rebind-manifest

name

rebind-manifest

description

Pipeline stage 2 of report-regeneration (detect -> strip -> rebind): turn an RSG + the OLD template + the new dataset into a schema-valid Binding Manifest and a taint dictionary. Enforces the earned-frozen rule (a data-shaped literal / old-taint / new-dataset value demotes a candidate-frozen node to needs-review), forces raster/embedded-cache nodes to regenerate, and proposes each node's data_query. Reach for this when building or amending a Binding Manifest.

Skill: rebind-manifest (report-regeneration stage 2)

The surgeon planning the transplant. Given a Report Structure Graph (RSG), the OLD template, and the new dataset, this stage builds a versioned, human-reviewable Binding Manifest plus the taint dictionary that the V4 egress leg scans against. It addresses + classifies + proposes queries — it never renders.

The honest guarantee (verbatim — never soften it): the plugin GUARANTEES only (a) no old-client-data leak and (b) every low-confidence classification is surfaced for human review. It does NOT guarantee a human-free-correct report. needs-review is guarantee (b) made mechanical.

Run it

python3 build_manifest.py \
    --rsg <rsg.json> --template <old-template.html> --new-data <data.json> \
    --out <manifest.json> [--taint-out <taint-dict.json>] \
    [--manifest-version 0.1.0] [--confidence-threshold 0.7] [--format json|text] [--pretty]

Exit codes: 0 valid manifest; 2 usage / path-guard / parse error / input RSG failed rsg.schema.json (fail-closed); 3 manifest built but failed binding-manifest.schema.json.

--new-data shape: {"source_ref": "...", "source_period": "...", "values": [...] | {...}}. values is the new value domain the earned-frozen rule tests against; if omitted, every scalar leaf (minus reserved metadata keys) is the domain.

What it does (mirrors knowledge/core-architecture-spec.md §3/§4/§6)

1. Taint dictionary from the OLD template — distinct rendered value literals (via the pinned, non-inference data-shaped-literal detector) + identity strings (author / company / title / source-filename), including a declared prior-artifact taint block (old_company: "..." comment convention) when present.
2. Earned-frozen rule. frozen is never the default. A candidate-frozen node is demoted to needs-review if an independent re-run of the detector over its rendered text fires (or the RSG already flagged it), OR the text carries an old-taint value, OR it carries a new-dataset value. A data-shaped literal force-demotes regardless of classifier confidence. (The RSG is already schema-forbidden from carrying frozen + data_shaped_literal:true, so the demotion authority here is the independent detector, not the RSG flag.)
3. Raster / embedded-cache force. Any node that renders as a raster or carries an embedded binary/data cache is forced to regenerate — a transplanted blob cannot be proven data-free.
4. Propose one binding per NON-static node: node_id, anchor, class, confidence, provenance {source, source_period, method, pbi_route}, data_query. A frozen binding carries no data_query; every non-frozen class must carry one.
5. Validate the manifest against binding-manifest.schema.json (stdlib-only validator).

Conservative-by-design note

The detector is deliberately non-inference: it fires on "100%" in a marketing tagline exactly as on a KPI's "100%". Over-flagging a candidate-frozen node to needs-review is the safe direction (guarantee (b)); under-flagging a data-bound node classed frozen is what a leak looks like.

Constraints

Stdlib only (argparse / html.parser / re / json / pathlib), Python 3.9.6, no pip, no network, no subprocess. Path-guarded (rejects .. traversal). Code is written to the schemas in ../../knowledge/.

Tests

python3 -m unittest tests.test_manifest (run from this skill directory) — demotion, taint dict (inline + real corpus), raster→regenerate, and schema-validity.