RRepoGEO

REPOGEO REPORT · LITE

dmtrKovalenko/fff

Default branch main · commit a0008b1c · scanned 6/29/2026, 11:26:44 PM

GitHub: 9,373 stars · 360 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface dmtrKovalenko/fff, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify fff's core identity as an SDK for AI agents in the README's opening

    Why:

    CURRENT
    <p>
  <i>A file search toolkit for humans and AI agents. Really fast.</i>
</p>
    COPY-PASTE FIX
    <p>
  <i>fff is a high-performance, in-process file search SDK for AI agents and applications. Really fast.</i>
</p>
  • hightopics#2
    Add AI-specific topics

    Why:

    CURRENT
    bun, fff, filesearch, fzf, grep, lua, neovim, neovim-plugin, nodejs, python, rust
    COPY-PASTE FIX
    ai-agents, ai-development, file-search-sdk, in-process-search, bun, fff, filesearch, fzf, grep, lua, neovim, neovim-plugin, nodejs, python, rust
  • mediumreadme#3
    Strengthen the README's second paragraph to highlight in-process advantage for AI

    Why:

    CURRENT
    Typo-resistant path and content search, frecency-ranked file access, a background watcher, and a lightweight in-memory content index. Way faster than CLIs like ripgrep and fzf in any long-running process that searches more than once.
    COPY-PASTE FIX
    fff provides typo-resistant path and content search, frecency-ranked file access, a background watcher, and a lightweight in-memory content index. It's designed as an in-process library, making it significantly faster than CLIs like ripgrep and fzf for AI agents and applications that perform multiple searches.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface dmtrKovalenko/fff
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Lucene
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Lucene · recommended 2×
  2. Elasticsearch · recommended 1×
  3. Faiss · recommended 1×
  4. Solr · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    Looking for a high-performance file search library for AI agent development.
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch
    2. Apache Lucene
    3. Faiss
    4. Solr
    5. Weaviate
    6. Milvus
    7. PostgreSQL with pg_vector

    AI recommended 7 alternatives but never named dmtrKovalenko/fff. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an alternative to fzf or ripgrep for faster in-process file content indexing.
    you: not recommended
    AI recommended (in order):
    1. fzf
    2. ripgrep
    3. Apache Lucene
    4. Lucene.NET
    5. PyLucene
    6. Whoosh
    7. Tantivy
    8. SQLite
    9. FTS5
    10. Bleve

    AI recommended 10 alternatives but never named dmtrKovalenko/fff. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of dmtrKovalenko/fff?
    pass
    AI named dmtrKovalenko/fff explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts dmtrKovalenko/fff in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dmtrKovalenko/fff explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo dmtrKovalenko/fff solve, and who is the primary audience?
    pass
    AI named dmtrKovalenko/fff explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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dmtrKovalenko/fff — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite