RRepoGEO

REPOGEO REPORT · LITE

alchaincyf/darwin-skill

Default branch master · commit 7c7b7909 · scanned 6/29/2026, 11:13:14 PM

GitHub: 4,411 stars · 481 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
23 /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
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 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a clarifying sentence to the README's opening to prevent misinterpretation of 'Darwin'

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the main title/tagline in both `README.md` and `README_EN.md`: `(This system is for optimizing AI Agent Skills, not related to Apple's Darwin operating system.)`
  • mediumlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with your project's goals.

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 (W&B)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases (W&B) · recommended 2×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. MLflow · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    Looking for tools to autonomously evaluate and refine agent skills based on performance metrics.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Weights & Biases (W&B)
    4. MLflow
    5. Humanloop
    6. DeepMind's Acme
    7. OpenAI Evals

    AI recommended 7 alternatives but never named alchaincyf/darwin-skill. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I systematically improve the reliability and robustness of my AI agent's capabilities?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. MLflow (mlflow/mlflow)
    3. Deepchecks (deepchecks/deepchecks)
    4. Great Expectations (great-expectations/great_expectations)
    5. Pachyderm (pachyderm/pachyderm)
    6. Sentry (getsentry/sentry)
    7. OpenTelemetry

    AI recommended 7 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 named alchaincyf/darwin-skill explicitly

    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