RRepoGEO

REPOGEO REPORT · LITE

ai-boost/awesome-harness-engineering

Default branch main · commit 81308baa · scanned 5/23/2026, 9:17:45 PM

GitHub: 1,077 stars · 100 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
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 ai-boost/awesome-harness-engineering, 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
    Strengthen README's opening to clarify repo type as an 'awesome list' of resources

    Why:

    CURRENT
    Curated resources, patterns, and templates for building reliable AI agent harnesses.
    COPY-PASTE FIX
    This is a comprehensive, curated **awesome list** of resources, patterns, and templates for building reliable AI agent harnesses. It is not a framework or a tool, but a guide to the best in the field.
  • mediumreadme#2
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    Add a section or sentence to the README, e.g., 'This project is licensed under [Specify License Name(s) from LICENSE file]. See the [LICENSE file](LICENSE) for details.'
  • lowreadme#3
    Add a 'How to Use This List' section to the README

    Why:

    COPY-PASTE FIX
    Add a section titled 'How to Use This List' with brief instructions on navigating the categories, finding specific tools/patterns, or contributing.

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 ai-boost/awesome-harness-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. RasaHQ/rasa · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. Docker · recommended 1×
  • CATEGORY QUERY
    What are the best practices and tools for engineering reliable and robust AI agent systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Rasa (RasaHQ/rasa)
    4. MLflow (mlflow/mlflow)
    5. Docker
    6. Kubernetes (kubernetes/kubernetes)
    7. Prometheus (prometheus/prometheus)
    8. Grafana (grafana/grafana)
    9. pytest (pytest-dev/pytest)
    10. Weights & Biases (wandb/wandb)

    AI recommended 10 alternatives but never named ai-boost/awesome-harness-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for resources on evaluating, orchestrating, and managing memory for autonomous AI agents.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. Weaviate
    6. Pinecone
    7. Redis
    8. Haystack

    AI recommended 8 alternatives but never named ai-boost/awesome-harness-engineering. 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 ai-boost/awesome-harness-engineering?
    pass
    AI named ai-boost/awesome-harness-engineering explicitly

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

  • If a team adopts ai-boost/awesome-harness-engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ai-boost/awesome-harness-engineering 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 ai-boost/awesome-harness-engineering solve, and who is the primary audience?
    pass
    AI named ai-boost/awesome-harness-engineering explicitly

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

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ai-boost/awesome-harness-engineering — 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