Agent skill

grading

Evaluates document relevance to queries using binary scoring (yes/no) with reasoning. Use when filtering retrieved documents, determining relevance for answer generation, or when the user mentions grading, filtering, or document quality.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/grading

SKILL.md

Document Grading

Instructions

Grade documents for relevance using the functions in components/grader.py. The grading uses Claude API with structured outputs for consistent binary decisions.

Default workflow:

  1. Retrieve documents from ChromaDB or web search
  2. Call grade_documents(query, documents) to grade all documents
  3. Call filter_relevant_documents(graded_docs) to get only relevant ones
  4. Check if len(relevant_docs) == 0 to trigger retry logic

Key functions:

python
# Grade multiple documents (preferred)
graded_docs = grade_documents(query, documents)

# Filter to relevant only
relevant_docs = filter_relevant_documents(graded_docs)

# Grade single document (if needed)
result = grade_document(query, document)

Grading returns:

  • relevant: Boolean (True/False)
  • reasoning: Explanation of decision

Critical for adaptive retry: When ALL documents are graded as not relevant (len(relevant_docs) == 0), the pipeline triggers query rewriting and retry.

Implementation: components/grader.py, uses GRADING_MODEL from config.py (default: Haiku 4.5), temperature 0.

Examples

Example 1: Standard grading workflow

python
from components.grader import grade_documents, filter_relevant_documents
from tools.chromadb_tool import query_chromadb

# Input
docs = query_chromadb("gaming laptops", top_k=5)
# Retrieved: 5 documents from catalog

# Grade
graded = grade_documents("gaming laptops", docs)
relevant = filter_relevant_documents(graded)

# Output
# Found 3 relevant documents out of 5 total
# Each has grading_result: {relevant: True, reasoning: "..."}

Example 2: Triggering retry

python
# Input: Vague query
docs = query_chromadb("fast computer", top_k=5)
graded = grade_documents("fast computer", docs)
relevant = filter_relevant_documents(graded)

# Output
# len(relevant) == 0
# → Triggers query rewrite and retry

Example 3: In adaptive RAG pipeline

python
# After retrieval
graded_documents = grade_documents(current_query, retrieved_documents)
relevant_documents = filter_relevant_documents(graded_documents)

if len(relevant_documents) == 0:
    # No relevant docs - rewrite query and retry
    if num_retries < RETRY_LIMIT:
        rewrite_result = rewrite_query(current_query, previous_context)
        current_query = rewrite_result['rewritten_query']
        num_retries += 1
        # Loop back to retrieval
else:
    # Found relevant docs - proceed to generation
    generate_answer(original_query, relevant_documents)

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