System Card
System Card v2.0 AI-Assisted Astrology PlatformVersion 2.0 · · Governance 2.0 public evidence surface
Governance 2.0 Overview
This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.
Governance Summary
This page is the canonical public system disclosure for platform behavior, model/provider boundaries, and evidence-backed governance.
Scope
System behavior, AI assistance boundaries, public claims, governance metrics, provider alignment, and release integrity.
Implementation Status
Version 2.0 is the source structure used to align the other public governance pages.
This System Card extends the Model Card and describes starnum.com.tw overall platform capabilities, knowledge-base infrastructure, AI-assisted pipelines, quality-assurance mechanisms, and known limitations.
1. System Overview
| Platform Name | Starnum Platform |
| Version | v5.0 (2026-04-12) |
| Core Features | Zi Wei Dou Shu chart analysis, Numerology (Life Path) calculation, astrology knowledge-base articles |
| Target Audience | Traditional Eastern metaphysics enthusiasts; Traditional Chinese and multilingual users |
| Knowledge System | Zi Wei: Lu Binzhao school (Zhongzhou) San-He + Si-Hua; Numerology: Pythagorean system |
| Charting Engine | iztro v3.x (open source, GitHub-verifiable) |
| AI Execution | Governed, task-specific agent routes with bounded inputs, verification, and fail-closed release gates |
| Language Support | 10 languages (zh-TW / zh-CN / en / ja / ko / es / vi / th / ms / id) |
| Deployment Infrastructure | Cloudflare Worker Assets + Workers, R2 object storage, and D1 indexes |
2. Knowledge-Base Infrastructure
2.1 Scale
| Zi Wei Dou Shu KB | 225+ files, 88 indexes, 8-volume structure (01-fundamentals ~ 09-advanced) |
| Numerology KB | 141 files ~56,544 lines |
| Total KB Scale | 2,758 files ~1,537,711+ lines of structured text |
| Vector Index | Large-scale semantic vector database (Qdrant) |
| Knowledge Graph | Knowledge graph (Neo4j, stars × palaces × Si-Hua) |
2.2 Knowledge Quality Grading
- Grade A: Primary sources (Lu Binzhao school / Pythagorean) — directly citable
- Grade B: iztro open-source engine / multi-source public web data (distilled) — citable with cross-verification recommended
- Grade C: Forums / blogs — only citable with Grade A/B cross-verification
2.3 School-Integrity Mechanism
Si-Hua viewpoints not belonging to the Lu Binzhao school are labeled with ⚠️ Non-Lu Binzhao school. The knowledge base maintains a blocklist preventing re-crawl of sources flagged for prompt injection.
3. AI-Assisted Pipelines
3.1 Chart Plain-Language Analysis Pipeline
3.2 Article Production Pipeline
4. Quality-Assurance Mechanisms
4.1 Six-Layer Closed-Loop Quality Model
- L1 Output Layer: 232 validations per article; failures block publication
- L2 Error Layer: failure log → ≥3 recurrences auto-propose a new rule
- L3 Performance Layer: GA4 + Cloudflare traffic feedback identifies articles needing refresh
- L4 Meta-Rule Layer: rule-audit.js audits the rule system itself, retiring rules untriggered for 90 days
- L5 Environment Layer: security headers, dependency vulnerabilities, credential expiry monitored automatically
- L6 Strategy Layer: quarterly strategic review benchmarked against the competitive landscape
4.2 Key Quality Metrics
| Astrology Hard Rules | 117 (41 Four Transformations rules + 49 chart-pattern rules + 17 core-metric rules + 10 meta-rules) |
| Editorial Skill Modules | 19 skill packs (prompts/skills/) |
| Terminology Dictionary | 221 terms × 9 languages (glossary.json) |
| Case Library | 435 anonymized + verified astrology cases |
| Automated Quality Validation | 232-item checklist (sop-checklist.md) |
5. Limitations & Known Boundaries
5.1 Knowledge Limitations
- Only the Lu Binzhao school is adopted; other schools (Wang Tingzhi, Zheng Muting, etc.) are not fully covered
- Numerology only computes numbers derivable from birth date; name-based numerology systems are not supported
- Annual-forecast content expires over time; the knowledge base does not store predictions containing specific years
- Interpersonal synastry (relationship-compatibility analysis) is a planned feature and not yet launched
5.2 Known Risks of AI Assistance
- Hallucination Risk: mitigated via 117 hard rules + KB citation tagging, but not fully eliminated
- School Bias: training data leans toward the Lu Binzhao school; other-school viewpoints are systematically labeled but not deeply covered
- Language Quality: 10 languages use AI-assisted translation with language-specific review; quality is monitored and may vary by release
- Timeliness: the AI model knowledge cutoff may trail the latest astrology research; manual KB updates compensate
6. Governance & Updates
| Human Supervisor | mychenan (site owner, astrology researcher) |
| AI Collaborators | Claude Sonnet 4.5 × 20 (Anthropic) |
| Independent Verification | Risk-appropriate machine checks and a separate verifier review bounded artifacts before release |
| Update Cycle | System Card updated quarterly; major capability changes trigger immediate updates |
| Public Documents | Methodology / Model Card / Benchmark / Ethics Statement |
| Governance Documents | Ethics Statement / Transparency Log |
Contact: Instagram @mychenan
External standards and primary sources
These primary sources inform this page. They are benchmarks, not third-party endorsements of this site.
Current Machine Audit Snapshot
This block uses only traceable local audit data. No unsupported metrics or model claims are added.
- data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
- data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
- data/discovery-surface-audit.json: 0 errors, 0 warnings.
- data/sla-report.json: critical / 5 critical, 0 warnings.
Content Maintenance And Update Decision
This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.
Update Decision
This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.
Human Boundary
Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.
Verification Command
node scripts/verify-trust-pages.js --check
data/state-machine/model-card.jsondata/state-machine/public-bench.jsondata/public-claim-registry.json- Related governance pages: Data Governance · Transparency Report · Research · model-card · methodology
- Update flow:
npm run update:trust-pages→npm run test:trust
Verifiable Evidence Layer
This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.
| Claim ID | Verifiable value | Status | Owner | Source and verification |
|---|---|---|---|---|
| claim.public-url-manifest.indexable-count Public URL and canonical inventory |
38,965 indexable URLs | verified | sitewide | node scripts/generate-public-evidence-manifest.js --dry |
| claim.trust-pages.audit-pass-rate Trust page machine audit |
180/180 pass | verified | sitewide | node scripts/verify-trust-pages.js --check |
| claim.discovery-surface.zero-errors AI discovery surface audit |
{"errors":0,"warnings":0} | verified | sitewide | node scripts/verify-discovery-surface.js |
| claim.structured-data.jsonld-errors JSON-LD / structured data audit |
{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406} | verified | sitewide | node scripts/site-machine-audit.js |
| claim.status.sla-state Status page SLA source |
critical / 5 critical, 0 warnings | verified | sitewide | node scripts/generate-status-page.js |
| claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment |
benchmark alignment only unless code/config evidence exists | verified | sitewide | node scripts/verify-public-evidence.js --check |
| claim.transparency-report.sha256 Transparency report SHA-256 anchor |
{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"} | verified | sitewide | node scripts/update-transparency-current-data.js |
| claim.release-integrity.gpg-signing GPG signing status |
GPG signing configured locally; GitHub verification pending | github_verification_pending | sitewide | gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature |
| claim.system-card.production-model-evidence Production model setting source scan |
{"productionModelEvidence":[]} | verified | system-card | node scripts/verify-public-evidence.js --check |
| claim.system-card.quality-gates System card machine quality gates |
{"trustPages":{"pages":180,"pass":180,"fail":0},"discovery":{"errors":0,"warnings":0},"structuredData":{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506 | verified | system-card | node scripts/verify-trust-pages.js --check && node scripts/verify-discovery-surface.js |
| claim.system-card.kb-tm-lineage KB / TM source lineage |
{"kb":{"source_files":3238,"chunks":32724,"chunked_source_files":3238,"excluded_source_files":0,"missing_chunk_coverage":0,"orphan_chunks":0},"tm":{"total_entries":789031,"counts": | verified | system-card | node scripts/verify-public-evidence.js --check |
| claim.system-card.provider-benchmark-scope Provider benchmark scope separated from production usage |
{"benchmarkProviders":["OpenAI","Anthropic","Google Gemini"],"productionModelEvidence":[]} | verified | system-card | node scripts/verify-public-evidence.js --check |
System Card V2.0: Technical Transparency Layer
This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.
Public architecture
Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.
AI-use disclosure
AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.
Quality and safety gates
Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.
Data traceability
Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.
| Governance area | OpenAI | Anthropic | Google Gemini | Starnum implementation evidence |
|---|---|---|---|---|
| Model/system-card disclosure | OpenAI models + safety docs | Claude model docs + system/model cards | Gemini model docs + safety settings | system-card, model-card, methodology, benchmark, transparency-log |
| Safety evaluation and use boundaries | Safety best practices / deployment checklist | Responsible Scaling / safety policy | Gemini safety controls / policy | AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit |
| Data governance | Data controls / privacy controls | privacy and data handling docs | Gemini API data governance references | privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes |
| Monitoring and release | production checklist / eval discipline | system-card transparency discipline | model/version documentation discipline | deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits |
- Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
- Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
- Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
- Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.
The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.
Release Integrity And GPG
GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.
OpenAI / Anthropic / Google Gemini Alignment
The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30
| Provider | Governance focus | Starnum disclosure | Official source |
|---|---|---|---|
| OpenAI | Model documentation, latest model notes, safety best practices, and data controls. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://platform.openai.com/docs/models |
| Anthropic | Claude model documentation, system/model cards, Responsible Scaling, and safety policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://docs.anthropic.com/en/docs/about-claude/models |
| Google Gemini | Gemini API model documentation, safety settings, data governance, and platform policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://ai.google.dev/gemini-api/docs/models |