porcupine-analyzerlisted

# Porcupine Failure Analyzer ## Quick start ```bash python3 SKILL_DIR/scripts/analyze_porcupine_failure.py path/to/violation.html ``` The script reads the HTML, finds non-linearizable partitions, and prints a diagnosis to stdout. The first line of each partition section is a one-line headline naming the contradicting op, the frontier state, and the most likely culprit — start there. ## How to read the output A typical diagnostic loop: 1. **Read the headline.** Each partition section opens with: `DIAGNOSIS: op#K c<id> <description> contradicts frontier state X; most likely culprit: op#J c<id> <description> (token T, produces_frontier_state)` This is your starting hypothesis. If the headline matches the failure you'd expect, you're often done at this step. 2. **Check the frontier state.** What does the model think was in place at the failure point? Is that what your test should have produced? 3. **Read the blocked candidate's description.** What did it return? Why does that contradict the frontier state? 4. **Use value-token provenance** to trace where the unexpected value came from. An `overlap` hit means a concurrent op produced it; a `before` hit means an older op wrote it and something should have invalidated it. The row tagged `(..., produces_frontier_state)` is the writer the model believed left the cache in its current state. 5. **Open the HTML in a browser** for the visual timeline. The analyzer has already told you the op# to