How Claude Interprets the Descriptions in Skill Files

Claude interprets skill file descriptions by extracting the description field from JSON manifests and injecting that plain English text directly into the model's system prompt, allowing the LLM to understand the skill's purpose without additional parsing layers.

The anthropics/claude-plugins-community repository implements a plugin architecture where skills are defined through metadata rather than hardcoded logic. When Claude loads a skill, it relies on the description field in the skill's JSON manifest to establish domain-specific behavior and contextual expectations.

The Skill Manifest and Description Field

Skills in the Claude ecosystem are bundles of metadata stored in JSON files. The manifest contains fields like skill_name, examples, parameters, and the critical description field that serves as the behavioral contract between the developer and the model.

According to the source code, the description serves two distinct purposes:

1. Contextual Guidance – The description becomes part of the system message that conditions Claude's reasoning, telling the model the skill's specific purpose (e.g., "analyze a ledger reconciliation report") and any domain-specific expectations.

2. Prompt Generation – When a user invokes a skill (e.g., /tres-report-analyzer), Claude formats a request combining the description with the user's query and referenced files to generate aligned responses.

How the Plugin Loader Processes Descriptions

The core plugin loader located in the repository's infrastructure handles the extraction and injection of skill descriptions. When a session initializes a skill, the loader parses the manifest file—such as tres-finance-plugin/.claude-plugin/plugin.json—and extracts the description field.

The loader merges this description with the skill_name and other metadata to construct a specialized system context. The resulting system prompt follows this exact structure:

You are a specialized assistant for <skill_name>.
<description>
User query: <user prompt>
Relevant files: <file list>

Runtime Loading Logic

The loading process reads the manifest at runtime and prepares the system context. Here is a simplified representation of how the loader extracts the description:

import json
import pathlib

def load_skill(skill_path: pathlib.Path):
    manifest = json.loads((skill_path / "manifest.json").read_text())
    description = manifest["description"]
    system_prompt = f"You are a specialized assistant for {manifest['skill_name']}.\n{description}"
    return system_prompt

Runtime Skill Invocation Flow

When a user invokes a skill, the previously loaded description conditions the model's response generation. The system prompt containing the skill description is combined with the user's specific query and any relevant file paths before being sent to the underlying model.

def invoke_skill(skill_name: str, user_query: str, files: list):
    system_prompt = load_skill(SKILL_ROOT / skill_name)
    full_prompt = f"{system_prompt}\nUser query: {user_query}\nRelevant files: {files}"
    response = model.complete(full_prompt)   # Claude model call

    return response

Because the description is plain English, Claude natively incorporates it into its reasoning chain without requiring formal parsing logic or additional code layers.

Key Repository Files That Demonstrate Description Flow

Several files in the anthropics/claude-plugins-community repository illustrate how descriptions move from static JSON to active model context:

• tres-finance-plugin/.claude-plugin/plugin.json – Contains the top-level plugin description that is injected into Claude's system prompt.
• tres-finance-plugin/skills/tres-report-analyzer/evals/evals.json – Shows a concrete skill_name entry and example prompts that rely on the skill's description for context.
• .claude-plugin/marketplace.json – Lists multiple skills with their description fields, demonstrating how the marketplace feeds these strings into Claude for each available skill.

Summary

• Skill descriptions reside in JSON manifest files (such as plugin.json) and are read at load time by the plugin loader.
• The description field is merged directly into Claude's system prompt, providing a human-readable behavioral contract.
• Because descriptions use plain English, Claude understands them natively, enabling skill-specific behavior without additional parsing code.
• The repository uses files like marketplace.json and skill-specific evals to organize and serve these descriptions to the model at runtime.

Frequently Asked Questions

Where is the skill description stored in the repository?

The skill description is stored in JSON manifest files, typically named plugin.json or within skill-specific directories like tres-finance-plugin/.claude-plugin/plugin.json. The description field at the root of these JSON objects contains the plain English text that Claude interprets.

Does Claude parse the description field using formal grammar or regex?

No. Claude treats the description as natural language text and injects it directly into the system prompt. The model interprets the meaning contextually using its language understanding capabilities rather than through formal parsing logic, regex patterns, or code-level interpretation.

How does the skill description differ from the examples field in the manifest?

While the description field provides high-level behavioral context and domain expectations (e.g., "analyze ledger reconciliation reports"), the examples field typically contains specific input-output pairs or invocation patterns. The description shapes the model's general approach, whereas examples demonstrate concrete usage patterns.

Can a skill function without a description field?

Technically, the loader may still initialize a skill, but without a description, Claude lacks the contextual guidance necessary to understand the skill's purpose. The model would receive an incomplete system prompt, likely resulting in generic or incorrect responses that do not align with the intended domain-specific behavior.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}