feat(scripts): add symbolic forward-chaining rule engine

scripts/symbolic_reasoner.py

@@ -0,0 +1,276 @@
+#!/usr/bin/env python3
+"""symbolic_reasoner.py — Forward-chaining rule engine for the Timmy Foundation fleet.
+
+A classical GOFAI approach: declarative IF-THEN rules evaluated over a
+working-memory of facts.  Rules fire until quiescence (no new facts) or
+a configurable cycle limit.  Designed to sit *beside* the LLM layer so
+that hard policy constraints never depend on probabilistic inference.
+
+Usage:
+    python symbolic_reasoner.py --rules rules.yaml --facts facts.yaml
+    python symbolic_reasoner.py --self-test
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple
+
+try:
+    import yaml
+except ImportError:
+    yaml = None  # graceful fallback — JSON-only mode
+
+
+# ---------------------------------------------------------------------------
+# Domain types
+# ---------------------------------------------------------------------------
+Fact = Tuple[str, ...]  # e.g. ("agent", "timmy", "role", "infrastructure")
+
+
+@dataclass(frozen=True)
+class Rule:
+    """A single IF-THEN production rule."""
+    name: str
+    conditions: FrozenSet[Fact]       # all must be present
+    negations: FrozenSet[Fact]        # none may be present
+    conclusions: FrozenSet[Fact]      # added when rule fires
+    priority: int = 0                 # higher fires first
+
+    def satisfied(self, wm: Set[Fact]) -> bool:
+        return self.conditions.issubset(wm) and self.negations.isdisjoint(wm)
+
+
+# ---------------------------------------------------------------------------
+# Engine
+# ---------------------------------------------------------------------------
+class SymbolicReasoner:
+    """Forward-chaining production system."""
+
+    def __init__(self, rules: List[Rule], *, cycle_limit: int = 200):
+        self._rules = sorted(rules, key=lambda r: -r.priority)
+        self._cycle_limit = cycle_limit
+        self._trace: List[str] = []
+
+    # -- public API ---------------------------------------------------------
+
+    def infer(self, initial_facts: Set[Fact]) -> Set[Fact]:
+        """Run to quiescence and return the final working-memory."""
+        wm = set(initial_facts)
+        fired: Set[str] = set()
+        for cycle in range(self._cycle_limit):
+            progress = False
+            for rule in self._rules:
+                if rule.name in fired:
+                    continue
+                if rule.satisfied(wm):
+                    new = rule.conclusions - wm
+                    if new:
+                        wm |= new
+                        fired.add(rule.name)
+                        self._trace.append(
+                            f"cycle {cycle}: {rule.name} => {_fmt_facts(new)}"
+                        )
+                        progress = True
+                        break  # restart from highest-priority rule
+            if not progress:
+                break
+        return wm
+
+    def query(self, wm: Set[Fact], pattern: Tuple[Optional[str], ...]) -> List[Fact]:
+        """Return facts matching *pattern* (None = wildcard)."""
+        return [
+            f for f in wm
+            if len(f) == len(pattern)
+            and all(p is None or p == v for p, v in zip(pattern, f))
+        ]
+
+    @property
+    def trace(self) -> List[str]:
+        return list(self._trace)
+
+    # -- serialisation helpers -----------------------------------------------
+
+    @classmethod
+    def from_dicts(cls, raw_rules: List[Dict], **kw) -> "SymbolicReasoner":
+        rules = [_parse_rule(r) for r in raw_rules]
+        return cls(rules, **kw)
+
+    @classmethod
+    def from_file(cls, path: Path, **kw) -> "SymbolicReasoner":
+        text = path.read_text()
+        if path.suffix in (".yaml", ".yml"):
+            if yaml is None:
+                raise RuntimeError("PyYAML required for .yaml rules")
+            data = yaml.safe_load(text)
+        else:
+            data = json.loads(text)
+        return cls.from_dicts(data["rules"], **kw)
+
+
+# ---------------------------------------------------------------------------
+# Parsing helpers
+# ---------------------------------------------------------------------------
+def _parse_fact(raw: list | str) -> Fact:
+    if isinstance(raw, str):
+        return tuple(raw.split())
+    return tuple(str(x) for x in raw)
+
+
+def _parse_rule(d: Dict) -> Rule:
+    return Rule(
+        name=d["name"],
+        conditions=frozenset(_parse_fact(c) for c in d.get("if", [])),
+        negations=frozenset(_parse_fact(c) for c in d.get("unless", [])),
+        conclusions=frozenset(_parse_fact(c) for c in d.get("then", [])),
+        priority=d.get("priority", 0),
+    )
+
+
+def _fmt_facts(facts: Set[Fact]) -> str:
+    return ", ".join(" ".join(f) for f in sorted(facts))
+
+
+# ---------------------------------------------------------------------------
+# Built-in fleet rules (loaded when no --rules file is given)
+# ---------------------------------------------------------------------------
+DEFAULT_FLEET_RULES: List[Dict] = [
+    {
+        "name": "route-ci-to-timmy",
+        "if": [["task", "category", "ci"]],
+        "then": [["assign", "timmy"], ["reason", "timmy", "best-ci-merge-rate"]],
+        "priority": 10,
+    },
+    {
+        "name": "route-security-to-timmy",
+        "if": [["task", "category", "security"]],
+        "then": [["assign", "timmy"], ["reason", "timmy", "security-specialist"]],
+        "priority": 10,
+    },
+    {
+        "name": "route-architecture-to-gemini",
+        "if": [["task", "category", "architecture"]],
+        "unless": [["assign", "timmy"]],
+        "then": [["assign", "gemini"], ["reason", "gemini", "architecture-strength"]],
+        "priority": 8,
+    },
+    {
+        "name": "route-review-to-allegro",
+        "if": [["task", "category", "review"]],
+        "then": [["assign", "allegro"], ["reason", "allegro", "highest-quality-per-pr"]],
+        "priority": 9,
+    },
+    {
+        "name": "route-frontend-to-claude",
+        "if": [["task", "category", "frontend"]],
+        "unless": [["task", "repo", "fleet-ops"]],
+        "then": [["assign", "claude"], ["reason", "claude", "high-volume-frontend"]],
+        "priority": 5,
+    },
+    {
+        "name": "block-merge-without-review",
+        "if": [["pr", "status", "open"], ["pr", "reviews", "0"]],
+        "then": [["pr", "action", "block-merge"], ["reason", "policy", "no-unreviewed-merges"]],
+        "priority": 20,
+    },
+    {
+        "name": "block-merge-ci-failing",
+        "if": [["pr", "status", "open"], ["pr", "ci", "failing"]],
+        "then": [["pr", "action", "block-merge"], ["reason", "policy", "ci-must-pass"]],
+        "priority": 20,
+    },
+    {
+        "name": "auto-label-hotfix",
+        "if": [["pr", "title-prefix", "hotfix"]],
+        "then": [["pr", "label", "hotfix"], ["pr", "priority", "urgent"]],
+        "priority": 15,
+    },
+]
+
+
+# ---------------------------------------------------------------------------
+# Self-test
+# ---------------------------------------------------------------------------
+def _self_test() -> bool:
+    """Verify core behaviour; returns True on success."""
+    engine = SymbolicReasoner.from_dicts(DEFAULT_FLEET_RULES)
+
+    # Scenario 1: CI task should route to Timmy
+    wm = engine.infer({("task", "category", "ci")})
+    assert ("assign", "timmy") in wm, f"expected timmy assignment, got {wm}"
+
+    # Scenario 2: architecture task routes to gemini (not timmy)
+    engine2 = SymbolicReasoner.from_dicts(DEFAULT_FLEET_RULES)
+    wm2 = engine2.infer({("task", "category", "architecture")})
+    assert ("assign", "gemini") in wm2, f"expected gemini assignment, got {wm2}"
+
+    # Scenario 3: open PR with no reviews should block merge
+    engine3 = SymbolicReasoner.from_dicts(DEFAULT_FLEET_RULES)
+    wm3 = engine3.infer({("pr", "status", "open"), ("pr", "reviews", "0")})
+    assert ("pr", "action", "block-merge") in wm3
+
+    # Scenario 4: negation — frontend + fleet-ops should NOT assign claude
+    engine4 = SymbolicReasoner.from_dicts(DEFAULT_FLEET_RULES)
+    wm4 = engine4.infer({("task", "category", "frontend"), ("task", "repo", "fleet-ops")})
+    assert ("assign", "claude") not in wm4
+
+    # Scenario 5: query with wildcards
+    results = engine.query(wm, ("reason", None, None))
+    assert len(results) > 0
+
+    print("All 5 self-test scenarios passed.")
+    for line in engine.trace:
+        print(f"  {line}")
+    return True
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+def main():
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--rules", type=Path, help="YAML/JSON rule file")
+    ap.add_argument("--facts", type=Path, help="YAML/JSON initial facts")
+    ap.add_argument("--self-test", action="store_true")
+    ap.add_argument("--json", action="store_true", help="output as JSON")
+    args = ap.parse_args()
+
+    if args.self_test:
+        ok = _self_test()
+        sys.exit(0 if ok else 1)
+
+    if args.rules:
+        engine = SymbolicReasoner.from_file(args.rules)
+    else:
+        engine = SymbolicReasoner.from_dicts(DEFAULT_FLEET_RULES)
+
+    if args.facts:
+        text = args.facts.read_text()
+        if args.facts.suffix in (".yaml", ".yml"):
+            raw = yaml.safe_load(text)
+        else:
+            raw = json.loads(text)
+        initial = {_parse_fact(f) for f in raw.get("facts", [])}
+    else:
+        initial = set()
+        print("No --facts provided; running with empty working memory.")
+
+    wm = engine.infer(initial)
+
+    if args.json:
+        print(json.dumps({"facts": [list(f) for f in sorted(wm)], "trace": engine.trace}, indent=2))
+    else:
+        print(f"Final working memory ({len(wm)} facts):")
+        for f in sorted(wm):
+            print(f"  {' '.join(f)}")
+        if engine.trace:
+            print(f"\nInference trace ({len(engine.trace)} firings):")
+            for line in engine.trace:
+                print(f"  {line}")
+
+
+if __name__ == "__main__":
+    main()