sw-vocab

name: sw-vocab description: Validate spiritwriter's own terminology in docs and AI-generated drafts against a canonical registry. Catches drift ("Entity Sense Signature" → "Entity Semantic Scoring"), invented terms ("SW-CAP"), and deferred-but-claimed terms ("trust epochs") before publication.

Skill: Terminology Validation

Run a terminology audit of any markdown/text document against spiritwriter's canonical vocabulary registry. Produces a list of issues for review.

This skill dogfoods spiritwriter.fabric.canonicalize — the same engine that powers Phalanx (entity resolution) and the audit findings registry — applied to our own vocabulary so AI-generated drafts can't silently drift terms past peer review.

When to Use

• Reviewing a draft doc, blog post, or spec written by an AI agent
• Pre-commit hook on docs/ or README.md
• Auditing a CFP submission or paper draft for accidental hallucinations
• Whenever you suspect terminology has drifted between conversations

Install

pip install -e /path/to/spiritwriter-core
python -m spiritwriter.sw_vocab.seed      # one-time: populate canonical terms DB

Concepts

Issue Types

Workflow

Step 1 — Validate a doc

From the CLI (the easy path):

# Single file
python -m spiritwriter.sw_vocab validate docs/some-draft.md

# Directory (recursive *.md walk)
python -m spiritwriter.sw_vocab validate docs/

# Custom failure policy (default: invented_term,deferred_term)
python -m spiritwriter.sw_vocab validate docs/ --fail-on invented_term,deferred_term,known_drift

Exit code is 0 if no failing issues, 1 if any. Other issue types (fuzzy_drift, unknown_term) are reported with --verbose but don't cause a non-zero exit unless listed in --fail-on.

From Python:

from spiritwriter.sw_vocab import load_registry, validate_doc

with load_registry() as registry:
    issues = validate_doc("docs/some-draft.md", registry)
    for i in issues:
        print(i)

Or, from a string:

from spiritwriter.sw_vocab import load_registry, validate_text

with load_registry() as registry:
    issues = validate_text(open("draft.md").read(), registry)

Step 2 — Validate a single candidate

When debugging or building a custom integration:

from spiritwriter.sw_vocab import load_registry, validate_candidate

with load_registry() as registry:
    issue = validate_candidate("Entity Semantic Scoring", registry)
# {'term': 'Entity Semantic Scoring', 'issue': 'known_drift',
#  'canonical': 'Entity Sense Signature', 'note': '...'}

Step 3 — Seed a new canonical term, or update an existing one

Edit spiritwriter/sw_vocab/data/canonical_terms.json:

{
  "term": "MyNewPrimitive",
  "category": "primitive",
  "definition": "Brief plain-English explanation.",
  "defined_in": "spiritwriter/path/to/file.py",
  "aliases": ["my new primitive", "MyNewPrim"]
}

Two cases:

• Adding a new canonical — just re-seed:

  python -m spiritwriter.sw_vocab seed
  

• Editing an existing term (new alias, updated definition, fixed defined_in, etc.) — re-seed with --force. A plain re-seed hits T1_EXACT on the term name and skips the metadata update; the CLI detects this and errors with a clear message pointing at --force, which wipes and rebuilds the DB:

  python -m spiritwriter.sw_vocab seed --force
  

Adding a brand-new alias to an existing term is still an edit — re-seed with --force.

Step 4 — Generate the prompt-ready term list

When prompting an AI agent to write spiritwriter docs, paste the canonical list into the system prompt so the agent uses approved terminology from the start:

from spiritwriter.sw_vocab import load_registry, canonical_term_list

with load_registry() as registry:
    print(canonical_term_list(registry))

load_registry() returns a CanonicalRegistry, which is a context manager. On Windows in particular, using with ensures SQLite's WAL and SHM sidecars get cleaned up so the parent directory can be deleted or moved cleanly afterwards.

Invented and deferred terms appear first in the listing, so the agent sees what NOT to write before what TO write.

What Gets Scanned

The validator scans text in two passes:

1. Bolded and inline-code candidates: anything matching **Foo** or `Foo` is treated as an explicit terminology marker. These are validated against the canonical registry.
2. Substring scan for invented + deferred terms: anywhere the blacklisted phrases appear in prose (case-insensitive, word-boundary matched), they get flagged. This catches drift in unmarked text.

Free-form prose that doesn't bold or code-format terms is otherwise NOT scanned — that produces too much noise. If you want every mention of a canonical term validated, mark it with ** or ` in your writing.

Categories in the Seed Data

Limits

• Plain-text only. PDF/DOCX/HTML need to be converted first.
• Markdown-aware, not Markdown-parsing. Code blocks are NOT excluded — code samples may flag false positives. Wrap them appropriately or use the markdown linter approach (extract code blocks first, validate the rest).
• No semantic understanding. "Entity Sense Signature" used correctly in prose and "Entity Sense Signature" used incorrectly both pass the validator. The validator catches drift in spelling, not in meaning.
• Not a substitute for human review. Final say is yours. The validator surfaces issues; it doesn't decide them.

Wire It to CI

# .github/workflows/docs-vocab.yml
- name: Validate docs vocabulary
  run: |
    python -m spiritwriter.sw_vocab seed
    python -m spiritwriter.sw_vocab validate docs/ --fail-on invented_term,deferred_term

Failing on invented_term and deferred_term is the conservative gate. Failing on fuzzy_drift / known_drift / unknown_term produces more noise — keep those as warnings until the registry is fully seeded with project-specific terminology.

Do not

• Seed invented or deferred terms without an explanation in the definition field — future readers need to know why a term is blacklisted.
• Add aliases that collide with another term's canonical name (the schema test will catch this).
• Treat this as a security boundary. It's a hygiene tool, not a gate.