RRepoGEO

REPOGEO REPORT · LITE

d-krupke/cpsat-primer

Default branch main · commit 11bf47a9 · scanned 6/2/2026, 3:17:28 AM

GitHub: 746 stars · 60 forks

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 d-krupke/cpsat-primer, 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 opening to emphasize 'primer' nature

    Why:

    CURRENT
    *A book-style version of this primer is available at https://d-krupke.github.io/cpsat-primer/.*
    
    # The CP-SAT Primer: Using and Understanding Google OR-Tools' CP-SAT Solver
    
    _By Dominik Krupke, TU Braunschweig, with contributions
    from Leon Lan, Michael Perk, and
    others._
    
    Many combinatorially difficult
    optimization problems can, despite their proven theoretical hardness, be solved
    reasonably well in practice.
    COPY-PASTE FIX
    *A book-style version of this primer is available at https://d-krupke.github.io/cpsat-primer/.*
    
    # The CP-SAT Primer: Using and Understanding Google OR-Tools' CP-SAT Solver
    
    This repository provides a comprehensive, book-style primer and practical guide for learning and mastering Google OR-Tools' CP-SAT solver, designed for students, researchers, and developers.
    
    _By Dominik Krupke, TU Braunschweig, with contributions
    from Leon Lan, Michael Perk, and
    others._
    
    Many combinatorially difficult
    optimization problems can, despite their proven theoretical hardness, be solved
    reasonably well in practice.
  • mediumtopics#2
    Add topics that clarify the resource type

    Why:

    CURRENT
    combinatorial-optimization, cp-sat, operations-research, optimization, ortools
    COPY-PASTE FIX
    combinatorial-optimization, cp-sat, operations-research, optimization, ortools, tutorial, guide, learning, education, primer
  • lowabout#3
    Refine the 'About' description to highlight 'educational primer'

    Why:

    CURRENT
    The CP-SAT Primer: Using and Understanding Google OR-Tools' CP-SAT Solver
    COPY-PASTE FIX
    A comprehensive educational primer and practical guide for using and understanding Google OR-Tools' CP-SAT Solver.

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 d-krupke/cpsat-primer
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
google/or-tools
Recommended in 5 of 2 queries
COMPETITOR LEADERBOARD
  1. google/or-tools · recommended 5×
  2. Gurobi Optimizer · recommended 1×
  3. CPLEX Optimizer · recommended 1×
  4. scipopt/scip · recommended 1×
  5. Pyomo/pyomo · recommended 1×
  • CATEGORY QUERY
    How to solve combinatorial optimization problems with many logical constraints efficiently?
    you: not recommended
    AI recommended (in order):
    1. Gurobi Optimizer
    2. CPLEX Optimizer
    3. OR-Tools (google/or-tools)
    4. CP-SAT solver (google/or-tools)
    5. SCIP (google/or-tools)
    6. GLOP (google/or-tools)
    7. SCIP (scipopt/scip)
    8. Pyomo (Pyomo/pyomo)
    9. MiniZinc (MiniZinc/minizinc)
    10. GLPK

    AI recommended 10 alternatives but never named d-krupke/cpsat-primer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an introductory guide for using constraint programming in operations research.
    you: not recommended
    AI recommended (in order):
    1. IBM CP Optimizer
    2. Google OR-Tools (google/or-tools)
    3. MiniZinc (MiniZinc/libminizinc)
    4. Gecode (gecode/gecode)
    5. Choco Solver (chocoteam/choco-solver)

    AI recommended 5 alternatives but never named d-krupke/cpsat-primer. 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 d-krupke/cpsat-primer?
    pass
    AI named d-krupke/cpsat-primer explicitly

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

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

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

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d-krupke/cpsat-primer — 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