literal["approved", "rejected", "escalated"]
    @validator('approval_status')
    def check_high_risk(cls, status, values):
        if values.get('risk_score') > 80 and status == "approved":
            raise ValueError("High‑risk transactions must be escalated")
        return status

The state‑machine definition guarantees the order KYC → risk assessment → review → decision, raising a ValueError if a step is skipped.

Legal Reasoning Scenario

Legal analysis requires a strict three‑premise structure and citation of specific statutes. Skills encode this with a state machine that forces the stages Fact → Legal Relationship → Rule Selection → Element Verification → Conclusion. Pydantic validators ensure every required element is present and that the conclusion matches the verification results:

class LegalOpinion(BaseModel):
    case_type: str
    applicable_rules: List[LegalProvision]
    constitutive_elements: List[ConstitutiveElement]
    conclusion: Literal["构成", "不构成", "证据不足"]
    @validator('applicable_rules')
    def validate_citation(cls, rules):
        for r in rules:
            if not r.article:
                raise ValueError("Citations must include article numbers")
        return rules
    @validator('constitutive_elements')
    def validate_elements(cls, elems, values):
        required = cls.get_required_elements(values.get('case_type'))
        missing = required - {e.element_name for e in elems}
        if missing:
            raise ValueError(f"Missing elements: {missing}")
        return elems
    @validator('conclusion')
    def validate_conclusion(cls, concl, values):
        all_sat = all(e.is_satisfied for e in values.get('constitutive_elements', []))
        if all_sat and concl != "构成":
            raise ValueError("All elements satisfied → conclusion must be '构成'")
        if not all_sat and concl == "构成":
            raise ValueError("Unsatisfied elements → cannot conclude '构成'")
        return concl

When the model attempts to output a conclusion without the required citations, the Skills layer intercepts the response, returns a ValidationError, and forces Claude to regenerate a compliant answer.

Evaluation Framework

The article proposes a five‑dimensional suitability matrix (constraint complexity, dynamic reasoning, customization depth, auditability, maintenance cost). Scenarios scoring three or more "fit" cells are recommended for Skills; those with two or more "mismatch" cells should avoid Skills.

Decision trees compare Skills, MCP (Model‑Connect‑Protocol) and plain prompting, concluding that Skills excel when tasks have clear, repeatable constraints, while MCP is better for external connectivity and prompting remains suitable for low‑constraint, creative tasks.

Practical Guidance & Limitations

Skills are currently in an Early‑Preview stage; APIs may change. Designers must balance granularity—over‑fine‑grained Skills increase maintenance overhead, while overly coarse Skills lose modular benefits. Token cost rises because constraint definitions are injected as additional context.

Overall, the shift from "train the model to understand rules" to "design executable constraints for the model" offers a more reliable path to domain‑specific LLM performance, especially in regulated fields where audit trails and strict process adherence are mandatory.

References

[1] Introducing Agent Skills | Claude – https://www.anthropic.com/news/introducing-agent-skills

[2] Agent Skills – Claude Code Docs – https://docs.anthropic.com/en/docs/build-with-claude/agent-skills

[3] GitHub – anthropics/skills – https://github.com/anthropics/skills

[4] New capabilities for building agents on the Anthropic API – https://www.anthropic.com/news/agent-api-capabilities

[5] Don’t Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag – https://www.youtube.com/watch?v=RSB9vJmGhko