qmd-expert

name	qmd-expert
description	How to effectively use QMD (Quick Markdown Search) to pull in extra relevant context, run multi-query searches, extract high-scoring chunk IDs, and fetch their contents. Use this skill when asked to search across a large markdown knowledge base, run QMD queries, or retrieve deep context using the qmd CLI.

QMD Expert Workflow

This skill outlines the most effective workflow for AI agents to query, extract, and fetch relevant context from a knowledge base using the qmd CLI tool.

Because qmd chunks documents under the hood, standard search commands often return specific, highly relevant chunks (identified by a #hash) rather than entire files. To ensure you don't lose the pointer to the most relevant information, follow this robust workflow.

1. Understand QMD Capabilities

If you are ever unsure about the syntax, query types (lex, vec, hyde, expand), or available flags, you should start by running:

qmd skill show

This will output the embedded QMD documentation detailing how to write good queries, use intent for disambiguation, and combine search types for the best recall.

2. Formulating Queries

Identify a list of diverse queries to cast a wide net over the knowledge base. Consider mixing query types based on the user's need:

• qmd query "exact search terms": Good for general hybrid search (auto-expands).
• qmd search "keywords": Strict BM25 text search (no LLM, good for exact matches).
• qmd vsearch "natural language question": Vector similarity search (good when you don't know the exact vocabulary).

3. Bypassing Limits and Extracting Chunk IDs

By default, the qmd CLI limits results to 20 when using the --files flag. When running multiple queries to gather comprehensive context, you should bypass this limit, set a strict quality threshold, and dump the raw results to a file.

DO NOT parse the text immediately in the shell, as you might throw away useful data (like the #hash chunk ID) before verifying it.

Example Command:

# Clean up any old results
rm -f raw-qmd-results.txt

# Define your queries
queries=(
  'query "PerformanceEventTiming"'
  'vsearch "PerformanceEventTiming"'
  'search "Digging into Interaction to Next Paint"'
)

# Run queries with --all (bypasses the 20 limit) and --min-score 0.7
for q in "${queries[@]}"; do
  echo "--- Query: $q ---" >> raw-qmd-results.txt
  # The --files flag returns output in the format: #hash,score,qmd://url
  eval "qmd $q --all --min-score 0.7 --files" 2>/dev/null | grep "^#" >> raw-qmd-results.txt
done

4. Processing the Raw Results

Once you have the raw-qmd-results.txt file, you can parse it to extract the unique chunk IDs. The output from the --files flag is comma-separated: ChunkID,Score,URI.

If the result set is large, you should sort the results by score descending and take the top N chunks to avoid overwhelming your context window.

# Extract the first column (the #hash), sort, and remove duplicates
awk -F, '{print $1}' raw-qmd-results.txt | grep "^#" | sort -u > unique-chunks.txt

# Or, extract the top 10 highest-scoring unique chunks:
awk -F, '{print $2 "," $1}' raw-qmd-results.txt | sort -rn | awk -F, '!seen[$2]++ {print $2}' | head -n 10 > top-10-chunks.txt

Note: Extracting the #hash ID is critical. If you only extract the URI (qmd://...), you lose the pointer to the specific chunk that scored highly, forcing you to sift through potentially massive documents later.

5. Retrieving Chunk Content

With your list of highly relevant chunk IDs, you can now fetch the exact snippets using qmd get.

If you are fetching many chunks, be mindful of your context window. Always fetch the output into a markdown file, and consider limiting the lines returned if you are dealing with a large result set.

# Create a file to hold the context
> qmd-chunks.md

# Fetch the full content of specific chunks
while read -r chunk_id; do
  echo -e "\n\n--- Chunk: $chunk_id ---" >> qmd-chunks.md
  qmd get "$chunk_id" >> qmd-chunks.md 2>/dev/null
done < top-10-chunks.txt

# Optional: You can control how many lines of context are returned around the chunk
# using the -l flag (e.g. -l 20) to keep the context window lean.
qmd get "#888e5a" -l 20

By retrieving the chunk via its hash, you pull exactly the high-value context that matched your query threshold, keeping your context window lean and highly relevant.

6. Retrieving Full Documents

Sometimes a chunk does not provide enough surrounding context and you need to read the entire file. The --files flag outputs ChunkID,Score,URI. You can extract the URI, strip any line number suffixes, and fetch the full document using qmd get.

# Extract URIs, strip line numbers (e.g., :32), and get unique URLs
awk -F, '{print $3}' raw-qmd-results.txt | sed 's/:[0-9]*$//' | sort -u > full-urls.txt

# Fetch the full documents
> full-docs.md
while read -r url; do
  echo -e "\n\n=== Document: $url ===" >> full-docs.md
  qmd get "$url" >> full-docs.md 2>/dev/null
done < full-urls.txt