RRepoGEO

REPOGEO REPORT · LITE

lidangzzz/goal-driven

Default branch main · commit c8e54ba3 · scanned 6/29/2026, 2:23:14 AM

GitHub: 1,417 stars · 113 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 lidangzzz/goal-driven, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    multi-agent-system, agent-framework, llm-agents, long-running-tasks, complex-problem-solving, ai-orchestration
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumreadme#3
    Strengthen README opening to explicitly state project category

    Why:

    CURRENT
    The purpose of the Goal-Driven approach is to enable your multi-agent system (e.g., Claude Code, Codex, OpenClaw) to sustain more than 300 hours of continuous effort in solving an extremely complex problem that has a specific objective and a set of strict, well-defined criteria.
    COPY-PASTE FIX
    Goal-Driven is an open-source framework for building robust, long-running multi-agent systems. It enables agents (e.g., Claude Code, Codex, OpenClaw) to sustain continuous effort for hundreds of hours, tackling extremely complex problems with specific objectives and strict, well-defined criteria.

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 lidangzzz/goal-driven
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 · recommended 1×
  2. Google DeepMind AlphaFold · recommended 1×
  3. TensorFlow · recommended 1×
  4. PyTorch · recommended 1×
  5. Ray · recommended 1×
  • CATEGORY QUERY
    How to build an AI system for solving complex, long-running engineering problems?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Google DeepMind AlphaFold
    3. TensorFlow
    4. PyTorch
    5. Ray
    6. Gurobi
    7. CPLEX
    8. Ansys
    9. Abaqus
    10. Optuna
    11. Hyperopt

    AI recommended 11 alternatives but never named lidangzzz/goal-driven. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Frameworks for orchestrating multiple AI agents on long-term, complex problem-solving initiatives?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. CrewAI
    4. Haystack
    5. LlamaIndex
    6. Open Interpreter
    7. Semantic Kernel

    AI recommended 7 alternatives but never named lidangzzz/goal-driven. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 lidangzzz/goal-driven?
    pass
    AI named lidangzzz/goal-driven explicitly

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

  • If a team adopts lidangzzz/goal-driven in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lidangzzz/goal-driven 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 lidangzzz/goal-driven solve, and who is the primary audience?
    pass
    AI named lidangzzz/goal-driven 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 lidangzzz/goal-driven. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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Pro

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lidangzzz/goal-driven — 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