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 inscriptions
-
data/gordon.json — Gordon + YasharMana lexicon
-
data/semitic_roots.json — Proto-Semitic roots
-
data/cognate_cache.json — Precomputed cognate scores (built by cognate_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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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.