RRepoGEO

REPOGEO REPORT · LITE

DenisSergeevitch/repo-task-proof-loop

Default branch main · commit 85362214 · scanned 6/2/2026, 9:53:18 PM

GitHub: 715 stars · 47 forks

AI VISIBILITY SCORE
28 /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
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 DenisSergeevitch/repo-task-proof-loop, 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 the README's opening to clearly state its category and core function

    Why:

    CURRENT
    # Repo Task Proof Loop
    
    This skill was built from OpenClaw-RL: Train Any Agent Simply by Talking and applies its proven approach to agentic flows in a repo-local workflow.
    
    > "next-state signals are universal, and policy can learn from all of them simultaneously."
    
    Repo Task Proof Loop is a repo-local workflow skill for non-trivial coding tasks.
    COPY-PASTE FIX
    # Repo Task Proof Loop: A Repo-Local AI Agent Workflow Skill for Verifiable Coding Tasks
    
    Repo Task Proof Loop is a specialized **AI agent workflow skill** designed for **repo-local, spec-driven coding tasks**, applying the OpenClaw-RL approach to ensure verifiable, iterative development. It creates a durable task folder under `.agent/tasks/<TASK_ID>/`, installs project-scoped Codex and Claude subagents, updates repo guidance, and drives a strict loop: `spec freeze -> build -> evidence -> fresh verify -> minimal fix -> fresh verify`.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-agents, workflow, development-tools, coding-tasks, verification, spec-driven, repo-local, proof-of-concept
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/DenisSergeevitch/repo-task-proof-loop

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 DenisSergeevitch/repo-task-proof-loop
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Significant-Gravitas/AutoGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Significant-Gravitas/AutoGPT · recommended 1×
  2. gpt-engineer-org/gpt-engineer · recommended 1×
  3. smol-ai/smol-developer · recommended 1×
  4. OpenInterpreter/open-interpreter · recommended 1×
  5. joaomdmoura/crewAI · recommended 1×
  • CATEGORY QUERY
    Seeking a framework for managing complex coding tasks using AI agents locally.
    you: not recommended
    AI recommended (in order):
    1. AutoGPT (Significant-Gravitas/AutoGPT)
    2. GPT-Engineer (gpt-engineer-org/gpt-engineer)
    3. Smol-Developer (smol-ai/smol-developer)
    4. Open Interpreter (OpenInterpreter/open-interpreter)
    5. CrewAI (joaomdmoura/crewAI)
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)

    AI recommended 7 alternatives but never named DenisSergeevitch/repo-task-proof-loop. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement a verifiable, spec-driven development loop with AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Function Calling
    2. GPT-4
    3. GPT-3.5 Turbo
    4. Pydantic
    5. LangChain
    6. LangSmith
    7. LlamaIndex
    8. Guardrails AI
    9. Marvin AI
    10. Instructor

    AI recommended 10 alternatives but never named DenisSergeevitch/repo-task-proof-loop. 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 DenisSergeevitch/repo-task-proof-loop?
    pass
    AI named DenisSergeevitch/repo-task-proof-loop explicitly

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

  • If a team adopts DenisSergeevitch/repo-task-proof-loop in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DenisSergeevitch/repo-task-proof-loop 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 DenisSergeevitch/repo-task-proof-loop solve, and who is the primary audience?
    pass
    AI did not name DenisSergeevitch/repo-task-proof-loop — 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?

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DenisSergeevitch/repo-task-proof-loop — 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