feat(docs): add wiki stub system for 300 star systems
Generated wiki stubs at docs/wiki/systems/{GJ-id}/index.md for all
300 systems. Each stub has YAML frontmatter (54 machine-readable
columns) + markdown body (5 authored text sections) + derived fields.
13 columns pre-filled from star-map.json (identity, gate infrastructure,
location, wave, earth_proximity). 55 columns await authoring.
Includes:
- tooling/wiki/generate-stubs.py — template renderer, idempotent
- tooling/wiki/extract-csv.py — wiki→CSV extractor with fill rate report
- docs/design/star-systems.csv — initial CSV at 19.3% completion
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.6
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#!/usr/bin/env python3
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"""
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extract-csv.py
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Extracts all wiki system data into a single CSV file.
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Reads: docs/wiki/systems/*/index.md (YAML frontmatter + markdown body sections)
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Writes: docs/design/star-systems.csv
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Body sections (## headers) are mapped to columns:
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Supply Dependency -> supply_dependency
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Faction Notes -> faction_notes
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Silence Topic -> silence_topic
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Narrative Hook -> narrative_hook
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Calibration Note -> calibration_note
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"""
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import csv
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import re
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from pathlib import Path
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WIKI_DIR = Path(__file__).parent.parent.parent / "docs/wiki/systems"
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OUTPUT_CSV = Path(__file__).parent.parent.parent / "docs/design/star-systems.csv"
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# CSV column order — matches systems-framework.md section order
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COLUMNS = [
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# I. Identity and Location
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"system_id", "astronomical_id", "proper_name", "system_name", "star_type",
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"geographic_sector", "geographic_band", "political_zone",
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# II. Physical Character
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"habitable_planet_count", "inhabited_planet_count", "asteroid_belt",
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"gas_giant", "habitability_profile",
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# III. Gate Infrastructure
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"horizon_station", "aperture_count", "gate_connections", "gate_topology",
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"span_gate_network",
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# IV. Settlement History
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"settlement_wave", "founding_motivation", "founding_culture_primary",
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"founding_culture_secondary", "cultural_persistence", "historical_event",
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"historical_event_age_years", "religious_status", "religious_generation_count",
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# V. Economic Life
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"economic_tier", "population", "economic_base_primary",
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"economic_base_secondary", "distribution_index", "imprint_access",
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"supply_dependency",
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# VI. Governance
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"governance_type", "dominant_faction", "primary_fault_line",
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"secondary_fault_line",
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# VII. Faction Presence
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"assembly_presence", "commission_presence", "syndic_presence", "syndic_type",
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"separatist_presence", "separatist_type", "institute_presence",
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"guardians_presence", "faction_notes",
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# VIII. Cultural Voice
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"cultural_register", "cultural_register_secondary", "ambient_anxiety",
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"local_pride", "atmospheric_tone", "atmospheric_tone_secondary",
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"active_situation", "silence_threshold", "silence_topic",
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# IX. Political Character
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"earth_alignment", "earth_proximity", "earth_tension",
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# X. Narrative Profile
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"primary_archetype", "secondary_archetype", "narrative_notable",
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"narrative_hook", "generation_priority",
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# XI. Content Notes
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"stability_index", "system_volatility", "cultural_corridor",
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"calibration_note",
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]
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# Map markdown section headers to column names
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BODY_SECTION_MAP = {
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"supply dependency": "supply_dependency",
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"faction notes": "faction_notes",
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"silence topic": "silence_topic",
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"narrative hook": "narrative_hook",
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"calibration note": "calibration_note",
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}
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def parse_frontmatter(text: str) -> dict[str, str]:
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"""Extract YAML frontmatter between --- delimiters, ignoring comments."""
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match = re.match(r"^---\n(.*?\n)---", text, re.DOTALL)
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if not match:
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return {}
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fm_text = match.group(1)
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result = {}
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for line in fm_text.split("\n"):
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line = line.strip()
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if not line or line.startswith("#"):
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continue
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if ":" not in line:
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continue
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key, _, value = line.partition(":")
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key = key.strip()
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value = value.strip()
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# Strip quotes
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if value.startswith('"') and value.endswith('"'):
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value = value[1:-1]
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elif value.startswith("'") and value.endswith("'"):
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value = value[1:-1]
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# Convert YAML null to empty string
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if value == "~" or value.lower() == "null" or value == "":
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value = ""
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result[key] = value
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return result
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def parse_body_sections(text: str) -> dict[str, str]:
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"""Extract content from ## sections in the markdown body."""
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# Find where frontmatter ends
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match = re.match(r"^---\n.*?\n---\n", text, re.DOTALL)
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if not match:
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body = text
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else:
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body = text[match.end():]
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sections: dict[str, str] = {}
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current_key: str | None = None
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current_lines: list[str] = []
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for line in body.split("\n"):
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if line.startswith("## "):
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# Save previous section
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if current_key is not None:
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sections[current_key] = "\n".join(current_lines).strip()
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header = line[3:].strip().lower()
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current_key = BODY_SECTION_MAP.get(header)
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current_lines = []
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elif current_key is not None:
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# Skip HTML comments and the Derived Fields section
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if line.strip().startswith("<!--") and line.strip().endswith("-->"):
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continue
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current_lines.append(line)
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# Save last section
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if current_key is not None:
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sections[current_key] = "\n".join(current_lines).strip()
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return sections
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def main():
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wiki_dirs = sorted(WIKI_DIR.iterdir())
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rows: list[dict[str, str]] = []
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errors: list[str] = []
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for d in wiki_dirs:
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if not d.is_dir():
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continue
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index_file = d / "index.md"
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if not index_file.exists():
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errors.append(f"Missing index.md in {d.name}")
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continue
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text = index_file.read_text()
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# Parse frontmatter
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fm = parse_frontmatter(text)
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# Parse body sections
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body = parse_body_sections(text)
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# Merge into row
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row: dict[str, str] = {}
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for col in COLUMNS:
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if col in fm:
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row[col] = fm[col]
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elif col in body:
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row[col] = body[col]
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else:
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row[col] = ""
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rows.append(row)
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# Sort by system_id
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rows.sort(key=lambda r: r.get("system_id", ""))
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# Write CSV
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OUTPUT_CSV.parent.mkdir(parents=True, exist_ok=True)
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with open(OUTPUT_CSV, "w", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=COLUMNS)
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writer.writeheader()
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writer.writerows(rows)
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# Summary
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non_empty_counts = {col: 0 for col in COLUMNS}
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for row in rows:
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for col in COLUMNS:
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if row.get(col, ""):
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non_empty_counts[col] += 1
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print(f"Extracted {len(rows)} systems to {OUTPUT_CSV}")
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if errors:
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print(f"\nErrors ({len(errors)}):")
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for e in errors:
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print(f" {e}")
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# Completion stats
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total_cells = len(rows) * len(COLUMNS)
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filled_cells = sum(non_empty_counts.values())
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print(f"\nCompletion: {filled_cells}/{total_cells} cells filled ({filled_cells/total_cells*100:.1f}%)")
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print(f"\nColumn fill rates:")
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for col in COLUMNS:
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count = non_empty_counts[col]
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pct = count / len(rows) * 100 if rows else 0
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bar = "█" * int(pct / 5) + "░" * (20 - int(pct / 5))
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status = "✓" if pct == 100 else " "
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print(f" {status} {col:35s} {count:3d}/{len(rows)} {bar} {pct:.0f}%")
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if __name__ == "__main__":
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main()
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