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Awesome-omni-skill zift

Fast, semantic, and hybrid code search tool. Use when you need to find specific code patterns, understand architectural flows, or locate symbols across a large codebase using natural language, exact strings, or regular expressions.

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/zift" ~/.claude/skills/diegosouzapw-awesome-omni-skill-zift && rm -rf "$T"
manifest: skills/data-ai/zift/SKILL.md
source content

zift Skill

zift
is a high-performance, local-first code search tool that combines semantic (vector) and lexical (FTS5) search.

Core Search Modes

1. Semantic / Hybrid (Default)

Uses natural language to find code by "meaning" or "intent".

zift "how do I handle database connections" .

Best for: High-level architectural questions, finding unfamiliar logic.

2. Exact Match (
-e
, 
--exact
)

Performs a literal, byte-perfect substring search.

zift -e "SearchResult { file_path" .

Best for: Finding specific variable names, error strings, or boilerplate.

3. Regex Search (
-r
, 
--regex
)

Uses Rust-powered regular expressions for pattern matching.

zift -r "pub (async )?fn" .

Best for: Finding all instances of a pattern (e.g., all public functions).

Workflow

Indexing

Before searching, you must index the project. 

zift
uses incremental indexing and will auto-refresh stale files on query, but a full initial index is recommended:

zift add .

Filtering

Use the 

-l
or 
--local
flag to restrict results to the current working directory (useful in monorepos).

zift "auth logic" . --local

Architecture & Performance

  • Local-first: All embeddings and indices stay on your machine (
    ~/.cache/zift
    ).
  • GPU Accelerated: Uses Metal/GPU for embedding generation via 
    llama-cpp-2
    .
  • Hybrid RRF: Fuses semantic and lexical results using Reciprocal Rank Fusion (k=60) with Power-Law scaling (γ=2.5) for intuitive percentages.
  • Speed: Exact and Regex searches skip the embedding phase and are near-instant (<50ms).

Troubleshooting

  • Stale Index: If results seem old, run 
    zift add .
    again or 
    zift forget .
    to reset.
  • Model Issues: 
    zift
    defaults to 
    nomic-embed-text-v1.5
    . Ensure the model is downloaded to the cache directory.