tomevault-io Linear A Decipherment
Computational pipeline for analyzing Linear A inscriptions against Semitic roots, formalizing Cyrus H. Gordon's five-step decipherment methodology. Built on data from lashon-ha-kretan (1,701 inscriptions, 60 Gordon readings, 2,871 Proto-Semitic roots).
Base directory: ~/.claude/skills/linear-a-decipherment
Scholarly Disclaimer
All readings are hypothetical. Linear A remains officially undeciphered. Gordon's Semitic hypothesis is one of several competing frameworks. Include this disclaimer on every analytical output.
Confidence Taxonomy
Every proposed reading must be tagged with a confidence level:
| Level | Criteria | Example |
|---|---|---|
| CONFIRMED | Ideographic + phonetic + mathematical confirmation | KU-NI-SU (emmer wheat) |
| PROBABLE | Direct Gordon reading + external attestation | DA-KU-SE-NE (Hurrian name at Nuzi) |
| CANDIDATE | Gordon reading or strong Proto-Semitic match (d < 0.3) | New cognate from distance search |
| SPECULATIVE | Weak phonetic match or single-source evidence | Proto-Semitic match with d > 0.5 |
Reference File Protocol
Route questions to the right reference before answering:
Question about a specific reading or word?
→ Read references/gordon-lexicon.md
→ Run: uv run scripts/cognate_search.py "WORD"
Question about methodology or approach?
→ Read references/methodology.md
Question about sign values or the syllabary?
→ Read references/sign-values.md
Question about ML/computational approaches?
→ Read references/ml-approaches.md
Question about a specific inscription?
→ Run: uv run scripts/analyze.py single INSCRIPTION_NAME
Question about corpus statistics?
→ Run: uv run scripts/sign_analysis.py SUBCOMMAND
Data Dependencies
Source data from lashon-ha-kretan:
| File | Path | Contents |
|---|---|---|
| Inscriptions | ~/Desktop/Programming/lashon-ha-kretan/LinearAInscriptions.js |
~1,701 GORILA inscriptions |
| Lexicon | ~/Desktop/Programming/lashon-ha-kretan/semiticLexicon.js |
60 Gordon + 3 YasharMana + 7 scholarly readings |
| Proto-Semitic | ~/Desktop/Programming/lashon-ha-kretan/etymology/Semitic.json |
2,871 roots |
Extracted data cached in data/ (generated by corpus_extract.py --all):
data/corpus.json— Structured inscriptionsdata/gordon.json— Gordon + YasharMana lexicondata/semitic_roots.json— Proto-Semitic rootsdata/cognate_cache.json— Precomputed cognate scores (built bycognate_search.py --build-cache)
If data/ files are missing, run extraction first:
uv run ~/.claude/skills/linear-a-decipherment/scripts/corpus_extract.py --all
Workflows
1. Analyze a Single Inscription
Runs Gordon's 5-step pipeline on one inscription:
# Human-readable report
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py single HT88
# JSON output
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py single HT88 --format json
Steps performed: transliteration extraction, segmentation, consonantal skeleton for each word, cognate search (Gordon → YasharMana → Proto-Semitic cache), coverage summary.
2. Search Cognates for a Word
Find Semitic cognates for any Linear A transliteration:
# Full search with table output
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py "KI-RE-TA"
# Skeleton extraction only
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py "KI-RE-TA" --skeleton
# JSON with top 10 matches
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py "KI-RE-TA" --top 10 --format json
# Skip cache for live Proto-Semitic search
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py "KI-RE-TA" --no-cache
Pipeline: transliteration → skeleton (k-r-t) → Gordon direct → YasharMana → Proto-Semitic distance.
3. Find Unknown Words (Discovery Mode)
Identify frequently-occurring words with no known reading—best targets for new cognate proposals:
# Top 20 unknown words appearing 3+ times
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py batch --mode unknowns
# More restrictive: top 10 appearing 5+ times
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py batch --mode unknowns --min-count 5 --top 10
4. Find Promising Inscriptions
Inscriptions with the highest ratio of identified words—best for study:
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py batch --mode promising --top 15
5. Compare Libation Formulas
Group inscriptions containing the libation formula (JA-SA-SA-RA-ME pattern):
# List all libation inscriptions
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py batch --mode libation
# With skeleton alignment
uv run ~/.claude/skills/linear-a-decipherment/scripts/analyze.py batch --mode libation --alignment
6. Corpus Statistics
Statistical analysis of sign patterns:
# Sign frequency (top 30)
uv run ~/.claude/skills/linear-a-decipherment/scripts/sign_analysis.py frequency
# Word frequency with hapax legomena count
uv run ~/.claude/skills/linear-a-decipherment/scripts/sign_analysis.py words
# Sign co-occurrence within words
uv run ~/.claude/skills/linear-a-decipherment/scripts/sign_analysis.py cooccurrence --signs KI,RO,SA
# Positional distribution (initial/medial/final)
uv run ~/.claude/skills/linear-a-decipherment/scripts/sign_analysis.py position
# Site distribution (HT, ZA, PK, etc.)
uv run ~/.claude/skills/linear-a-decipherment/scripts/sign_analysis.py distribution
# JSON output for any subcommand
uv run ~/.claude/skills/linear-a-decipherment/scripts/sign_analysis.py frequency --format json
7. Generate Training Data
Prepare JSONL for ML fine-tuning:
# Preview first 3 entries
uv run ~/.claude/skills/linear-a-decipherment/scripts/finetune_prep.py gordon-pairs --preview 3
# Generate full JSONL
uv run ~/.claude/skills/linear-a-decipherment/scripts/finetune_prep.py gordon-pairs --output data/gordon_pairs.jsonl
v1 produces 63 chat-format pairs (Gordon + YasharMana). See references/ml-approaches.md for v2 augmentation strategy.
8. Reverse Root Search (Semitic Root → Corpus Words)
Given a Semitic consonantal root, find all Linear A words in the corpus whose skeletons match:
# Find corpus words matching root KNS (e.g., kiništu "gathering place")
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py --reverse kns
# Broader search with higher distance tolerance
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py --reverse kns --max-dist 0.5 -n 30
# JSON output for programmatic use
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py --reverse thm --format json
# Search for Baal-related words (b-'-l root)
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py --reverse bl
# Search for "give" root (y-t-n)
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py --reverse ytn
Pipeline: root consonants → weighted Levenshtein against all corpus word skeletons → ranked by distance, annotated with Gordon/YasharMana readings, occurrence counts, sites, and inscriptions.
9. Extract / Rebuild Corpus
Extract structured data from JS source files:
# Extract everything (inscriptions + lexicons + Proto-Semitic roots)
uv run ~/.claude/skills/linear-a-decipherment/scripts/corpus_extract.py --all
# Inscriptions only, filtered by site
uv run ~/.claude/skills/linear-a-decipherment/scripts/corpus_extract.py --site HT
# Include Gordon lexicon
uv run ~/.claude/skills/linear-a-decipherment/scripts/corpus_extract.py --with-gordon
# Build cognate cache (takes ~10 seconds)
uv run ~/.claude/skills/linear-a-decipherment/scripts/cognate_search.py --build-cache
Integration with Other Skills
| Skill | Usage |
|---|---|
rlama |
Create gordon-dossiers RAG collection from ~/Desktop/minoanmystery-astro/souls/minoan/dossiers/scholarly-sources/gordon/ |
ancient-near-east-research |
Sefaria for Hebrew cognate verification, CDLI for Akkadian parallels |
exa-search |
Search recent computational decipherment papers |
llama-cpp |
Local inference with fine-tuned decipherment models (v2) |
Architecture
~/.claude/skills/linear-a-decipherment/
├── SKILL.md # This file
├── lib/ # Shared Python library
│ ├── __init__.py
│ ├── types.py # Frozen dataclasses (Inscription, LexiconEntry, CognateMatch)
│ ├── js_parser.py # JS Map → Python dict extraction
│ ├── normalization.py # normalize(), lookup_in(), J/Y swap
│ ├── skeleton.py # SIGN_DECOMPOSITION, extract_skeleton()
│ └── phonetics.py # SEMITIC_DISTANCES, weighted_levenshtein()
├── scripts/
│ ├── corpus_extract.py # JS → JSON extraction
│ ├── cognate_search.py # Forward + reverse cognate search + cache builder
│ ├── sign_analysis.py # Corpus-wide sign statistics
│ ├── analyze.py # Gordon 5-step pipeline (single + batch)
│ └── finetune_prep.py # ML training data generation
├── references/
│ ├── gordon-lexicon.md # Complete 60+3+7 entry lexicon tables
│ ├── methodology.md # Gordon's methods, 5-step pipeline
│ ├── sign-values.md # Sign confidence levels (HIGH/MEDIUM/LOW)
│ └── ml-approaches.md # Computational decipherment survey (v2)
└── data/ # Generated (not committed)
├── corpus.json # 1,701 inscriptions
├── gordon.json # 60 Gordon + 3 YasharMana + 7 scholarly entries
├── semitic_roots.json # 2,871 Proto-Semitic roots
└── cognate_cache.json # Precomputed cognate scores
All scripts use uv run with PEP 723 inline metadata. Dependencies: stdlib only.
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