| name | text-provenance |
| description | Use when you need to identify the likely source of a text passage, attribute text to documents in a RAG system, detect plagiarism, or match contract clauses to their origin. |
| author | Yu Chou Teo |
| jurisdiction | SG |
| version | 1.0.0 |
| last_reviewed | 2026-05 |
| tags | ["text-analysis","rag","citation","provenance","similarity","attribution"] |
text-provenance
When to Use
- RAG citation highlighting — show which source document a generated text came from
- Contract playbook matching — find which standard clause a contract clause derives from
- Plagiarism detection
- Source attribution for AI-generated legal text
- Any text provenance task where you need to trace text back to its origin
How It Works
Core Approach
Lightweight text similarity metrics — no embeddings or API calls at runtime. Fast, deterministic string matching that works in-browser or server-side.
Comparison Methods
- Surface similarity — character-level comparison
- N-gram overlap — phrase-level matching
- Fingerprint matching — exact phrase detection
Usage
import { findProvenance } from 'text-provenance';
const sources = await findProvenance(
"the quick brown fox jumps over the lazy dog",
corpusDocuments
);
Edge Cases
Works where embeddings fail:
- Short text snippets
- Exact phrase matching
- High-precision attribution tasks
- Privacy-sensitive contexts (no data leaves the machine)
Audience and Work Shape
Audience: developers and lawyers building RAG-citation, contract-derivation, or plagiarism-detection workflows. The output is a candidate ranking, not a finding.
Work shape: Pattern-Matched Review. Lexical similarity is the matching function; the user decides what counts as a match.
Scope and Legal Use
This skill provides legal support, not legal advice. The output is a ranked list of candidate sources with similarity scores — never an attribution conclusion, never a plagiarism finding, never a contract-derivation determination of legal effect.
Privilege and confidentiality. Runs client-side with no network calls. No text leaves the user's machine unless the calling application chooses to transmit it. Skill itself does not create new privilege exposure.
Accountability. A qualified lawyer must review and accept any output before relying on it for an attribution, plagiarism, or contract-derivation decision. The similarity score is a signal, not a verdict.
Confidence Bands
Map the raw similarity score to action bands:
- High (≥ 0.85) — strong lexical match; lawyer can rely on this as a likely source but must still verify the underlying passage.
- Medium (0.55 – 0.85) — possible match; route to human comparison before relying.
- Low (< 0.55) — weak signal; do not treat as a source attribution. Show alongside other candidates but do not single out.
Out of Scope
- Not a plagiarism adjudication or finding of academic/professional misconduct.
- Not legal advice on copyright, IP, or contract-derivation claims.
- Not evidentiary; results are not formatted for proceedings.
- Not a semantic search — lexically different paraphrases will be missed.
- Not a substitute for embedding-based retrieval where semantic match is required.
Escalation
Stop and route to the responsible lawyer when:
- the top result is in the Low band but the calling workflow expects a definitive source;
- the use case is disciplinary, evidentiary, or potentially defamatory (e.g., flagging a lawyer or student for plagiarism);
- two or more candidates score within 0.05 of each other and the workflow requires a single source attribution.
Limitations
- Cannot handle semantically similar but lexically different text
- Works best with longer source documents
- Not a substitute for embedding-based retrieval in all contexts
