lightsifter

Sift Light — AI skill for Claude Code

AI community

Evidence-first search for AI agents / Claude Code & Codex & Pi & OMP & kimi & mcp.

How to install Sift Light

This entry records only its repository, not the path inside it, so there is no exact command to give. Open lightsifter/sift-light and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Sift Light does

Evidence-first search for AI agents / Claude Code & Codex & Pi & OMP & kimi & mcp.

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README

sift-light

English · [简体中文](README.zh-CN.md)

**sift-light — Evidence-first search for AI agents.**

**A general-purpose local search plugin that helps agents find files, documents, notes, logs and other text material.**

Think of a patient librarian: describe what you need, and it helps locate the shelf, open the relevant page and follow the next lead. A small search brings the passages straight to you. A broad search starts with a map so you can decide where to look first.

How it helps

Find a passage without opening every folder

Looking for an error message, a sentence or a name is like giving a librarian a keyword. When there are only a few matches, the plugin returns their text and locations directly, saving repeated file opening and scrolling.

Start with a map when the collection is large

“Which documents mention refunds?” can produce a lot of material. The plugin first presents matching files and snippets, like marking promising stops on a map. The agent can choose what to open before filling the conversation with entire documents.

Keep your bookmark for the next question

“Continue from where we stopped” can follow the existing result to its next page. The agent can also open the surrounding text of a match, like returning to a bookmarked passage to read what came before and after it.

Recover when the remembered wording is not exact

Use `mode: "hybrid"` with one natural-language `query` when a sentence may have been remembered with different wording. Hybrid always runs an exact literal search and the installed local Concept model under one owned request. Exact evidence appears first; semantic candidates are clearly labeled, ranked only by similarity and removed when they overlap an exact match. The initial page shares counts, coverage, source references, one inspection cursor and a compact preview instead of concatenating two complete responses. `conceptLimit` changes only the non-overlapping semantic supplement (default 3, maxim