RRepoGEO

REPOGEO REPORT · LITE

StanfordPL/stoke

Default branch develop · commit 98d8a0f0 · scanned 6/8/2026, 1:32:03 PM

GitHub: 864 stars · 84 forks

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 StanfordPL/stoke, 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 STOKE's unique position as a research superoptimizer in the README opening

    Why:

    CURRENT
    STOKE is a stochastic optimizer and program synthesizer for the x86-64 instruction set.
    COPY-PASTE FIX
    STOKE is a research project and a stochastic superoptimizer and program synthesizer for the x86-64 instruction set. It is designed to generate novel and non-obvious code sequences that can outperform general-purpose compilers and even expert hand-written code.
  • mediumabout#2
    Enhance the repository description to highlight formal verification

    Why:

    CURRENT
    STOKE: A stochastic superoptimizer and program synthesizer
    COPY-PASTE FIX
    STOKE: A stochastic superoptimizer and program synthesizer for x86-64 assembly, featuring formal verification capabilities.

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 StanfordPL/stoke
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Intel VTune Profiler
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Intel VTune Profiler · recommended 1×
  2. AMD uProf · recommended 1×
  3. LLVM's `opt` · recommended 1×
  4. GCC's `gcc` · recommended 1×
  5. Intel C++ Compiler (ICC) · recommended 1×
  • CATEGORY QUERY
    How can I automatically optimize x86-64 assembly code for maximum performance?
    you: not recommended
    AI recommended (in order):
    1. Intel VTune Profiler
    2. AMD uProf
    3. LLVM's `opt`
    4. GCC's `gcc`
    5. Intel C++ Compiler (ICC)
    6. Agner Fog's `objconv`
    7. Agner Fog's `uops.py`
    8. Intel Intrinsics
    9. Alive2

    AI recommended 9 alternatives but never named StanfordPL/stoke. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a program synthesis engine for x86 assembly with formal verification capabilities.
    you: not recommended
    AI recommended (in order):
    1. Rosetta
    2. Souper
    3. Synthesizer
    4. CVC4
    5. Z3
    6. Coq
    7. Isabelle/HOL
    8. Lean

    AI recommended 8 alternatives but never named StanfordPL/stoke. 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 StanfordPL/stoke?
    pass
    AI named StanfordPL/stoke explicitly

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

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

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

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StanfordPL/stoke — 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