🇬🇧 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).

Positioning & Ecosystem Role:
SAGE focuses on static SEO/AEO/GEO diagnostics, analysis, and intelligence extraction (to understand and diagnose). For active multi-page crawling and automated source-code remediation, see SiteProbe (to crawl, analyze, and remediate).

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 (understand & diagnose)
• Action: SiteProbe (crawl & remediate)
• 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

Install from Source / Repository (Pre-release)

# Standard installation from GitHub
pip install git+https://github.com/tmolavi/sage-audit.git

# Recommended: with local neural embeddings for the GEO pillar
pip install "sage-audit[embeddings] @ git+https://github.com/tmolavi/sage-audit.git"

# With MCP server for Claude Desktop / Cursor / AI agents
pip install "sage-audit[mcp] @ git+https://github.com/tmolavi/sage-audit.git"

# Everything at once
pip install "sage-audit[all] @ git+https://github.com/tmolavi/sage-audit.git"

PyPI Package Release Note: When the PyPI release workflow is completed, pip install sage-audit will be available directly from PyPI.

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

• Reproducibility Protocol: docs/reproducibility-protocol.md
• 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

📊 Benchmark & Reproducibility Protocol

To prevent non-reproducible AI benchmark claims and account for evaluation stochasticity, SAGE enforces a formal statistical reproducibility pipeline (full specification: docs/reproducibility-protocol.md):

• Minimum $N \ge 20$ Repeated Runs: Any public comparative claim requires at least 20 repeated runs (--public-claim), while fast single runs remain available for local development and CI testing.
• Statistical Rigor: Computes 95% Wilson Score Confidence Intervals for success rates and 95% non-parametric bootstrap confidence intervals ($B=2000$) for latency. If confidence intervals overlap, claims of superiority are disallowed.
• 18-Field Telemetry & Provenance: Every observation records 18 standard fields (benchmark_id, task_id, run_id, pass, raw_score, model, provider, model_version_if_available, framework_or_agent_version, temperature, timestamp, latency_ms, input_tokens, output_tokens, estimated_cost_usd, tool_calls, config_hash, git_sha, raw_evidence_path).
• Complete Run Preservation: Artifacts are saved under results/<benchmark>/<run-date>/ (manifest.json, observations.jsonl, summary.csv), ensuring summary.csv is 100% mathematically recoverable from observations.jsonl.
• 1-Command Reproduction: Any researcher can verify or re-run a benchmark from its pinned manifest.

# Run benchmark with public claim validation (20 runs + Wilson/Bootstrap CIs)
sage benchmark run --suite resource --runs 20 --public-claim

# Reproduce any past benchmark run from its pinned manifest
sage benchmark reproduce results/resource_eval_public_test/2026-10-02/manifest.json

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
│   ├── reproducibility-protocol.md # Statistical benchmark protocol (Wilson/Bootstrap CI)
│   └── BENCHMARK_ECOSYSTEM.md  # Ecosystem data flow & contracts
├── results/                    # Reproducible benchmark manifests & observations
├── src/
│   └── sage_audit/
│       ├── __init__.py         # exports SageAuditor, evaluation, validation, __version__
│       ├── _version.py         # single source of truth
│       ├── config.py           # SageConfig & configurable heuristic thresholds
│       ├── models.py           # pydantic schemas (EvidenceMetadata, CspDetails, Reports)
│       ├── evaluation.py       # Benchmark runner, 18-field telemetry, Wilson/Bootstrap CI
│       ├── memory_eval.py      # SAGE Memory benchmark suite
│       ├── validation.py       # Spearman, AUROC, Precision@k, Brier score, calibration
│       ├── cli.py              # sage audit | generate-llms | validate | benchmark | 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_reproducibility_protocol.py # 20-run, Wilson/Bootstrap CI, manifest pinning 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

          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)

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

# اجرای بنچ‌مارک با اعتبارسنجی آماری ادعای عمومی (۲۰ تکرار)
sage benchmark run --suite resource --runs 20 --public-claim

# بازتولید و اعتبارسنجی یک اجرای گذشته از طریق فایل مانیفست
sage benchmark reproduce results/resource_eval_public_test/2026-10-02/manifest.json

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

🇹🇷 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

📊 SAGE Karşılaştırmalı Değerlendirme ve Yeniden Üretilebilirlik Protokolü

Tek çalıştırmalı testlerdeki gürültüyü ve dayanaksız karşılaştırmalı iddiaları önlemek amacıyla SAGE, istatistiksel olarak doğrulanabilir bir değerlendirme hattı sunar (ayrıntılı kılavuz: docs/reproducibility-protocol.md):

• Kamuya Açık İddialar İçin Minimum 20 Tekrar (--public-claim): Karşılaştırmalı tüm kamu iddiaları en az 20 bağımsız çalıştırma gerektirir; yerel geliştirmede ise hızlı tek çalıştırmalar desteklenir.
• İstatistiksel Güven Aralıkları (%95 Wilson ve Bootstrap CI): Başarı oranı %95 Wilson Skor Güven Aralığı ile, gecikmeler ise parametrik olmayan bootstrap (%95 CI) ile hesaplanır. Güven aralıkları örtüşüyorsa kesin üstünlük ilan edilmez.
• 18 Alanlı Ham Kanıt Şeması ve Tam Köken Takibi: Her gözlem results/<benchmark>/<tarih>/ dizininde manifest.json, observations.jsonl ve summary.csv olarak kaydedilir.
• Tek Komutla Yeniden Üretim:

# Kamuya açık iddia protokolü ile 20 tekrarlı çalıştırma
sage benchmark run --suite resource --runs 20 --public-claim

# Manifest dosyası üzerinden geçmiş çalıştırmayı yeniden üretme
sage benchmark reproduce results/resource_eval_public_test/2026-10-02/manifest.json

🚧 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