feat(opcodes): data model + loader for opcodes.toml

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

tools/opcodes_model.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""In-memory model + loader + linter for vm-map/opcodes.toml (the canonical opcode reference).
+Read-only: uses stdlib tomllib. See docs/superpowers/specs/2026-07-06-opcode-reference-design.md."""
+from __future__ import annotations
+import tomllib
+from dataclasses import dataclass, field
+from pathlib import Path
+
+CATEGORIES = {"marker", "structural", "control", "adv", "draw", "audio", "input", "compute", "unknown"}
+SOURCES = {"kelebek", "harness", "investigation", "frida", "unicorn", "inference"}
+CONFIDENCE = {"low": 1, "med": 2, "high": 3}
+
+@dataclass
+class Semantics:
+    name: str
+    category: str = "unknown"
+    summary: str = ""
+    noop_headless: bool = False
+    source: str = "kelebek"
+    confidence: str = "low"
+    depends_on: list[int] = field(default_factory=list)
+    evidence: str = ""
+    details: str = ""
+    confirm_by: str = ""
+    args: list[dict] = field(default_factory=list)
+
+@dataclass
+class Opcode:
+    op: int
+    label: str
+    argc: int
+    code_target_args: list[int] = field(default_factory=list)
+    abi_source: str = "kelebek+decode-validated"
+    abi_note: str = ""
+    semantics: Semantics | None = None
+
+@dataclass
+class Model:
+    meta: dict
+    opcodes: dict[int, Opcode]
+
+def load(path) -> Model:
+    data = tomllib.loads(Path(path).read_text(encoding="utf-8"))
+    ops: dict[int, Opcode] = {}
+    for e in data.get("opcode", []):
+        sem = None
+        s = e.get("semantics")
+        if s is not None:
+            sem = Semantics(
+                name=s.get("name", e.get("label", "")),
+                category=s.get("category", "unknown"),
+                summary=s.get("summary", ""),
+                noop_headless=bool(s.get("noop_headless", False)),
+                source=s.get("source", "kelebek"),
+                confidence=s.get("confidence", "low"),
+                depends_on=[int(x) for x in s.get("depends_on", [])],
+                evidence=s.get("evidence", ""),
+                details=s.get("details", ""),
+                confirm_by=s.get("confirm_by", ""),
+                args=list(s.get("args", [])),
+            )
+        ops[int(e["op"])] = Opcode(
+            op=int(e["op"]), label=e.get("label", ""), argc=int(e["argc"]),
+            code_target_args=[int(x) for x in e.get("code_target_args", [])],
+            abi_source=e.get("abi_source", "kelebek+decode-validated"),
+            abi_note=e.get("abi_note", ""), semantics=sem,
+        )
+    return Model(meta=data.get("meta", {}), opcodes=ops)
+
+def dependents(model: Model) -> dict[int, list[int]]:
+    """Reverse of depends_on: op -> [ops whose semantics depend on it]."""
+    rev: dict[int, list[int]] = {op: [] for op in model.opcodes}
+    for op, oc in model.opcodes.items():
+        if oc.semantics:
+            for dep in oc.semantics.depends_on:
+                rev.setdefault(dep, []).append(op)
+    for k in rev:
+        rev[k].sort()
+    return rev