RRepoGEO

REPOGEO REPORT · LITE

ZhangHanDong/harness-engineering-from-cc-to-ai-coding

Default branch main · commit e40e0fee · scanned 5/27/2026, 1:13:01 PM

GitHub: 1,358 stars · 252 forks

AI VISIBILITY SCORE
22 /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
1 / 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 ZhangHanDong/harness-engineering-from-cc-to-ai-coding, 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
  • highabout#1
    Update repository description to clarify "CC" and focus

    Why:

    CURRENT
    Harness Engineering From Claude Code source code to AI Coding
    COPY-PASTE FIX
    Harness Engineering: Deep analysis of AI Coding Agent architecture, context strategies, and production practices, based on Claude Code's source map and implementation.
  • mediumreadme#2
    Explicitly clarify "CC" in the English README's opening

    Why:

    COPY-PASTE FIX
    This book, 'Harness Engineering', offers a deep technical analysis of AI Coding Agent architecture and best practices. It specifically dissects the public release and source map of Claude Code `v2.1.88` (referred to as 'CC' in the repository name) to extract real-world engineering patterns, context strategies, permission systems, and production insights, rather than replicating official product documentation.

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 ZhangHanDong/harness-engineering-from-cc-to-ai-coding
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Git
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Git · recommended 2×
  2. Docker · recommended 2×
  3. GitHub · recommended 1×
  4. GitLab · recommended 1×
  5. Bitbucket · recommended 1×
  • CATEGORY QUERY
    Need guidance on engineering principles and architectural patterns for building robust AI coding agents.
    you: not recommended
    AI recommended (in order):
    1. Git
    2. Docker

    AI recommended 2 alternatives but never named ZhangHanDong/harness-engineering-from-cc-to-ai-coding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the practical best practices for deploying and managing AI coding agents in production?
    you: not recommended
    AI recommended (in order):
    1. Git
    2. GitHub
    3. GitLab
    4. Bitbucket
    5. DVC (iterative/dvc)
    6. MLflow (mlflow/mlflow)
    7. Weights & Biases
    8. Docker
    9. Kubernetes (kubernetes/kubernetes)
    10. AWS SageMaker
    11. Google Cloud AI Platform
    12. Azure Machine Learning
    13. Promptflow (microsoft/promptflow)
    14. LangChain (langchain-ai/langchain)
    15. LlamaIndex (run-llama/llama_index)
    16. Prometheus (prometheus/prometheus)
    17. Grafana (grafana/grafana)
    18. ELK Stack
    19. Elasticsearch (elastic/elasticsearch)
    20. Logstash (elastic/logstash)
    21. Kibana (elastic/kibana)
    22. OpenTelemetry (open-telemetry/opentelemetry-specification)
    23. Pytest (pytest-dev/pytest)
    24. GitHub Actions
    25. GitLab CI/CD
    26. Jenkins (jenkinsci/jenkins)
    27. AWS Secrets Manager
    28. Azure Key Vault
    29. HashiCorp Vault (hashicorp/vault)
    30. firejail (netblue30/firejail)

    AI recommended 30 alternatives but never named ZhangHanDong/harness-engineering-from-cc-to-ai-coding. 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 ZhangHanDong/harness-engineering-from-cc-to-ai-coding?
    pass
    AI did not name ZhangHanDong/harness-engineering-from-cc-to-ai-coding — 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?

  • If a team adopts ZhangHanDong/harness-engineering-from-cc-to-ai-coding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ZhangHanDong/harness-engineering-from-cc-to-ai-coding 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 ZhangHanDong/harness-engineering-from-cc-to-ai-coding solve, and who is the primary audience?
    pass
    AI did not name ZhangHanDong/harness-engineering-from-cc-to-ai-coding — 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?

Embed your GEO score

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite