package/
├── README.md
├── WATERFALL.md
├── metrics.csv
├── inputs/
│   └── <input-name>.cif
└── outputs/
    └── <input-name>/
        └── <design-id>/
            ├── <design-id>__design.cif
            ├── <design-id>__<role>__<backend>__<prediction-id>__refold.cif
            └── <design-id>__<role>__<backend>__<prediction-id>__confidence.json

The generated design is the oracle input and alignment reference. Include it once. Transform each copied refold into the design's frame by superimposing the recorded chain or chains of interest, then write only that aligned refold to the package. Include confidence JSON when it exists. Do not add a database, manifest, checksum file, duplicate raw/aligned structures, provenance JSON, filter-results table, or alignment metadata file.

Prerequisites

The RFD4-Proteina Python environment with AtomWorks, Biotite, and NumPy; a reconciled campaign ledger and its archived structures.

Use the user-selected model checkout at RFPROTEINA_ROOT for model commands, configs, and upstream manuals. This catalog contains the skills, not that checkout. In helper examples, resolve open-models-skills/rfd4-proteina/<name>/... to its absolute installed path when the catalog and model checkout are separate.

Inputs

Required: the user-selected existing database, immutable criteria-set ID, selected designs, and fresh archive destination. Optional: package display name. Read chain-of-interest mappings and measured values from the reconciled ledger.

Instructions

Confirm the immutable criteria-set version and whether the selection is all passers or an explicitly defined unique-passer subset.
Import corrections and rerun evaluate-passers for the chosen criteria set, then reconcile the campaign ledger. Delivery uses each design's latest decision and its exact evidence; historical passes do not override newer failures. Stop on missing selected designs, missing input mappings, missing criteria evidence, or ambiguous chain-of-interest alignment.
Group selected designs by their exact annotated-CIF input and assign unique, filesystem-safe names.
Copy each annotated CIF and design. Copy and align every linked oracle prediction, preserving role (on-target, named off-target, checkpoint), backend, and prediction identity in its filename. Copy available confidence JSON beside it.
Write metrics.csv with one row per design and the actual numeric values used by every filter. Do not replace those values with Boolean pass/fail columns.
Write WATERFALL.md with the exact metric, operator, threshold, missing policy, and cumulative counts overall and per input.
Write a short README.md, create the TAR, reopen it, and validate its expected paths and one-to-one design/input grouping.

Read references/package-format.md when building or reviewing the archive. Read references/alignment.md before aligning refolds.

Examples

python open-models-skills/rfd4-proteina/rfd4-proteina-package/scripts/package_campaign.py --help

The builder reads the campaign database only while constructing the archive. The archive itself is a pure directory tree of Markdown, CSV, CIF, and optional JSON files. Compressed source structures are decoded into ordinary CIFs in the archive; never copy gzip or Zstandard bytes under a plain .cif filename.

Limitations

Alignment requires an unambiguous recorded chain-of-interest mapping. Packaging preserves measured evidence; it does not validate biological claims or repair missing provenance.

Troubleshooting

Stop on missing archived files, unreconciled metrics, or ambiguous alignment. Resolve the ledger evidence first; select a new destination if an archive already exists.

Available Scripts

Script	Purpose	Arguments
`scripts/package_campaign.py`	Build a TAR from an existing ledger	`--database`, `--criteria-set`, `--output`; optional `--package-name`

rfd4-proteina-package

Package RFD4-Proteína designs

Purpose

Output Format