RRepoGEO

REPOGEO REPORT · LITE

china-qijizhifeng/agentic-harness-engineering

Default branch main · commit cb6ea4e0 · scanned 6/7/2026, 11:23:03 AM

GitHub: 520 stars · 60 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 china-qijizhifeng/agentic-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
  • hightopics#1
    Add specific topics to the repository

    Why:

    COPY-PASTE FIX
    agentic-ai, llm-agents, coding-agents, prompt-engineering, harness-engineering, observability, automatic-evolution, meta-learning, gpt-optimization, ai-benchmarking
  • highreadme#2
    Strengthen the README's opening statement to clarify its unique focus

    Why:

    CURRENT
    # Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

**AHE (Agentic Harness Engineering)** is an open **observability system** for automatically evolving the harness around a coding agent. The base model is held fixed; what evolves are the harness components — system prompts, tool descriptions, tool implementations, middleware, skills, sub-agents, and long-term memory.
    COPY-PASTE FIX
    # Agentic Harness Engineering (AHE): Observability-Driven Automatic Evolution for Coding-Agent Harnesses

AHE is the official code for Agentic Harness Engineering, a novel observability system designed for the automatic evolution of coding-agent harnesses. It uniquely focuses on optimizing the surrounding components—system prompts, tools, middleware, and memory—while keeping the base LLM fixed. This approach has achieved 84.7% pass@1 on Terminal-Bench 2 (GPT-5.5) and significantly boosts performance for models like GPT-5.4, outperforming existing methods like Codex/ACE/Training-Free GRPO.
  • mediumreadme#3
    Add a 'Why AHE?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why AHE? Differentiating from General LLM Frameworks' or 'AHE vs. [Competitor Names]' that explicitly explains how AHE's focus on *observability-driven harness evolution for coding agents* differs from broader LLM frameworks, prompt engineering tools, or general observability platforms.

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 china-qijizhifeng/agentic-harness-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 1×
  3. W&B Prompts · recommended 1×
  4. OpenAI Evals · recommended 1×
  5. PromptLayer · recommended 1×
  • CATEGORY QUERY
    How can I automatically optimize system prompts and tools for my AI coding agent?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. W&B Prompts
    4. OpenAI Evals
    5. PromptLayer
    6. Guardrails AI

    AI recommended 6 alternatives but never named china-qijizhifeng/agentic-harness-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help improve coding agent performance through observability and harness evolution?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. OpenTelemetry
    4. Weights & Biases Prompts
    5. Arize AI
    6. MLflow
    7. ELK Stack
    8. Grafana Loki

    AI recommended 8 alternatives but never named china-qijizhifeng/agentic-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
    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 china-qijizhifeng/agentic-harness-engineering?
    pass
    AI did not name china-qijizhifeng/agentic-harness-engineering — 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 china-qijizhifeng/agentic-harness-engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named china-qijizhifeng/agentic-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 china-qijizhifeng/agentic-harness-engineering solve, and who is the primary audience?
    pass
    AI named china-qijizhifeng/agentic-harness-engineering explicitly

    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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china-qijizhifeng/agentic-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