Installs into .claude/skills of the current project.
Are you the author of Bapps 2afc Protocol?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-bapps-2afc-protocol)
---
name: bapps_2afc_protocol
description: Evaluate BAPPS-style two-alternative forced choice perceptual similarity triplets with auditable per-item predictions.
---
# BAPPS 2AFC Protocol
Use this skill when evaluating a perceptual distance function on reference, `p0`, `p1`, and human-choice triplets.
Do not use this skill to compute LPIPS distances internally; it owns the protocol and scoring contract only.
## Inputs
- Triplets with `id`, `ref`, `p0`, `p1`, and `judge` fields.
- A distance provider that returns `d0 = distance(ref, p0)` and `d1 = distance(ref, p1)`, or precomputed `d0` and `d1` values.
- Labels where `judge = 0` means `p0` is closer and `judge = 1` means `p1` is closer.
## Outputs
- Aggregate `2afc_accuracy`.
- Per-item predictions, distances, labels, and correctness.
## Workflow
1. Validate every triplet label.
2. Compute or consume `d0` and `d1`.
3. Predict `1` when `d1 < d0`; otherwise predict `0`.
4. Average correctness over items.
5. Save per-item records for recovery analysis.
## Validation
Run:
```bash
python scripts/evaluate_2afc.py --self-test
python tests/test_evaluate_2afc.py
```
## Limitations
Ties default to prediction `0` for deterministic scoring. If a benchmark specifies probabilistic tie handling, record that deviation outside this skill.
