🧭 SAGE — Search, Answer, & Generative Engine Auditor

sage-audit — The Unified 3-Pillar Audit Engine for SEO, Entity AEO, and Generative Engine Optimization (GEO)

Inspect any URL or raw HTML and score it for the era of AI answers — from classical technical SEO to JSON-LD entity graphs to RAG Citation Survival Probability — in one deterministic, crash-proof command.

English · فارسی · Türkçe

🇬🇧 English

The Problem: Ten Blue Links Are Dying

Search is undergoing its biggest architectural shift since PageRank. Users no longer click 10 blue links — they get synthesized answers from ChatGPT Search, Perplexity, Claude, Google AI Overviews and Copilot. These engines do not rank pages; they run vector RAG pipelines: they fetch your HTML, strip boilerplate, split it into passages, embed it, retrieve top-k chunks, and let an LLM cite whatever survives.

Classical SEO tools stop at title tags and page speed. They tell you nothing about:

• whether robots.txt is blocking GPTBot, PerplexityBot, ClaudeBot, Google-Extended, Amazonbot or Applebot-Extended;

• whether your JSON-LD entity graph is rich, complete, and anchored to Wikidata/Crunchbase via sameAs;

• whether the first 50–70 words of each section form a direct, fluff-free, factual answer an engine can quote;

• whether your passages are chunked at coherent semantic boundaries (60–120 tokens);

• whether your content survives retrieval — or drowns in semantic entropy and is never cited.

SAGE (sage-audit) closes that gap. One open-source engine, three pillars, four interfaces (CLI · Python library · MCP server · JSON/Markdown exports).

Related MCP server: OctoBoost SEO MCP Server

The 3 Pillars

                    ┌───────────────────────────────────────┐
          L5        │  CITATION SURVIVAL PROXY (CSP) +      │
                    │  llms.txt / rag_ready_chunks.json     │
                    ├───────────────────────────────────────┤
          L4        │  RAG RETRIEVAL SIMULATION             │
                    │  top-k ranking · cosine distance      │
                    ├───────────────────────────────────────┤
          L3        │  VECTOR EMBEDDINGS                    │
                    │  fastembed → sentence-transformers →  │
                    │  deterministic hashed n-gram fallback │
                    ├───────────────────────────────────────┤
          L2        │  SEMANTIC CHUNKING                    │
                    │  60–120 token passage boundaries      │
                    ├───────────────────────────────────────┤
          L1        │  CLEAN DOM EXTRACTION                 │
                    │  boilerplate stripping (Trafilatura)  │
                    └───────────────────────────────────────┘

🏆 Integration with Molavi AI Visibility Index (MAVI) & Benchmark Contribution

sage-audit provides the foundational diagnostic signals (L1–L4) for the Molavi AI Visibility Index (MAVI) and supports the GEO, SEO & Digital Marketing Agency Iran 2026 Benchmark:

• L1 Technical Accessibility: Evaluated via SAGE Technical Auditor (robots.txt AI crawler policies for GPTBot, PerplexityBot, ClaudeBot; clean DOM extraction; HTTP security headers).

• L2 Semantic Extractability: Evaluated via SAGE semantic passage chunking (60–120 token boundaries) and clean text-to-code ratio.

• L3 Entity Clarity: Evaluated via SAGE AEO JSON-LD entity graph validation (Organization, FAQPage, sameAs Wikidata/Crunchbase disambiguation).

• L4 Citation Readiness: Evaluated via SAGE Citation Survival Proxy (CSP) using in-memory RAG retrieval simulation and semantic entropy scoring.

• L5 Empirical AI Visibility: Measured by GEO-Scope across real multi-model provider executions.

• Ecosystem Architecture: See Benchmark Ecosystem Map for how SAGE diagnostics feed directly into MAVI composite scoring and SiteProbe automated remediation.

🏛️ Ecosystem

SAGE Audit operates as the static diagnostic component of the Molavi AI Visibility Stack:

• Discovery: AnswerPath GEO

• Measurement: GEO-Scope

• Diagnostics: SAGE Audit

• Action: SiteProbe

• Protocol: MCP GEO Server

📖 Runnable Python Example

Run the bundled 3-pillar audit script:

python examples/audit_example.py

A complete JSON audit output sample is available at examples/example_audit.json.

Installation

pip install sage-audit

# recommended: local neural embeddings for the GEO pillar
pip install "sage-audit[embeddings]"

# MCP server for Claude Desktop / Cursor / AI agents
pip install "sage-audit[mcp]"

# everything at once
pip install "sage-audit[all]"

🚧 Not on PyPI yet? Until the first PyPI release is cut (step-by-step runbook in Distribution & Publishing below), install straight from the repository:

pip install git+https://github.com/tmolavi/sage-audit.git

Zero-crash guarantee: if no neural embedding package is installed (or a model download fails), the GEO pillar silently falls back to a deterministic hashed n-gram TF vectorizer. Results stay stable and reproducible — the tool never crashes.

Quickstart (CLI)

sage audit https://your-site.com
sage audit https://your-site.com --format markdown -o report.md --save-artifacts ./out
sage audit --raw-html page.html --format json | jq .overall_score
sage generate-llms https://your-site.com -o llms.txt --also-chunks
sage audit https://your-site.com --fail-under 70    # CI quality gate → exit 1

Sample (rich) terminal output:

╭────────────────────────────── SAGE AUDIT REPORT ──────────────────────────────╮
│  https://acme-analytics.example.com/                                          │
│  2026-09-01 09:45 UTC · 9 ms · sage-audit v1.0.0                              │
│  ████████████████████░░░░  84.8/100  (Grade B)                                │
╰────────────────────────────────────────────────────────────────────────────────╯
Pillar 1 — Technical SEO            —  96.9/100 (Grade A+)
│ Canonical URL           ✔ pass  │ canonical → https://acme-analytics.example.com
│ Meta robots directives  ✔ pass  │ content="index,follow"
│ robots.txt AI-crawlers  ⚠ warn  │ GPTBot: blocked; ClaudeBot: allowed; …
Pillar 2 — Answer Engine Optimization (AEO) —  90.5/100 (Grade A)
│ Entity authority        ✔ pass  │ 5 sameAs URL(s); anchors: GitHub, LinkedIn,
│                                 │ Wikidata, Wikipedia, X/Twitter
│ Direct-answer density   ✔ pass  │ 5 sections · avg window score 0.74 ·
│                                 │ 5/5 open with an explicit answer
Pillar 3 — Generative Engine Optimization (GEO) —  68.6/100 (Grade C)
│ Chunk size compliance   ✔ pass  │ 100% of chunks inside 60–120 tokens
│ Citation Survival       ✖ fail  │ CSP 43.1% across 8 simulated RAG queries
│ Semantic retrieval      ⚠ warn  │ entropy 0.67 — passages look alike
╭────────────────────── Priority recommendations ───────────────────────╮
│ ✖ Citation Survival Probability (GEO)                                 │
│     Retrieval attention is diffuse — consolidate per-topic sections   │
│     and front-load definitional sentences.                            │
╰─────────────────────────────────────────────────────────────────────────╯
Artifacts generated: llms.txt, rag_ready_chunks.json

Python API (Library)

from sage_audit import SageAuditor

auditor = SageAuditor(embedding_backend="auto", top_k=3)

# Live URL — fetches the page AND its robots.txt
report = auditor.audit("https://molavi.pro")

# Raw HTML — fully offline
report = auditor.audit_html(html_source, url="https://example.local/")

print(report.overall_score, report.grade)          # e.g. 84.8 'B'
print(report.seo.score, report.aeo.score, report.geo.score)
print(report.artifacts["llms.txt"])                # ready-to-serve manifest

for finding in report.geo.findings:
    if finding.recommendation:
        print(finding.title, "→", finding.recommendation)

MCP Server (Claude Desktop · Cursor · Agents)

SAGE ships a FastMCP server exposing four autonomous tools: sage_audit_url, sage_audit_html, sage_generate_llms_txt, sage_version.

sage mcp                       # stdio transport (default)
sage mcp --transport sse --host 127.0.0.1 --port 8642

Register it in claude_desktop_config.json:

{
  "mcpServers": {
    "sage-audit": {
      "command": "sage",
      "args": ["mcp"]
    }
  }
}

Or without installing, via uvx:

{
  "mcpServers": {
    "sage-audit": {
      "command": "uvx",
      "args": ["--from", "sage-audit[mcp]", "sage", "mcp"]
    }
  }
}

🔬 Evidence & Diagnostic Benchmarks

• Sample Diagnostic Audit Output: examples/example_audit.json

• Standalone Offline Diagnostic Demo: examples/public_demo/

• Mathematical Specification & Taxonomy: docs/methodology.md

• Ecosystem Data Flow & Contracts: docs/BENCHMARK_ECOSYSTEM.md

• Cross-Repository Evidence Map: Ecosystem Evidence Flow

Scoring Model & Epistemic Evidence Taxonomy

• Every check is a weighted Finding tagged with structured epistemic Evidence Taxonomy (E0–E5) metadata:

  • E0: Deterministic technical facts (HTTP status, headers, HTML tag presence, syntax).

  • E1: Standards & specifications (W3C HTML5, Schema.org, IETF RFCs).

  • E2: Documented search engine & platform guidance (Google Search Central, bot policies).

  • E3: Empirical evidence from validated benchmark datasets.

  • E4: Industry heuristics & configurable thresholds (word counts, chunk sizes, answer density).

  • E5: Experimental proxies & mathematical models (CSP, softmax semantic entropy).

• Prohibition of Unproven Claims: SAGE strictly labels heuristics and proxies without asserting unproven direct ranking-factor claims.

• Pillar score = weighted mean × 100. Overall = 0.30·SEO + 0.35·AEO + 0.35·GEO (configurable).

• Grades: A+ ≥ 93 · A ≥ 85 · B ≥ 75 · C ≥ 65 · D ≥ 50 · F < 50.

• Citation Survival Proxy (CSP): A heuristic proxy score (0–100) evaluating candidate passage retrieval prominence and low semantic entropy under simulated dense vector retrieval: $$\text{CSP} = \text{mean}(0.6 \cdot \text{prominence} + 0.4 \cdot (1 - \text{entropy})) \times 100$$ (Note: CSP is an uncalibrated heuristic retrieval proxy, not a literal probability of external AI citation).

• Empirical Validation Framework: Compare SAGE/CSP scores against real observed AI search citation logs via Spearman correlation ($\rho$), AUROC, Precision@k, Brier score, and probability calibration curves (sage validate).

• Detailed specification in docs/methodology.md.

Project Structure

sage-audit/
├── LICENSE                     # MIT — Copyright (c) 2026 Taqi Molavi
├── pyproject.toml              # build system, metadata, deps, `sage` entrypoint
├── requirements.txt
├── .gitignore
├── docs/
│   └── methodology.md          # Full Evidence Taxonomy & CSP specification
├── src/
│   └── sage_audit/
│       ├── __init__.py         # exports SageAuditor, models, validation, __version__
│       ├── _version.py         # single source of truth
│       ├── config.py           # SageConfig & configurable heuristic thresholds
│       ├── models.py           # pydantic schemas (EvidenceMetadata, CspDetails, Reports)
│       ├── validation.py       # Spearman, AUROC, Precision@k, Brier score, calibration
│       ├── cli.py              # sage audit | generate-llms | validate | mcp
│       ├── core.py             # SAGE orchestrator (0.30/0.35/0.35 configurable fusion)
│       ├── auditors/
│       │   ├── __init__.py
│       │   ├── seo_auditor.py  # Pillar 1 — Technical SEO (E0–E4)
│       │   ├── aeo_auditor.py  # Pillar 2 — Entity AEO (E1–E4)
│       │   └── geo_auditor.py  # Pillar 3 — GEO / RAG simulation / CSP (E0, E4, E5)
│       ├── server/
│       │   ├── __init__.py
│       │   └── mcp_server.py   # FastMCP tools for AI agents
│       └── utils/
│           ├── __init__.py
│           ├── extractor.py    # clean text / DOM parser (Trafilatura + BS4)
│           ├── formatter.py    # terminal / JSON / Markdown renderers with taxonomy
│           └── text.py         # tokenizer, sentence splitter, vector math
├── tests/
│   ├── __init__.py
│   ├── test_auditors.py        # SEO, AEO, GEO, core, CLI unit tests
│   ├── test_taxonomy.py        # Evidence taxonomy & ranking claims tests
│   ├── test_csp_hardening.py   # Hardened CSP proxy semantics tests
│   ├── test_validation.py      # Statistical validation & calibration tests
│   └── test_config.py          # Heuristic threshold override tests
└── README.md

Development

git clone https://github.com/tmolavi/sage-audit.git
cd sage-audit
pip install -e ".[dev]"
pytest -q

📦 Distribution & Publishing

Two "publish" targets — don't confuse them

⚠️ Until the first PyPI release, SAGE is installed directly from the repo:

pip install git+https://github.com/tmolavi/sage-audit.git
# with every optional extra (embeddings + mcp):
pip install "sage-audit[all] @ git+https://github.com/tmolavi/sage-audit.git"

Path A — Manual PyPI publish (≈5 minutes)

GitHub and PyPI are separate services: pushing code to GitHub never publishes a pip package. To put sage-audit on PyPI the maintainer must:

1. Create a PyPI account at pypi.org/account/register, verify the email address, and enable two-factor authentication (PyPI refuses uploads from accounts without 2FA).

2. Mint an API token at pypi.org/manage/account/token → Add API token.

   • The project does not exist on PyPI yet, so the first token must be scoped "Entire account" — project-scoped tokens only become available after the first upload.

   • The token starts with pypi- — treat it like a password and never commit it to git.

3. Build & upload from the repo root:

   pip install --upgrade build twine
   python -m build        # → dist/sage_audit-1.0.0-py3-none-any.whl + .tar.gz
   twine upload dist/*    # username: __token__    password: pypi-...

4. Verify at pypi.org/project/sage-audit — from that moment pip install sage-audit works worldwide.

5. Hygiene — revoke the token immediately after the upload (or create a new project-scoped one and keep the expiry short).

Path B — Trusted Publishing (recommended, zero secrets)

This repository already ships .github/workflows/publish.yml, which uses the official pypa/gh-action-pypi-publish action over GitHub OIDC — no token is ever stored anywhere.

One-time setup (PyPI side):

1. Go to pypi.org/manage/account/publishing → Add a new pending publisher and enter:

   • PyPI project name: sage-audit

   • Owner: tmolavi · Repository: sage-audit

   • Workflow filename: publish.yml

2. Cut a release on GitHub (Releases → Draft a new release → tag v1.0.0): the workflow builds the wheel + sdist and publishes them automatically — every future release is a one-click PyPI publish.

GitHub Topics

seo · aeo · geo · generative-engine-optimization · ai-search · rag · mcp-server · perplexity · chatgpt-search · llms-txt · schema-markup · entity-seo

🇮🇷 فارسی

SAGE — سِیج: اولین موتور ممیزی متن‌باز سه‌گانهٔ سئو، AEO و GEO در دنیا

sage-audit (مخفف Search, Answer, & Generative Engine Auditor) اولین ابزار متن‌باز جهان است که هر سه ستون بهینه‌سازی برای موتورهای جست‌وجو، موتورهای پاسخ‌گو و موتورهای مولد را در یک دستور واحد ممیزی می‌کند. توسعه‌دهنده و معمار این پروژه تقی مولوی است.

چرا SAGE؟

دوران ده لینک آبی تمام شده است. کاربران امروز پاسخ خود را از ChatGPT، Perplexity، Claude و Google AI Overviews می‌گیرند؛ سیستم‌هایی که صفحهٔ شما را «رتبه‌بندی» نمی‌کنند، بلکه آن را به قطعات برداری (Chunk) تقسیم کرده و از طریق خط‌لولهٔ RAG بازیابی می‌کنند. ابزارهای کلاسیک سئو هیچ‌چیز دربارهٔ این‌ها نمی‌گویند:

• سیاست robots.txt در برابر خزنده‌های هوش مصنوعی (GPTBot، PerplexityBot، ClaudeBot، Google-Extended، Amazonbot، Applebot-Extended)؛

• عمق گراف موجودیت JSON-LD و لنگرهای اعتبار sameAs (ویکی‌دیتا، ویکی‌پدیا، Crunchbase)؛

• چگالی «پاسخ مستقیم» در ۵۰ تا ۷۰ کلمهٔ نخست هر بخش؛

• و احتمال بقای ارجاع (Citation Survival Probability) در خط‌لولهٔ بازیابی برداری.

معماری هرم ۵ لایهٔ GEO

          لایهٔ ۵  │  احتمال بقای ارجاع + تولید llms.txt و rag_ready_chunks.json
          لایهٔ ۴  │  شبیه‌سازی بازیابی RAG (رتبه‌بندی top-k با شباهت کسینوسی)
          لایهٔ ۳  │  تعبیهٔ برداری: fastembed ← sentence-transformers ← بردارساز قطعی n-gram
          لایهٔ ۲  │  قطعه‌بندی معنایی متن (مرزهای ۶۰ تا ۱۲۰ توکن)
          لایهٔ ۱  │  استخراج DOM تمیز و حذف صفحه‌آرایی (Trafilatura)

یکی از خروجی‌های خودکار، فایل llms.txt است: سندی استاندارد که به LLMها اعلام می‌کند صفحهٔ شما چه ساختاری دارد — SAGE آن را همراه با rag_ready_chunks.json به‌صورت خودکار تولید می‌کند.

نصب و اجرای سریع

pip install sage-audit
pip install "sage-audit[embeddings]"   # توصیه‌شده: تعبیهٔ عصبی محلی
pip install "sage-audit[mcp]"          # سرور پروتکل MCP

sage audit https://molavi.pro
sage audit https://molavi.pro --format markdown -o report.md --save-artifacts ./out
sage generate-llms https://molavi.pro -o llms.txt --also-chunks
sage audit https://molavi.pro --fail-under 70   # دروازهٔ کیفی در CI
sage mcp                                        # اتصال به Claude Desktop / Cursor

🚧 هنوز روی PyPI منتشر نشده؟ تا قبل از اولین انتشار رسمی (راهنمای گام‌به‌گام در بخش انتشار روی PyPI پایین‌تر)، مستقیم از ریپو نصب کنید:

pip install git+https://github.com/tmolavi/sage-audit.git

انتشار روی PyPI (اختیاری — راهنمای گام‌به‌گام نگهدارنده)

دو مقصد انتشار را با هم اشتباه نکنید: گیت‌هاب (سورس‌کد — منتشر شده ✅) و PyPI (پکیج pip روی pypi.org — نیازمند اکانت جدا و یک‌بار راه‌اندازی ⏳). پوش کردن کد به گیت‌هاب به‌هیچ‌وجه پکیج pip منتشر نمی‌کند.

تا قبل از اولین انتشار رسمی، هر کسی می‌تواند مستقیم از ریپو نصب کند:

pip install git+https://github.com/tmolavi/sage-audit.git

برای انتشار رسمی روی PyPI:

1. ساخت اکانت PyPI — در pypi.org/account/register ثبت‌نام کنید، ایمیل را تأیید و احراز هویت دو مرحله‌ای (2FA) را فعال کنید (بدون 2FA پلتفرم اجازهٔ آپلود نمی‌دهد).

2. ساخت API Token — در pypi.org/manage/account/token روی Add API token بزنید. چون پروژه هنوز روی PyPI وجود ندارد، scope اولین توکن باید «Entire account» باشد (توکن پروژه‌محور فقط بعد از اولین آپلود قابل ساخت است). توکن با pypi- شروع می‌شود و مثل رمز عبور محرمانه است.

3. Build و آپلود — از ریشهٔ پروژه:

   pip install --upgrade build twine
   python -m build        # خروجی: dist/sage_audit-1.0.0-py3-none-any.whl و .tar.gz
   twine upload dist/*    # username: __token__ — password: همان توکن pypi-...

4. کنترل نهایی — صفحهٔ pypi.org/project/sage-audit را باز کنید؛ از این لحظه pip install sage-audit در سراسر جهان کار می‌کند.

5. امنیت — بلافاصله بعد از آپلود، توکن را Revoke کنید (یا یک توکن جدید پروژه‌محور با تاریخ انقضای کوتاه بسازید). توکن را هرگز داخل git کامیت نکنید.

مسیر جایگزین بدون توکن (Trusted Publishing): فایل آمادهٔ .github/workflows/publish.yml در ریپو موجود است؛ کافی است یک‌بار در تنظیمات Publishing اکانت PyPI گیت‌هاب (Owner: tmolavi، Repo: sage-audit، Workflow: publish.yml) را به‌عنوان Trusted Publisher معرفی کنید — از آن به بعد هر Release جدید روی گیت‌هاب، به‌طور خودکار روی PyPI منتشر می‌شود، بدون اینکه هیچ توکنی در جایی ذخیره شود.

تضمین پایداری

اگر بستهٔ تعبیهٔ عصبی نصب نباشد یا دانلود مدل شکست بخورد، ستون GEO به‌صورت خودکار به بردارساز قطعی و بازتولیدپذیر n-gram کاهش می‌یابد؛ ابزار هرگز متوقف نمی‌شود و نتایج همواره قطعی و قابل اتکا می‌مانند.

توسعه‌دهنده

🇹🇷 Türkçe

SAGE Projesi: SEO, AEO ve GEO için Dünyanın İlk Birleşik Açık Kaynak Denetim Motoru

sage-audit (Search, Answer, & Generative Engine Auditor), bir web sayfasını üç sütunda birden denetleyen dünyanın ilk birleşik açık kaynak motorudur: klasik teknik SEO, varlık tabanlı AEO (Cevap Motoru Optimizasyonu) ve GEO (Üretken Motor Optimizasyonu). Projenin geliştiricisi ve mimarı Taqi Molavi'dir.

Neden SAGE?

Mavi bağlantılar çağı kapanıyor. Kullanıcılar artık yanıtları ChatGPT, Perplexity, Claude ve Google AI Overviews gibi sistemlerden alıyor. Bu sistemler sayfanızı sıralamaz; HTML'inizi alır, metni 60–120 tokenlık parçalara böler, vektör uzayına gömer ve yalnızca RAG hattından sağ çıkan pasajları alıntılar. SAGE şunları ölçer:

• robots.txt dosyasının yapay zekâ tarayıcılarına (GPTBot, PerplexityBot, ClaudeBot, Google-Extended, Amazonbot, Applebot-Extended) karşı politikası;

• JSON-LD varlık grafiğinin derinliği ve Wikidata / Wikipedia / Crunchbase gibi sameAs otorite sinyalleri;

• Her bölümün ilk 50–70 kelimesindeki doğrudan yanıt yoğunluğu;

• Kosinüs uzaklığı + anlamsal entropi ile Alıntı Hayatta Kalma Olasılığı (CSP);

• 5 katmanlı GEO piramidi sonunda otomatik olarak llms.txt ve rag_ready_chunks.json üretimi.

Kurulum ve Hızlı Başlangıç

pip install sage-audit
pip install "sage-audit[embeddings]"   # önerilen: yerel sinirsel gömme
pip install "sage-audit[mcp]"          # MCP sunucusu

sage audit https://ornek-siteniz.com
sage audit https://ornek-siteniz.com --format markdown -o rapor.md --save-artifacts ./cikti
sage generate-llms https://ornek-siteniz.com -o llms.txt --also-chunks
sage audit https://ornek-siteniz.com --fail-under 70   # CI kalite kapısı
sage mcp                                               # Claude Desktop / Cursor bağlantısı

🚧 Henüz PyPI'de yok mu? İlk resmî PyPI sürümüne kadar (aşağıdaki kılavuz) doğrudan depodan kurun:

pip install git+https://github.com/tmolavi/sage-audit.git

PyPI'de Yayınlama (İsteğe Bağlı — Adım Adım Kılavuz)

İki yayın hedefini karıştırmayın: GitHub (kaynak kodu — yayınlandı ✅) ve PyPI (pypi.org'daki pip paketi — ayrı hesap ve tek seferlik kurulum gerekir ⏳). Kodu GitHub'a push etmek, asla bir pip paketi yayınlamaz.

İlk resmî PyPI sürümünden önce herkes doğrudan depodan kurabilir:

pip install git+https://github.com/tmolavi/sage-audit.git

Resmî PyPI yayını için:

1. PyPI hesabı — pypi.org/account/register adresinden kaydolun, e-postayı doğrulayın ve 2FA'yı etkinleştirin (2FA olmadan yükleme yapılamaz).

2. API tokenı — pypi.org/manage/account/token sayfasında Add API token. Proje henüz PyPI'de olmadığı için ilk tokenın kapsamı «Entire account» olmalıdır (proje bazlı token ancak ilk yüklemeden sonra oluşturulabilir). Token pypi- ile başlar ve parola gibi gizli tutulmalıdır.

3. Derleme & yükleme — depo kökünden:

   pip install --upgrade build twine
   python -m build        # çıktı: dist/sage_audit-1.0.0-py3-none-any.whl + .tar.gz
   twine upload dist/*    # kullanıcı adı: __token__ — parola: pypi-... tokenı

4. Doğrulama — pypi.org/project/sage-audit sayfasını açıp pip install sage-audit komutunu test edin; artık tüm dünyada çalışır.

5. Güvenlik — yüklemeden hemen sonra tokenı revoke edin (ya da yalnızca bu projeye özel, kısa süreli bir token oluşturun). Tokenı asla git'e commit etmeyin.

Tokensiz alternatif (Trusted Publishing): depoda hazır bulunan .github/workflows/publish.yml dosyası sayesinde; PyPI Publishing ayarlarından tmolavi/sage-audit (workflow: publish.yml) bir kez Trusted Publisher olarak tanımlandığında, bundan sonraki her GitHub Release'i otomatik olarak PyPI'de yayınlanır — hiçbir token saklanmadan.

Geliştirici

SAGE; sinirsel gömme modeli bulunamadığında otomatik olarak deterministik n-gram vektörleştiriciye geçer — araç asla çökmez, sonuçlar her zaman tekrar üretilebilirdir.

💬 Community & External Collaboration

We welcome researchers and engineers contributing to multi-pillar diagnostic algorithms and evidence taxonomies:

• Discussions: GitHub Discussions across General, Research, Ideas, Help, and Show & Tell.

• First Contribution Guide: docs/FIRST_CONTRIBUTION.md

• Methodology & CSP Formalization: docs/methodology.md

• Report Issues: GitHub Issues

• Contribution Standards: CONTRIBUTING.md and SECURITY.md

Author & Architect

Taqi Molavi (Taghi Molavi)

🌐 molavi.pro · 🔬 Molavi R&D Think Tank — molavi.pro/research · 🐙 github.com/tmolavi

If SAGE helps your research or your rankings, please star ⭐ the repository — it keeps the engine open-source and evolving.

License: MIT — Copyright (c) 2026 Taqi Molavi