RRepoGEO

REPOGEO REPORT · LITE

strongdm/attractor

Default branch main · commit fb57a55e · scanned 6/18/2026, 6:04:32 AM

GitHub: 1,203 stars · 192 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 strongdm/attractor, 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify project's core purpose in the README's opening

    Why:

    CURRENT
    # Attractor 

This repository contains [NLSpecs](#terminology) to build your own version of Attractor to create your own software factory.
    COPY-PASTE FIX
    # Attractor: Natural Language Specifications for AI Coding Agents

This repository contains NLSpecs (Natural Language Specifications) for building your own non-interactive AI Coding Agent, designed to power an autonomous software factory. This project focuses on defining agent behavior for automated code generation, not code analysis or churn metrics.
  • mediumabout#2
    Enhance About description for AI clarity

    Why:

    CURRENT
    nlspec of StrongDM's Attractor, a non-interactive Coding Agent sufficient for use in a Software Factory
    COPY-PASTE FIX
    NLSpecs for StrongDM's Attractor: a non-interactive AI Coding Agent designed for automated software development within an autonomous software factory.

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 strongdm/attractor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 1×
  2. OpenAI API · recommended 1×
  3. LangChain · recommended 1×
  4. LlamaIndex · recommended 1×
  5. Cursor · recommended 1×
  • CATEGORY QUERY
    How to automate code generation from natural language specifications using AI agents?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. OpenAI API
    3. LangChain
    4. LlamaIndex
    5. Cursor
    6. Replit AI
    7. Code Llama
    8. StarCoder
    9. Smol-Developer

    AI recommended 9 alternatives but never named strongdm/attractor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for creating an autonomous software development factory?
    you: not recommended
    AI recommended (in order):
    1. GitHub Actions
    2. GitLab CI/CD
    3. Jenkins (jenkinsci/jenkins)
    4. Argo CD (argoproj/argo-cd)
    5. Terraform (hashicorp/terraform)
    6. Kubernetes (kubernetes/kubernetes)
    7. Prometheus (prometheus/prometheus)
    8. Grafana (grafana/grafana)
    9. Vault (hashicorp/vault)

    AI recommended 9 alternatives but never named strongdm/attractor. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 strongdm/attractor?
    pass
    AI named strongdm/attractor explicitly

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

  • If a team adopts strongdm/attractor in production, what risks or prerequisites should they evaluate first?
    pass
    AI named strongdm/attractor 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 strongdm/attractor solve, and who is the primary audience?
    pass
    AI named strongdm/attractor explicitly

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

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strongdm/attractor — 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