License Compliance Audit

license-compliance-audit

Gate G0 (road-to-provenance-and-license-governance, Phase 0) measured both candidate similarity detectors against a frozen golden corpus and neither met the pre-registered bar for a CI gate. Council resolved (2026-07-28, Option A): no lint_code_provenance.ts in any form — not even advisory. This skill is the result of that decision: the scan capability that used to be a planned CI gate now exists only as something a human invokes deliberately, so every hit gets investigated because a human asked for it — never as a silent pipeline signal nobody reads.

When to use

• Before merging a diff that looks suspiciously similar to something you've seen elsewhere, or that a reviewer flagged.
• During a periodic self-audit (e.g. the Phase-4 "run the full pipeline on this repo" dogfood exercise).
• A reviewer or the origin: uncertain self-flag from code-provenance names a residual-class file worth a closer look.

Do NOT use when:

• You expect this to run automatically in CI — it does not, by explicit council decision; nothing here is wired into any pipeline.
• You expect a clean result to prove the code was not copied — it cannot; see the scope bound below.
• You already have a specific, known source to check against a specific license — that's license-compliance-borrow-check, a targeted one-source check, not a corpus-wide scan.

Procedure

1. Inspect available tooling. The offline layer (L-1) is always available via npx:

   npx jscpd --version
   

   The online layer (L0) is optional — check whether it is installed and reachable:

   scanoss-py --version   # or: pip show scanoss
   

   Offline-only is expected and fine in most environments; report it honestly rather than treating it as a failure.
2. Run the offline scan (L-1) against the target path or diff:

   npx jscpd <path> --min-tokens 25
   

   --min-tokens 25 is the pre-registered shipping value — the best false-positive-clean point of the S0.3 sweep (internal/bench/provenance/reports/baseline-2026-07-28.md). Read every reported clone pair as a candidate, never a verdict.
3. If L0 is reachable, run the fingerprint scan:

   scanoss-py scan <path>
   

   Only WFP fingerprints leave the machine, never raw source (privacy by construction, principle 7). If L0 is unreachable, rate-limited, or not installed, report the result as partial (offline) explicitly — never claim a full two-layer scan ran.
4. Investigate every hit manually. For each match: is the matched file already in provenance/borrows.jsonl (cleared)? Does it read like independent convergence on a common algorithm shape, or an unattributed real copy? Open both files side by side before deciding — a match id is a starting point, not a conclusion.
5. Route unresolved hits. A hit with no existing ledger entry and no independent-convergence explanation routes to license-compliance-borrow-check to classify and draft the entry. Never clear a hit yourself by simply deciding it's fine.
6. Report the scope bound alongside every result (see below) — a clean run means "nothing matched this KB/corpus at this sensitivity", never "no copying happened".

What the measured numbers mean — and don't

On the frozen synthetic corpus (internal/bench/provenance/, 36 files, content-sha256 dbbc84a7…34bb3):

Full source: internal/bench/provenance/reports/baseline-2026-07-28.md.

• Scope bound: the corpus is synthetic-canonical — independently authored implementations of well-known algorithm shapes, never fetched or pasted from any upstream file. This measures transformation-depth sensitivity and false-positive rate on independent code — it does not measure recall against SCANOSS's real-OSS knowledge base. No sample here is indexed anywhere, so a KB lookup returning zero hits says nothing about the detector's real-world recall. Real-KB recall is unmeasured and would need a second, real-snippet corpus.
• Rename-only laundering is not reliably caught — 0/8 (SCANOSS), 4/8 (jscpd). The provenance ledger, not this scan, is the anti-launder control (see code-provenance).
• No tool sees model training data. Unconscious reproduction from training data is not addressed by this scan at all, regardless of outcome.

Output format

1. Per scan target: which layer(s) actually ran, hit or clean, and — if the online layer was unreachable — the explicit partial (offline) marker (never a silent green presented as a full scan).
2. For every hit: the matched file/snippet, a manual same-shape-vs-real-copy judgment, and the routing decision (already ledgered / route to license-compliance-borrow-check / independent convergence, no action).
3. The scope-bound paragraph from the section above, restated so the requester never reads a clean result as a copying guarantee.

Gotcha

• A clean scan is not proof of originality. It means "nothing matched at this sensitivity, in this corpus/KB" — never "not copied from training data".
• Rename-only copies routinely pass both layers. Do not trust a clean re-scan after a purely cosmetic rewrite as evidence of real transformation.
• Both of SCANOSS's measured false positives came from independently authored files matching on convergent canonical-algorithm shape (MIT snippet matches) — a hit is an investigation trigger, not an accusation.
• Nothing here is wired into CI. A hit found by this skill never blocked anything automatically; if you expected a pipeline signal, there isn't one — that's the point of Gate G0's Option A resolution.

Do NOT

• NEVER claim or imply this scan runs in CI — it explicitly does not, by council decision (Gate G0, Option A).
• NEVER report a scan as complete when the online layer was unreachable — always say partial (offline).
• NEVER present a clean result as "provably not copied" — restate the scope bound every time a result is shared.
• NEVER clear a hit without either a ledger entry or a documented independent-convergence judgment.

See also

• code-provenance — the rule that makes the ledger, not this scan, the primary anti-launder control.
• license-compliance-borrow-check — where an unresolved hit routes for classification + ledger drafting.
• internal/bench/provenance/reports/baseline-2026-07-28.md — the full measured baseline this skill's numbers are drawn from.
• docs/CLAIMS.md — claim provenance-detector-transformation-sensitivity (unbacked, carries the same scope bound).