--- name: linear-a-decipherment description: "Analyze Linear A inscriptions computationally via Gordon's Semitic hypothesis — sign frequency, co-occurrence stats, consonantal skeleton extraction, Proto-Semitic root comparison, Gordon lexicon lookup, libation formula patterns, and ML training data prep. Triggers on Linear A, Minoan script, GORILA corpus, HT/ZA/PK tablets, Semitic cognates, sign values, Phaistos, Hagia Triada, lashon ha-kretan." --- # 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: ```bash 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: ```bash # 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: ```bash # 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: ```bash # 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: ```bash 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): ```bash # 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: ```bash # 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: ```bash # 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: ```bash # 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: ```bash # 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.