RRepoGEO

REPOGEO REPORT · LITE

alchaincyf/darwin-skill

Default branch master · commit 2056abfc · scanned 5/18/2026, 4:28:52 PM

GitHub: 2,542 stars · 295 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
17 /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
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 alchaincyf/darwin-skill, 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 README H1 and opening paragraph to clarify "AI Agent Skill Optimization"

    Why:

    CURRENT
    # 达尔文.skill

**像训练模型一样优化你的 Agent Skills。**

受 Andrej Karpathy 的 autoresearch 启发，将自主实验循环从模型训练搬到 Skill 优化领域。一个只能向前转的棘轮。
    COPY-PASTE FIX
    # 达尔文.skill: AI Agent Skill 自动优化系统

**一个受 Andrej Karpathy 的 autoresearch 启发，用于评估、改进和测试 AI Agent Skills 的系统，特别针对 Claude Code、Codex、OpenClaw 等平台。**
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-agents, skill-optimization, claude-code, autoresearch, agent-skills, llm-agents, autonomous-agents
  • mediumlicense#3
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root.

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 alchaincyf/darwin-skill
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Weights & Biases
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases · recommended 1×
  2. mlflow/mlflow · recommended 1×
  3. optuna/optuna · recommended 1×
  4. ray-project/ray · recommended 1×
  5. prometheus/prometheus · recommended 1×
  • CATEGORY QUERY
    How can I automatically evaluate and optimize performance for my numerous AI agent skills?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. MLflow (mlflow/mlflow)
    3. Optuna (optuna/optuna)
    4. Ray Tune (ray-project/ray)
    5. Prometheus (prometheus/prometheus)
    6. Grafana (grafana/grafana)
    7. Datadog
    8. New Relic
    9. Dynatrace
    10. Flask (pallets/flask)
    11. Django (django/django)
    12. PostgreSQL
    13. MongoDB (mongodb/mongo)

    AI recommended 13 alternatives but never named alchaincyf/darwin-skill. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a system to iteratively improve and test AI agent capabilities based on performance metrics.
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. MLflow
    3. Ray Tune
    4. TensorBoard
    5. Comet ML
    6. ClearML

    AI recommended 6 alternatives but never named alchaincyf/darwin-skill. 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 alchaincyf/darwin-skill?
    pass
    AI did not name alchaincyf/darwin-skill — 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 alchaincyf/darwin-skill in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alchaincyf/darwin-skill 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 alchaincyf/darwin-skill solve, and who is the primary audience?
    pass
    AI did not name alchaincyf/darwin-skill — 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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alchaincyf/darwin-skill — 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