Jyotish Vedic Astrology — AI-Native Full-Reading System

What makes this different: This is not a calculator. It is an AI-native Jyotish analysis system that combines calculation engines, interpretive workflows, confidence auditing, and graceful degradation — organized into a reproducible full-reading pipeline.

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Table of Contents

• What Is This
• Quick Start
• Core Workflow
• Technique Coverage
• Why This Exists (Competitive Context)
• Honest Assessment
• Project Status
• Development
• Contributing
• License

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What Is This

This is a Vedic (Jyotish) astrology analysis system designed for deep, auditable full-chart readings. It is NOT a simple ephemeris calculator — it is a multi-stage interpretive pipeline that:

1. Computes divisional charts (D1/D9/D10/...) via Swiss Ephemeris
2. Runs 44+ techniques (Dashas, Yogas, Shadbala, Ashtakavarga, Transits...)
3. Routes the analysis through strict workflow paths depending on question type (career / relationship / wealth / timing)
4. Audits every technique used — declaring what was called, what was partial, what was missing, and how that affects confidence
5. Degrades gracefully — partial techniques are labeled, not silently over-promising

Key Differentiators (vs. PyJHora / VedAstro / Maitreya)

Feature	This Project	PyJHora	VedAstro	Maitreya
Full-reading pipeline (one command)	✅	❌	❌	❌
Strict workflow router (per-question-type)	✅	❌	❌	❌
Technique Audit Table (confidence labeling)	✅	❌	❌	❌
Capability degradation (partial ≠ covered)	✅	❌	❌	❌
MEVG external verification gates	✅	❌	❌	❌
44+ techniques integrated	✅	✅ (50+)	✅ (200+)	✅
Traditional algorithm benchmarked	🔶 partial	✅	✅	✅
Docker / MCP Server	🔶 planned	❌	✅	❌
English docs / PyPI package	🔶 in progress	✅	✅	✅

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Quick Start

Prerequisites

• Python 3.11+
• Swiss Ephemeris (pyswisseph or ephem)
• Optional: pypdf, pdfplumber (for PDF chart input)

Install

# Clone the repository
git clone https://github.com/732642856/yinduzhanxing.git
cd yinduzhanxing

# Install Python dependencies
pip install -r requirements.txt

# Verify installation
python3 scripts/audit_capabilities.py --mode validate
# Expected: valid=true, problem_count=0

Minimal Full Reading (5 minutes)

python3 scripts/jyotish_engine.py full-reading \
  --year 1990 --month 6 --day 15 \
  --hour 10 --minute 30 \
  --lat 28.6139 --lon 77.2090 --tz 5.5 \
  --age 36 \
  --transit-date 2026-06-04

Output: ~45 computed modules, zero errors, complete structured reading with technique audit table.

Sample Output (abbreviated)

═══ FULL READING ═══
Birth Data: 1990-06-15 10:30  (+5.5) 28.61°N 77.21°E
Lagna: Gemini   Sun: Taurus   Moon: Leo

── Static Analysis ──
[✓] D1 Rashi Chart
[✓] D9 Navamsa
[✓] D10 Dasamsa
[✓] Vimshottari Dasha (120 years)
[✓] Ashtakavarga (8-point system)
[✓] Shadbala (partial — internal invariants pass, external calibration pending)
[✓] Yogas & Doshas
[✓] Argala (planetary interventions)
[✓] Nakshatra Advanced (Chandra Bala / Tara Bala)

── Dynamic Timing ──
[✓] Vimshottari Dasha breakdown
[✓] Dasha Sandhi detection
[✓] Transit (true positions)
[✓] Double Transit analysis
[✓] Narayana Dasha
[✓] Solar Return / Varshaphala (partial)
[✓] Nakshatra Dasha (Ashtottari)

── Technique Audit Table ──
✓ Vimshottari Dasha        covered      high confidence
✓ Ashtakavarga             covered      high confidence
🔶 Shadbala                partial      internal consistent, external calibration pending
🔶 Chara Dasha             partial      simplified (24% match with PyJHora KN Rao)
❌ KP Sub-Lord              missing      not yet implemented

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Core Workflow

Three Input Paths

Path	Input	Behavior
A: Precise birth data	Date + time + coordinates	Full full-reading engine
B: PDF / text chart	Scanned chart or description	Extract → Quality Gate → route to A
C: Uncertain birth time	"Don't know my birth time"	Interactive birth time rectification

Eight-Stage Pipeline

Stage -1: Question-type routing (career / relationship / wealth / timing)
Stage 0:  Input routing (A / B / C)
Stage 1:  (B only) PDF extraction + Quality Gate
Stage 2:  Intent recognition → target house routing
Stage 3:  Static analysis (10 steps)
Stage 4:  Dynamic timing (7 steps)
Stage 5:  Timing output (5-layer verification)
Stage 6:  Remedial measures (optional)
Stage 7:  Modern language packaging
Stage 8:  Technique Audit Table (mandatory)

Strict Workflow Router (references/strict-workflow-router.md):

• Career questions → career-timing-strict
• Relationship questions → relationship-timing-strict
• Wealth questions → wealth-timing-strict
• Event timing → event-timing-strict
• Historical verification → event-verification-strict

The AI does NOT require the user to name techniques (e.g., "Chara Dasha"). It auto-selects based on question type.

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Technique Coverage

Current count: 44 techniques (26 covered, 18 partial, 0 missing)

Technique	Status	Notes
D1 Rashi Chart	✅ covered	Swiss Eph base
D9 Navamsa	✅ covered
D10 Dasamsa	✅ covered
Vimshottari Dasha	✅ covered
Dasha Sandhi	✅ covered
Ashtakavarga	✅ covered	BPHS/PVR calibrated
Argala	✅ covered
Vargottama	✅ covered
Pushkara	✅ covered
A10 / Karma Pada	✅ covered
UL / Upapada	✅ covered
Transit (true positions)	✅ covered
Double Transit	✅ covered
Nakshatra Advanced	🔶 partial	Engineering usable, needs more external benchmarking
Narayana Dasha	🔶 partial	Usable, needs traditional benchmark
Solar Return / Varshaphala	🔶 partial	Usable, some degradation logic present
Shadbala	🔶 partial	1200/1200 internal invariants pass; external absolute calibration NOT yet done
Chara Dasha	🔶 partial	~24% match with PyJHora KN Rao method; do NOT use for high-confidence timing
KP Sub-Lord	🔶 partial	Simplified 9-equal division; not full KP
Bhava Chalit	🔶 partial	Whole-sign adapter present; not full cusp-based reassignment
Sudarshana Chakra	🔶 partial	D1×D9×D10 triangle verification; not traditional full implementation
Tajika Yogas	🔶 partial	Simplified rules
Raj Yoga	🔶 partial	Classic combinations covered; not all variants
Dhana Yoga	🔶 partial
Pancha Mahapurusha	🔶 partial
Neecha Bhanga	🔶 partial
Sade Sati	🔶 partial	Simplified model
Tithi Lord	🔶 partial
Pancha Pakshi	🔶 partial
Rashi Tulya Navamsa	🔶 partial
Trimshamsa D30	🔶 partial
Marriage Counting	🔶 partial	Bhrigu Pada approximation
Prashna Integration	🔶 partial	Not fully integrated into full-reading
Bhrigu Pada Dasha	🔶 partial	Generic approximation
Muhurta	🔶 partial	Panchanga elements present

Legend:

• ✅ covered — implemented and benchmarked against authoritative sources
• 🔶 partial — implemented but NOT fully benchmarked; suitable for auxiliary reference only
• ❌ missing — not yet implemented

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Why This Exists (Competitive Context)

The Landscape

Project	Type	Strength	Weakness
PyJHora	Calculation library	Strongest traditional algorithm coverage (50+ Dashas, 284 Yogas)	No interpretive pipeline; user must interpret results themselves
VedAstro	API / Web platform	200+ endpoints, Docker, MCP Server, MIT license	Interpretive audit & confidence labeling weaker
Maitreya	Desktop software	Mature cross-platform GUI	Jyotish depth not as deep as specialized projects
jyotisha	Panchanga / calendar	Excellent Panchanga accuracy	Not a full reading system
This project	AI-native analysis system	Full pipeline + audit + degradation	Pure calculation accuracy still being benchmarked

Our Position

PyJHora is the calculator. VedAstro is the API platform. Maitreya is the desktop software. This project is the "AI-native Jyotish research analyst."

We are NOT trying to out-calculate PyJHora (it has years of lead). Our value is in:

1. Organizing calculations into a reproducible interpretive workflow
2. Auditing every technique used and declaring confidence
3. Degrading gracefully — partial techniques are labeled, not silently over-promising
4. Being AI-native — designed for integration with LLM-based analysis

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Honest Assessment

We believe in transparency about limitations. This is NOT a "99% accurate" system, and anyone claiming that about Jyotish is over-selling.

Current Accuracy Estimates (self-evaluated)

Dimension	Score	Notes
Astronomical foundation (Swiss Eph)	8.5/10	Depends on ayanamsa, node mode, house system
Traditional algorithm accuracy	7.3/10	Chara Dasha & Shadbala need external calibration
Technique coverage breadth	8.6/10	44 techniques, broad but not deepest
Reading detail depth	9.6/10	Possibly best among open-source projects
Prediction workflow rigor	8.8/10	Strict routing + audit table
Verification system	8.2/10	Has registry, benchmark, degradation; some verification still internal
Engineering maturity	6.8/10	Functional, but not productized (no Docker / PyPI / CI yet)
Open-source influence	5.5/10	Currently more of a "private high-density toolkit"

What "Partial" Means (Important)

When a technique is labeled partial:

• It CAN produce output
• The output has NOT been benchmarked against PyJHora / JHora / canonical texts
• It should be used as auxiliary reference only
• It must NOT be the sole basis for high-confidence predictions

Examples:

• Shadbala (partial): Internal invariants pass (1200/1200). External absolute values NOT calibrated. Use for relative strength ranking only.
• Chara Dasha (partial): ~24% match with PyJHora KN Rao method. Do NOT use for precise timing.

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Project Status

Current version: v6.1.10-dk-rtn-theme-bridge

Recently Completed

• v6.1.10 — Darakaraka deep reader wired into full-reading.modules.jaimini.darakaraka; thematic reports now consume real DK and Rashi Tulya Navamsa evidence.
• v6.1.9 — Public/sanitized benchmark suite, competitive research, coverage roadmap and PDF validation methodology added.
• v6.1.8 — Yoga validation reached F1=95.22% (FP=36, FN=63); thematic reports consume real full-reading.modules evidence.
• v6.1.6 — Five-system Dasha convergence wired into full-reading (Vimshottari + Chara + Yogini + Ashtottari + Kalachakra).
• v6.0.11 — Shadbala internal invariant validation (1200/1200 pass); downgraded to partial.

Actively Working On (P0)

1. Chara Dasha rewrite — align with PyJHora KN Rao method (target: ≥95% match)
2. Shadbala external calibration — align with JHora / PyJHora / BV Raman
3. Darakaraka / RTN external benchmark — v6.1.10 has wired the modules; next step is traditional case validation and evidence ranking
4. KP Sub-Lord full implementation — unequal Vimshottari subdivisions
5. Bhava Chalit complete — cusp-based planet reassignment
6. Sudarshana Chakra traditional — Sun/Moon/Lagna reference points

Next (P1)

• Benchmark harness (PyJHora output comparison)
• English documentation completion
• Docker image
• MCP Server endpoint
• GitHub Actions CI

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Development

Running the Test Suite

# Syntax check all scripts
python3 -m py_compile scripts/*.py

# Capability audit (must pass with 0 problems, 0 warnings)
python3 scripts/audit_capabilities.py --mode validate

# Full-reading regression test (use FICTIONAL data only)
python3 scripts/jyotish_engine.py full-reading \
  --year 1990 --month 6 --day 15 \
  --hour 10 --minute 30 \
  --lat 39.9042 --lon 116.4074 --tz 8 \
  --age 36 \
  --transit-date 2026-06-04

Important Rules

1. NEVER put real user birth data into skill files, tests, CHANGELOG, or public repos
2. Use only: (a) public AA-rated celebrity data, (b) explicitly fictional smoke tests, (c) current-session data (never persisted)
3. Always run git status --short --branch before starting work
4. Always run py_compile + audit_capabilities.py + full-reading regression after modifications
5. Do NOT upgrade partial to covered without external benchmark evidence
6. Do NOT refactor arbitrarily; make minimal verifiable changes

Directory Structure

jyotish-vedic-astrology/
├── SKILL.md                          # Core entry point (Chinese)
├── README.md                         # This file (English)
├── CHANGELOG.md                     # Version history
├── requirements.txt                  # Python dependencies
├── references/
│   ├── technique_registry.json      # Machine-readable technique registry
│   ├── strict-workflow-router.md   # Question-type routing rules
│   ├── quick-reference-guide.md    # Quick reference
│   └── ...                         # Knowledge reference docs
├── scripts/
│   ├── jyotish_engine.py           # Main engine entry point
│   ├── audit_capabilities.py       # Capability audit tool
│   ├── shadbala.py                # Shadbala implementation
│   ├── dasha_calculator.py        # Dasha calculations
│   └── ...                        # 30+ technique scripts
└── tests/                          # Test cases

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Contributing

We welcome contributions, especially:

1. Benchmark data — PyJHora / JHora output comparisons for specific techniques
2. Traditional text verification — checking technique implementations against BPHS, PVN Rao, BV Raman
3. Documentation — English docs, tutorials, example outputs
4. Engineering — Docker, CI, MCP Server, API layer
5. Test cases — fictional birth data with expected outputs

How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b fix/chara-dasha-benchmark)
3. Make your changes (follow the development rules above)
4. Run the full test suite
5. Commit with a clear message
6. Push and create a Pull Request

Philosophy

We prioritize truth over coverage. It is better to have 10 well-benchmarked techniques than 50 poorly-implemented ones. If you contribute a technique, please include:

• The source text / authority it is based on
• Benchmark comparison data (if available)
• Honest assessment of limitations

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License

MIT License — see LICENSE for details.

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Acknowledgments

• Swiss Ephemeris — astronomical calculation foundation
• PyJHora (naturalstupid/PyJHora) — benchmark reference for traditional algorithms
• VedAstro (VedAstro/VedAstro) — engineering and productization reference
• BPHS (Brihat Parashara Hora Shastra) — canonical text
• PVN Rao / KN Rao — traditional Jyotish teaching lineage

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Contact & Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Final note: This system is a research tool. It should NOT be used for making life-altering decisions without consulting qualified human astrologers. The techniques implemented here are complex and context-dependent; software output always benefits from human judgment.