REPOGEO REPORT · LITE

justrach/codedb

Default branch main · commit c12e1e25 · scanned 6/28/2026, 11:21:56 PM

GitHub: 1,337 stars · 79 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)

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

AI VISIBILITY SCORE

33 /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

2 / 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 justrach/codedb, 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

Reposition README's opening to explicitly state purpose and negate misinterpretations

Why:

CURRENT

A context engine, not an editor. codedb helps agents find and understand code — search, symbols, callers, dependencies, outlines — and hands editing back to your native tools. `codedb_edit` is only a fallback.

COPY-PASTE FIX

**codedb is a high-performance code intelligence server for AI agents, not a code snippet manager or simple file database.** It helps agents find and understand code — search, symbols, callers, dependencies, outlines — and hands editing back to your native tools. `codedb_edit` is only a fallback.

mediumtopics#2

Add specific code intelligence and analysis topics

Why:

CURRENT

agentic-ai, agents, anthropic, cursor, gemini, mcp, openai, zig

COPY-PASTE FIX

agentic-ai, agents, anthropic, cursor, gemini, mcp, openai, zig, code-intelligence, code-analysis, dependency-graph, symbol-search, code-structure

lowreadme#3

Add a 'Why codedb?' section to highlight key differentiators

Why:

COPY-PASTE FIX

## Why codedb?

codedb stands out as a high-performance, Zig-powered code intelligence server specifically designed for AI agents, offering structural indexing, trigram search, and dependency graph generation with zero dependencies, unlike general-purpose code analysis platforms.

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 justrach/codedb

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Understand.ai

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Understand.ai · recommended 1×
Lattix Architect · recommended 1×
sourcegraph/sourcegraph · recommended 1×
SonarSource/sonarqube · recommended 1×
github/codeql · recommended 1×

CATEGORY QUERY
AI agents need to understand code structure and dependencies; what tools provide this context?
you: not recommended
AI recommended (in order):
1. Understand.ai
2. Lattix Architect
3. Sourcegraph (sourcegraph/sourcegraph)
4. SonarQube (SonarSource/sonarqube)
5. CodeQL (github/codeql)
6. NDepend
7. OpenGrok (OpenGrok/OpenGrok)
AI recommended 7 alternatives but never named justrach/codedb. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a high-performance code intelligence server to power agentic development workflows.
you: not recommended
AI recommended (in order):
1. Sourcegraph Cody Gateway
2. LSIF
3. Kythe
4. OpenGrok
5. Tree-sitter
6. rust-analyzer
7. gopls
8. pyright
AI recommended 8 alternatives but never named justrach/codedb. 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 justrach/codedb?
pass
AI did not name justrach/codedb — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts justrach/codedb in production, what risks or prerequisites should they evaluate first?
pass
AI named justrach/codedb 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 justrach/codedb solve, and who is the primary audience?
pass
AI named justrach/codedb explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of justrach/codedb. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/justrach/codedb.svg)](https://repogeo.com/en/r/justrach/codedb)

HTML

<a href="https://repogeo.com/en/r/justrach/codedb"><img src="https://repogeo.com/badge/justrach/codedb.svg" alt="RepoGEO" /></a>

Pro

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justrach/codedb — 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