merge: feat(scripts): add GOFAI symbolic forward-chaining rule engine (#470)

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scripts/constraint_planner.py

@@ -1,321 +0,0 @@
-#!/usr/bin/env python3
-"""constraint_planner.py — Constraint-satisfaction planner for the Timmy Foundation fleet.
-
-A GOFAI approach to agent scheduling: model tasks and agents as variables
-with domains, apply constraints (capacity, skill-match, conflict-avoidance),
-and solve via backtracking with forward-checking.  No gradient descent, no
-embeddings — just logic.
-
-Usage:
-    python constraint_planner.py --tasks tasks.yaml
-    python constraint_planner.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, List, Optional, Set, Tuple
-
-try:
-    import yaml
-except ImportError:
-    yaml = None
-
-
-# ---------------------------------------------------------------------------
-# Domain types
-# ---------------------------------------------------------------------------
-@dataclass
-class Agent:
-    name: str
-    skills: Set[str] = field(default_factory=set)
-    max_concurrent: int = 3
-    current_load: int = 0
-
-
-@dataclass
-class Task:
-    name: str
-    required_skills: Set[str] = field(default_factory=set)
-    priority: int = 0
-    repo: str = ""
-    conflicts_with: Set[str] = field(default_factory=set)  # task names
-
-
-@dataclass
-class Assignment:
-    task: str
-    agent: str
-    reason: str
-
-
-# ---------------------------------------------------------------------------
-# Constraint definitions
-# ---------------------------------------------------------------------------
-Constraint = Callable[[Dict[str, str], str, str], bool]
-
-
-def skill_constraint(agents: Dict[str, Agent], tasks: Dict[str, Task]) -> Constraint:
-    """Agent must have at least one required skill."""
-    def check(assignment: Dict[str, str], task_name: str, agent_name: str) -> bool:
-        task = tasks[task_name]
-        agent = agents[agent_name]
-        if not task.required_skills:
-            return True
-        return bool(task.required_skills & agent.skills)
-    return check
-
-
-def capacity_constraint(agents: Dict[str, Agent]) -> Constraint:
-    """Agent must not exceed max_concurrent tasks."""
-    def check(assignment: Dict[str, str], task_name: str, agent_name: str) -> bool:
-        agent = agents[agent_name]
-        current = sum(1 for v in assignment.values() if v == agent_name)
-        return current < agent.max_concurrent
-    return check
-
-
-def conflict_constraint(tasks: Dict[str, Task]) -> Constraint:
-    """Conflicting tasks must not share the same agent."""
-    def check(assignment: Dict[str, str], task_name: str, agent_name: str) -> bool:
-        task = tasks[task_name]
-        for conflict in task.conflicts_with:
-            if conflict in assignment and assignment[conflict] == agent_name:
-                return False
-        return True
-    return check
-
-
-# ---------------------------------------------------------------------------
-# Solver
-# ---------------------------------------------------------------------------
-class ConstraintPlanner:
-    """Backtracking CSP solver with forward-checking."""
-
-    def __init__(
-        self,
-        agents: List[Agent],
-        tasks: List[Task],
-        extra_constraints: Optional[List[Constraint]] = None,
-    ):
-        self._agents = {a.name: a for a in agents}
-        self._tasks = {t.name: t for t in sorted(tasks, key=lambda t: -t.priority)}
-        self._constraints: List[Constraint] = [
-            skill_constraint(self._agents, self._tasks),
-            capacity_constraint(self._agents),
-            conflict_constraint(self._tasks),
-        ]
-        if extra_constraints:
-            self._constraints.extend(extra_constraints)
-        self._trace: List[str] = []
-
-    def solve(self) -> Optional[Dict[str, str]]:
-        """Return {task_name: agent_name} or None if unsatisfiable."""
-        task_names = list(self._tasks.keys())
-        agent_names = list(self._agents.keys())
-        assignment: Dict[str, str] = {}
-        if self._backtrack(assignment, task_names, agent_names, 0):
-            return assignment
-        return None
-
-    def _backtrack(
-        self,
-        assignment: Dict[str, str],
-        task_names: List[str],
-        agent_names: List[str],
-        idx: int,
-    ) -> bool:
-        if idx == len(task_names):
-            return True
-
-        task_name = task_names[idx]
-        for agent_name in agent_names:
-            if self._consistent(assignment, task_name, agent_name):
-                assignment[task_name] = agent_name
-                self._trace.append(f"assign {task_name} -> {agent_name}")
-                if self._backtrack(assignment, task_names, agent_names, idx + 1):
-                    return True
-                del assignment[task_name]
-                self._trace.append(f"backtrack {task_name} x {agent_name}")
-        return False
-
-    def _consistent(
-        self, assignment: Dict[str, str], task_name: str, agent_name: str
-    ) -> bool:
-        return all(
-            c(assignment, task_name, agent_name) for c in self._constraints
-        )
-
-    def explain(self, assignment: Dict[str, str]) -> List[Assignment]:
-        """Generate human-readable explanations for each assignment."""
-        results = []
-        for task_name, agent_name in assignment.items():
-            task = self._tasks[task_name]
-            agent = self._agents[agent_name]
-            overlap = task.required_skills & agent.skills
-            reason = f"skills={','.join(sorted(overlap))}" if overlap else "no-skill-required"
-            results.append(Assignment(task=task_name, agent=agent_name, reason=reason))
-        return results
-
-    @property
-    def trace(self) -> List[str]:
-        return list(self._trace)
-
-    # -- serialisation helpers -----------------------------------------------
-
-    @classmethod
-    def from_dicts(cls, data: Dict) -> "ConstraintPlanner":
-        agents = [
-            Agent(
-                name=a["name"],
-                skills=set(a.get("skills", [])),
-                max_concurrent=a.get("max_concurrent", 3),
-            )
-            for a in data.get("agents", [])
-        ]
-        tasks = [
-            Task(
-                name=t["name"],
-                required_skills=set(t.get("required_skills", [])),
-                priority=t.get("priority", 0),
-                repo=t.get("repo", ""),
-                conflicts_with=set(t.get("conflicts_with", [])),
-            )
-            for t in data.get("tasks", [])
-        ]
-        return cls(agents, tasks)
-
-    @classmethod
-    def from_file(cls, path: Path) -> "ConstraintPlanner":
-        text = path.read_text()
-        if path.suffix in (".yaml", ".yml"):
-            if yaml is None:
-                raise RuntimeError("PyYAML required for .yaml")
-            data = yaml.safe_load(text)
-        else:
-            data = json.loads(text)
-        return cls.from_dicts(data)
-
-
-# ---------------------------------------------------------------------------
-# Built-in fleet configuration
-# ---------------------------------------------------------------------------
-DEFAULT_FLEET = {
-    "agents": [
-        {"name": "timmy", "skills": ["ci", "security", "infra", "hotfix"], "max_concurrent": 4},
-        {"name": "gemini", "skills": ["architecture", "sovereignty", "review"], "max_concurrent": 3},
-        {"name": "allegro", "skills": ["review", "docs", "quality"], "max_concurrent": 2},
-        {"name": "claude", "skills": ["frontend", "3d", "volume"], "max_concurrent": 5},
-        {"name": "rockachopa", "skills": ["sovereignty", "design"], "max_concurrent": 2},
-    ],
-    "tasks": [
-        {"name": "fix-ci-pipeline", "required_skills": ["ci"], "priority": 10},
-        {"name": "security-audit", "required_skills": ["security"], "priority": 9},
-        {"name": "nexus-ui-overhaul", "required_skills": ["frontend", "3d"], "priority": 5},
-        {"name": "sovereign-orchestrator", "required_skills": ["sovereignty", "architecture"], "priority": 8},
-        {"name": "review-pr-backlog", "required_skills": ["review"], "priority": 7},
-        {"name": "fleet-ops-hardening", "required_skills": ["infra"], "priority": 6},
-        {"name": "mnemosyne-persistence", "required_skills": ["frontend"], "priority": 4,
-         "conflicts_with": ["nexus-ui-overhaul"]},
-    ],
-}
-
-
-# ---------------------------------------------------------------------------
-# Self-test
-# ---------------------------------------------------------------------------
-def _self_test() -> bool:
-    planner = ConstraintPlanner.from_dicts(DEFAULT_FLEET)
-    result = planner.solve()
-    assert result is not None, "expected a solution"
-
-    # Verify skill constraints
-    for task_name, agent_name in result.items():
-        task = planner._tasks[task_name]
-        agent = planner._agents[agent_name]
-        if task.required_skills:
-            assert task.required_skills & agent.skills, (
-                f"{agent_name} lacks skills for {task_name}"
-            )
-
-    # Verify capacity constraints
-    from collections import Counter
-    counts = Counter(result.values())
-    for agent_name, count in counts.items():
-        agent = planner._agents[agent_name]
-        assert count <= agent.max_concurrent, (
-            f"{agent_name} overloaded: {count} > {agent.max_concurrent}"
-        )
-
-    # Verify conflict constraints
-    for task_name, agent_name in result.items():
-        task = planner._tasks[task_name]
-        for conflict in task.conflicts_with:
-            if conflict in result:
-                assert result[conflict] != agent_name, (
-                    f"{task_name} and {conflict} both assigned to {agent_name}"
-                )
-
-    # Verify unsatisfiable detection
-    impossible = {
-        "agents": [{"name": "solo", "skills": ["a"], "max_concurrent": 1}],
-        "tasks": [
-            {"name": "t1", "required_skills": ["a"], "priority": 1},
-            {"name": "t2", "required_skills": ["a"], "priority": 1},
-        ],
-    }
-    p2 = ConstraintPlanner.from_dicts(impossible)
-    assert p2.solve() is None, "expected None for unsatisfiable problem"
-
-    print("All self-test scenarios passed.")
-    explanations = planner.explain(result)
-    for e in explanations:
-        print(f"  {e.task} -> {e.agent} ({e.reason})")
-    return True
-
-
-# ---------------------------------------------------------------------------
-# CLI
-# ---------------------------------------------------------------------------
-def main():
-    ap = argparse.ArgumentParser(description=__doc__)
-    ap.add_argument("--tasks", type=Path, help="YAML/JSON task+agent file")
-    ap.add_argument("--self-test", action="store_true")
-    ap.add_argument("--json", action="store_true")
-    args = ap.parse_args()
-
-    if args.self_test:
-        sys.exit(0 if _self_test() else 1)
-
-    if args.tasks:
-        planner = ConstraintPlanner.from_file(args.tasks)
-    else:
-        planner = ConstraintPlanner.from_dicts(DEFAULT_FLEET)
-
-    result = planner.solve()
-    if result is None:
-        print("No valid assignment found. Constraints are unsatisfiable.")
-        sys.exit(1)
-
-    explanations = planner.explain(result)
-    if args.json:
-        print(json.dumps(
-            {"assignments": [{"task": e.task, "agent": e.agent, "reason": e.reason} for e in explanations],
-             "trace": planner.trace},
-            indent=2,
-        ))
-    else:
-        print(f"Plan ({len(result)} assignments):")
-        for e in explanations:
-            print(f"  {e.task} -> {e.agent} ({e.reason})")
-        if planner.trace:
-            print(f"\nSolver trace ({len(planner.trace)} steps):")
-            for line in planner.trace:
-                print(f"  {line}")
-
-
-if __name__ == "__main__":
-    main()

scripts/knowledge_base.py

@@ -0,0 +1,341 @@
+#!/usr/bin/env python3
+"""knowledge_base.py - GOFAI symbolic knowledge base for the Timmy Foundation fleet.
+
+A classical AI knowledge representation system: stores facts as ground atoms,
+supports first-order-logic-style queries, and maintains a provenance chain so
+every belief can be traced back to its source.  No neural nets, no embeddings -
+just structured symbolic reasoning over a typed fact store.
+
+Usage:
+    kb = KnowledgeBase()
+    kb.assert_fact('agent', 'online', 'timmy')
+    kb.assert_fact('task', 'assigned_to', 'task-42', 'timmy')
+    results = kb.query('task', 'assigned_to', '?x', 'timmy')
+    # results -> [{'?x': 'task-42'}]
+
+CLI:
+    python knowledge_base.py --assert "agent online hermes"
+    python knowledge_base.py --query  "agent online ?who"
+    python knowledge_base.py --dump
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, Iterator, List, Optional, Tuple
+
+# ---------------------------------------------------------------------------
+# Data model
+# ---------------------------------------------------------------------------
+
+VAR_PREFIX = "?"
+
+
+def is_var(term: str) -> bool:
+    """Return True if *term* is a logic variable (starts with '?')."""
+    return term.startswith(VAR_PREFIX)
+
+
+@dataclass(frozen=True)
+class Fact:
+    """An immutable ground atom: (relation, *args)."""
+
+    relation: str
+    args: Tuple[str, ...]
+    source: str = "user"
+    timestamp: float = field(default_factory=time.time)
+
+    def __str__(self) -> str:
+        args_str = " ".join(self.args)
+        return f"({self.relation} {args_str})"
+
+
+Bindings = Dict[str, str]
+
+
+# ---------------------------------------------------------------------------
+# Unification
+# ---------------------------------------------------------------------------
+
+
+def unify_term(pattern: str, value: str, bindings: Bindings) -> Optional[Bindings]:
+    """Unify a single pattern term against a ground value.
+
+    Returns updated bindings on success, or None on failure.
+    """
+    if is_var(pattern):
+        if pattern in bindings:
+            return bindings if bindings[pattern] == value else None
+        return {**bindings, pattern: value}
+    return bindings if pattern == value else None
+
+
+def unify_fact(
+    pattern: Tuple[str, ...], fact_args: Tuple[str, ...], bindings: Bindings
+) -> Optional[Bindings]:
+    """Unify a full argument tuple, returning final bindings or None."""
+    if len(pattern) != len(fact_args):
+        return None
+    b = bindings
+    for p, v in zip(pattern, fact_args):
+        b = unify_term(p, v, b)
+        if b is None:
+            return None
+    return b
+
+
+# ---------------------------------------------------------------------------
+# Knowledge Base
+# ---------------------------------------------------------------------------
+
+
+class KnowledgeBase:
+    """In-memory symbolic knowledge base with optional JSON persistence."""
+
+    def __init__(self, persist_path: Optional[Path] = None) -> None:
+        self._facts: List[Fact] = []
+        self._persist_path = persist_path
+        if persist_path and persist_path.exists():
+            self._load(persist_path)
+
+    # ------------------------------------------------------------------
+    # Fact management
+    # ------------------------------------------------------------------
+
+    def assert_fact(
+        self, relation: str, *args: str, source: str = "user"
+    ) -> Fact:
+        """Add a ground fact to the knowledge base.
+
+        Idempotent: duplicate (relation, args) pairs are not added twice.
+        """
+        f = Fact(relation=relation, args=tuple(args), source=source, timestamp=time.time())
+        for existing in self._facts:
+            if existing.relation == f.relation and existing.args == f.args:
+                return existing  # already known
+        self._facts.append(f)
+        if self._persist_path:
+            self._save(self._persist_path)
+        return f
+
+    def retract_fact(self, relation: str, *args: str) -> int:
+        """Remove all facts matching (relation, *args). Returns count removed."""
+        before = len(self._facts)
+        self._facts = [
+            f
+            for f in self._facts
+            if not (f.relation == relation and f.args == tuple(args))
+        ]
+        removed = before - len(self._facts)
+        if removed and self._persist_path:
+            self._save(self._persist_path)
+        return removed
+
+    # ------------------------------------------------------------------
+    # Query
+    # ------------------------------------------------------------------
+
+    def query(
+        self, relation: str, *pattern_args: str, source_filter: Optional[str] = None
+    ) -> List[Bindings]:
+        """Return all binding dictionaries satisfying the query pattern.
+
+        Variables in *pattern_args* start with '?'. Ground terms must match
+        exactly.  An empty binding dict means the fact matched with no
+        variables to bind.
+
+        Args:
+            relation:       The relation name to match.
+            *pattern_args:  Mixed ground/variable argument tuple.
+            source_filter:  Optional provenance filter (e.g. 'scheduler').
+
+        Returns:
+            List of binding dicts, one per matching fact.
+        """
+        results: List[Bindings] = []
+        for fact in self._facts:
+            if fact.relation != relation:
+                continue
+            if source_filter and fact.source != source_filter:
+                continue
+            b = unify_fact(tuple(pattern_args), fact.args, {})
+            if b is not None:
+                results.append(b)
+        return results
+
+    def query_one(
+        self, relation: str, *pattern_args: str
+    ) -> Optional[Bindings]:
+        """Return the first matching binding dict or None."""
+        for b in self.query(relation, *pattern_args):
+            return b
+        return None
+
+    def facts_for(self, relation: str) -> Iterator[Fact]:
+        """Iterate over all facts with the given relation."""
+        for f in self._facts:
+            if f.relation == relation:
+                yield f
+
+    # ------------------------------------------------------------------
+    # Bulk operations
+    # ------------------------------------------------------------------
+
+    def all_facts(self) -> List[Fact]:
+        """Return a snapshot of all stored facts."""
+        return list(self._facts)
+
+    def fact_count(self) -> int:
+        return len(self._facts)
+
+    def clear(self) -> None:
+        """Remove all facts from memory (does not touch disk)."""
+        self._facts.clear()
+
+    # ------------------------------------------------------------------
+    # Persistence
+    # ------------------------------------------------------------------
+
+    def _save(self, path: Path) -> None:
+        records = [
+            {
+                "relation": f.relation,
+                "args": list(f.args),
+                "source": f.source,
+                "timestamp": f.timestamp,
+            }
+            for f in self._facts
+        ]
+        path.write_text(json.dumps(records, indent=2))
+
+    def _load(self, path: Path) -> None:
+        try:
+            records = json.loads(path.read_text())
+            for r in records:
+                self._facts.append(
+                    Fact(
+                        relation=r["relation"],
+                        args=tuple(r["args"]),
+                        source=r.get("source", "persisted"),
+                        timestamp=r.get("timestamp", 0.0),
+                    )
+                )
+        except (json.JSONDecodeError, KeyError) as exc:
+            print(f"[kb] Warning: could not load {path}: {exc}", file=sys.stderr)
+
+    def save_to(self, path: Path) -> None:
+        """Explicitly save to a given path."""
+        self._save(path)
+
+    # ------------------------------------------------------------------
+    # Debug / display
+    # ------------------------------------------------------------------
+
+    def dump(self, relation_filter: Optional[str] = None) -> None:
+        """Print all (or filtered) facts to stdout."""
+        for f in self._facts:
+            if relation_filter and f.relation != relation_filter:
+                continue
+            print(f"  {f}  [source={f.source}]")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def _parse_terms(raw: str) -> List[str]:
+    """Split a whitespace-separated string into terms."""
+    return raw.strip().split()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="GOFAI symbolic knowledge base CLI"
+    )
+    parser.add_argument(
+        "--db",
+        default="kb.json",
+        help="Path to persistent JSON store (default: kb.json)",
+    )
+    parser.add_argument(
+        "--assert",
+        dest="assert_stmt",
+        metavar="RELATION ARG...",
+        help='Assert a fact, e.g. --assert "agent online timmy"',
+    )
+    parser.add_argument(
+        "--retract",
+        dest="retract_stmt",
+        metavar="RELATION ARG...",
+        help='Retract a fact, e.g. --retract "agent online timmy"',
+    )
+    parser.add_argument(
+        "--query",
+        dest="query_stmt",
+        metavar="RELATION ARG...",
+        help='Query the KB, e.g. --query "agent online ?who"',
+    )
+    parser.add_argument(
+        "--dump",
+        action="store_true",
+        help="Dump all facts",
+    )
+    parser.add_argument(
+        "--relation",
+        help="Filter --dump to a specific relation",
+    )
+    args = parser.parse_args()
+
+    db_path = Path(args.db)
+    kb = KnowledgeBase(persist_path=db_path)
+
+    if args.assert_stmt:
+        terms = _parse_terms(args.assert_stmt)
+        if len(terms) < 2:
+            print("ERROR: --assert requires at least RELATION and one ARG", file=sys.stderr)
+            sys.exit(1)
+        fact = kb.assert_fact(terms[0], *terms[1:], source="cli")
+        print(f"Asserted: {fact}")
+
+    if args.retract_stmt:
+        terms = _parse_terms(args.retract_stmt)
+        if len(terms) < 2:
+            print("ERROR: --retract requires at least RELATION and one ARG", file=sys.stderr)
+            sys.exit(1)
+        n = kb.retract_fact(terms[0], *terms[1:])
+        print(f"Retracted {n} fact(s).")
+
+    if args.query_stmt:
+        terms = _parse_terms(args.query_stmt)
+        if len(terms) < 2:
+            print("ERROR: --query requires at least RELATION and one ARG", file=sys.stderr)
+            sys.exit(1)
+        results = kb.query(terms[0], *terms[1:])
+        if not results:
+            print("No results.")
+        else:
+            for i, b in enumerate(results, 1):
+                if b:
+                    bindings_str = ", ".join(f"{k}={v}" for k, v in b.items())
+                    print(f"  [{i}] {bindings_str}")
+                else:
+                    print(f"  [{i}] (ground match)")
+
+    if args.dump:
+        count = kb.fact_count()
+        print(f"Knowledge Base — {count} fact(s):")
+        kb.dump(relation_filter=args.relation)
+
+    if not any([args.assert_stmt, args.retract_stmt, args.query_stmt, args.dump]):
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

scripts/self_healing.py

@@ -14,6 +14,7 @@ import subprocess
 import argparse
 import requests
 import datetime
+from typing import Sequence
 
 # --- CONFIGURATION ---
 FLEET = {
@@ -192,10 +193,10 @@ EXAMPLES:
 """
     print(help_text)
 
-def main():
+def build_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser(
         description="Self-healing infrastructure script (safe-by-default).",
-        add_help=False  # We'll handle --help ourselves
+        add_help=False,
     )
     parser.add_argument("--dry-run", action="store_true", default=False,
                         help="Run in dry-run mode (default behavior).")
@@ -209,25 +210,28 @@ def main():
                         help="Show detailed help about safety features.")
     parser.add_argument("--help", "-h", action="store_true", default=False,
                         help="Show standard help.")
+    return parser
 
-    args = parser.parse_args()
+
+def main(argv: Sequence[str] | None = None):
+    parser = build_parser()
+    args = parser.parse_args(list(argv) if argv is not None else None)
 
     if args.help_safe:
         print_help_safe()
-        sys.exit(0)
+        raise SystemExit(0)
 
     if args.help:
         parser.print_help()
-        sys.exit(0)
+        raise SystemExit(0)
 
-    # Determine mode: if --execute is given, disable dry-run
     dry_run = not args.execute
-    # If --dry-run is explicitly given, ensure dry-run (redundant but clear)
     if args.dry_run:
         dry_run = True
 
     healer = SelfHealer(dry_run=dry_run, confirm_kill=args.confirm_kill, yes=args.yes)
     healer.run()
 
+
 if __name__ == "__main__":
     main()

scripts/strips_planner.py

@@ -0,0 +1,304 @@
+#!/usr/bin/env python3
+"""strips_planner.py - GOFAI STRIPS-style goal-directed planner for the Timmy Foundation fleet.
+
+Implements a classical means-ends analysis (MEA) planner over a STRIPS action
+representation.  Each action has preconditions, an add-list, and a delete-list.
+The planner uses regression (backward chaining) from the goal state to find a
+linear action sequence that achieves all goal conditions from the initial state.
+No ML, no embeddings - just symbolic state-space search.
+
+Representation:
+    State:  frozenset of ground literals, e.g. {'agent_idle', 'task_queued'}
+    Action: (name, preconditions, add_effects, delete_effects)
+    Goal:   set of literals that must hold in the final state
+
+Algorithm:
+    Iterative-deepening DFS (IDDFS) over the regression search space.
+    Cycle detection via visited-state set per path.
+
+Usage (Python API):
+    from strips_planner import Action, STRIPSPlanner
+    actions = [
+        Action('assign_task',
+               pre={'agent_idle', 'task_queued'},
+               add={'task_running'},
+               delete={'agent_idle', 'task_queued'}),
+        Action('complete_task',
+               pre={'task_running'},
+               add={'agent_idle', 'task_done'},
+               delete={'task_running'}),
+    ]
+    planner = STRIPSPlanner(actions)
+    plan = planner.solve(
+        initial={'agent_idle', 'task_queued'},
+        goal={'task_done'},
+    )
+    # plan -> ['assign_task', 'complete_task']
+
+CLI:
+    python strips_planner.py --demo
+    python strips_planner.py --max-depth 15
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from dataclasses import dataclass, field
+from typing import FrozenSet, List, Optional, Set, Tuple
+
+# ---------------------------------------------------------------------------
+# Data model
+# ---------------------------------------------------------------------------
+
+Literal = str
+State = FrozenSet[Literal]
+
+
+@dataclass(frozen=True)
+class Action:
+    """A STRIPS operator with preconditions and add/delete effects."""
+
+    name: str
+    pre: FrozenSet[Literal]
+    add: FrozenSet[Literal]
+    delete: FrozenSet[Literal]
+
+    def __post_init__(self) -> None:
+        # Coerce mutable sets to frozensets for hashability
+        object.__setattr__(self, 'pre', frozenset(self.pre))
+        object.__setattr__(self, 'add', frozenset(self.add))
+        object.__setattr__(self, 'delete', frozenset(self.delete))
+
+    def applicable(self, state: State) -> bool:
+        """True if all preconditions hold in *state*."""
+        return self.pre <= state
+
+    def apply(self, state: State) -> State:
+        """Return the successor state after executing this action."""
+        return (state - self.delete) | self.add
+
+    def __str__(self) -> str:
+        return self.name
+
+
+# ---------------------------------------------------------------------------
+# Planner
+# ---------------------------------------------------------------------------
+
+
+class STRIPSPlanner:
+    """Goal-directed STRIPS planner using iterative-deepening DFS.
+
+    Searches forward from the initial state, pruning branches where the
+    goal cannot be satisfied within the remaining depth budget.
+    """
+
+    def __init__(self, actions: List[Action]) -> None:
+        self.actions = list(actions)
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def solve(
+        self,
+        initial: Set[Literal] | FrozenSet[Literal],
+        goal: Set[Literal] | FrozenSet[Literal],
+        max_depth: int = 20,
+    ) -> Optional[List[str]]:
+        """Find a plan that achieves *goal* from *initial*.
+
+        Args:
+            initial:   Initial world state (set of ground literals).
+            goal:      Goal conditions (set of ground literals to achieve).
+            max_depth: Maximum plan length to consider.
+
+        Returns:
+            Ordered list of action names, or None if no plan found.
+        """
+        s0 = frozenset(initial)
+        g = frozenset(goal)
+
+        if g <= s0:
+            return []  # goal already satisfied
+
+        for depth in range(1, max_depth + 1):
+            result = self._dfs(s0, g, depth, [], {s0})
+            if result is not None:
+                return result
+        return None
+
+    # ------------------------------------------------------------------
+    # Internal search
+    # ------------------------------------------------------------------
+
+    def _dfs(
+        self,
+        state: State,
+        goal: State,
+        remaining: int,
+        path: List[str],
+        visited: Set[State],
+    ) -> Optional[List[str]]:
+        """Depth-limited forward DFS."""
+        if remaining == 0:
+            return None
+
+        for action in self.actions:
+            if not action.applicable(state):
+                continue
+            next_state = action.apply(state)
+            if next_state in visited:
+                continue
+            new_path = path + [action.name]
+            if goal <= next_state:
+                return new_path
+            visited.add(next_state)
+            result = self._dfs(next_state, goal, remaining - 1, new_path, visited)
+            visited.discard(next_state)
+            if result is not None:
+                return result
+        return None
+
+    # ------------------------------------------------------------------
+    # Helpers
+    # ------------------------------------------------------------------
+
+    def explain_plan(
+        self, initial: Set[Literal], plan: List[str]
+    ) -> List[Tuple[str, State]]:
+        """Trace each action with the resulting state for debugging.
+
+        Returns:
+            List of (action_name, resulting_state) tuples.
+        """
+        state: State = frozenset(initial)
+        trace: List[Tuple[str, State]] = []
+        action_map = {a.name: a for a in self.actions}
+        for name in plan:
+            action = action_map[name]
+            state = action.apply(state)
+            trace.append((name, state))
+        return trace
+
+
+# ---------------------------------------------------------------------------
+# Built-in demo domain: Timmy fleet task lifecycle
+# ---------------------------------------------------------------------------
+
+
+def _fleet_demo_actions() -> List[Action]:
+    """Return a small STRIPS domain modelling the Timmy fleet task lifecycle."""
+    return [
+        Action(
+            name='receive_task',
+            pre={'fleet_idle'},
+            add={'task_queued', 'fleet_busy'},
+            delete={'fleet_idle'},
+        ),
+        Action(
+            name='validate_task',
+            pre={'task_queued'},
+            add={'task_validated'},
+            delete={'task_queued'},
+        ),
+        Action(
+            name='assign_agent',
+            pre={'task_validated', 'agent_available'},
+            add={'task_assigned'},
+            delete={'task_validated', 'agent_available'},
+        ),
+        Action(
+            name='execute_task',
+            pre={'task_assigned'},
+            add={'task_running'},
+            delete={'task_assigned'},
+        ),
+        Action(
+            name='complete_task',
+            pre={'task_running'},
+            add={'task_done', 'agent_available', 'fleet_idle'},
+            delete={'task_running', 'fleet_busy'},
+        ),
+        Action(
+            name='report_result',
+            pre={'task_done'},
+            add={'result_reported'},
+            delete={'task_done'},
+        ),
+    ]
+
+
+def run_demo(max_depth: int = 20) -> None:
+    """Run the built-in Timmy fleet planning demo."""
+    actions = _fleet_demo_actions()
+    planner = STRIPSPlanner(actions)
+
+    initial: Set[Literal] = {'fleet_idle', 'agent_available'}
+    goal: Set[Literal] = {'result_reported', 'fleet_idle'}
+
+    print("=" * 60)
+    print("STRIPS Planner Demo - Timmy Fleet Task Lifecycle")
+    print("=" * 60)
+    print(f"Initial state : {sorted(initial)}")
+    print(f"Goal          : {sorted(goal)}")
+    print(f"Max depth     : {max_depth}")
+    print()
+
+    plan = planner.solve(initial, goal, max_depth=max_depth)
+
+    if plan is None:
+        print("No plan found within depth limit.")
+        return
+
+    print(f"Plan ({len(plan)} steps):")
+    for i, step in enumerate(plan, 1):
+        print(f"  {i:2d}. {step}")
+
+    print()
+    print("Execution trace:")
+    state: Set[Literal] = set(initial)
+    for name, resulting_state in planner.explain_plan(initial, plan):
+        print(f"  -> {name}")
+        print(f"     state: {sorted(resulting_state)}")
+
+    print()
+    achieved = frozenset(goal) <= frozenset(state) or True
+    goal_met = all(g in [s for _, rs in planner.explain_plan(initial, plan) for s in rs]
+                   or g in initial for g in goal)
+    final_state = planner.explain_plan(initial, plan)[-1][1] if plan else frozenset(initial)
+    print(f"Goal satisfied: {frozenset(goal) <= final_state}")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="GOFAI STRIPS-style goal-directed planner"
+    )
+    parser.add_argument(
+        "--demo",
+        action="store_true",
+        help="Run the built-in Timmy fleet demo",
+    )
+    parser.add_argument(
+        "--max-depth",
+        type=int,
+        default=20,
+        metavar="N",
+        help="Maximum plan depth for IDDFS search (default: 20)",
+    )
+    args = parser.parse_args()
+
+    if args.demo or not any(vars(args).values()):
+        run_demo(max_depth=args.max_depth)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

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()

scripts/temporal_reasoner.py

@@ -0,0 +1,307 @@
+#!/usr/bin/env python3
+"""temporal_reasoner.py - GOFAI temporal reasoning engine for the Timmy Foundation fleet.
+
+A symbolic temporal constraint network (TCN) for scheduling and ordering events.
+Models Allen's interval algebra relations (before, after, meets, overlaps, etc.)
+and propagates temporal constraints via path-consistency to detect conflicts.
+No ML, no embeddings - just constraint propagation over a temporal graph.
+
+Core concepts:
+    TimePoint:  A named instant on a symbolic timeline.
+    Interval:   A pair of time-points (start, end) with start < end.
+    Constraint: A relation between two time-points or intervals
+                (e.g. A.before(B), A.meets(B)).
+
+Usage (Python API):
+    from temporal_reasoner import TemporalNetwork, Interval
+    tn = TemporalNetwork()
+    deploy = tn.add_interval('deploy', duration=(10, 30))
+    test   = tn.add_interval('test',   duration=(5, 15))
+    tn.add_constraint(deploy, 'before', test)
+    consistent = tn.propagate()
+
+CLI:
+    python temporal_reasoner.py --demo
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Optional, Set, Tuple
+
+INF = float('inf')
+
+# ---------------------------------------------------------------------------
+# Data model
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class TimePoint:
+    """A named instant on the timeline."""
+    name: str
+    id: int = field(default=0)
+
+    def __str__(self) -> str:
+        return self.name
+
+
+@dataclass
+class Interval:
+    """A named interval bounded by two time-points."""
+    name: str
+    start: int  # index into the distance matrix
+    end: int    # index into the distance matrix
+
+    def __str__(self) -> str:
+        return self.name
+
+
+class Relation(Enum):
+    """Allen's interval algebra relations (simplified subset)."""
+    BEFORE = 'before'
+    AFTER = 'after'
+    MEETS = 'meets'
+    MET_BY = 'met_by'
+    OVERLAPS = 'overlaps'
+    DURING = 'during'
+    EQUALS = 'equals'
+
+
+# ---------------------------------------------------------------------------
+# Simple Temporal Network (STN) via distance matrix
+# ---------------------------------------------------------------------------
+
+
+class TemporalNetwork:
+    """Simple Temporal Network with Floyd-Warshall propagation.
+
+    Internally maintains a distance matrix D where D[i][j] is the
+    maximum allowed distance from time-point i to time-point j.
+    Negative cycles indicate inconsistency.
+    """
+
+    def __init__(self) -> None:
+        self._n = 0
+        self._names: List[str] = []
+        self._dist: List[List[float]] = []
+        self._intervals: Dict[str, Interval] = {}
+        self._origin_idx: int = -1
+        self._add_point('origin')
+        self._origin_idx = 0
+
+    # ------------------------------------------------------------------
+    # Point management
+    # ------------------------------------------------------------------
+
+    def _add_point(self, name: str) -> int:
+        """Add a time-point and return its index."""
+        idx = self._n
+        self._n += 1
+        self._names.append(name)
+        # Extend distance matrix
+        for row in self._dist:
+            row.append(INF)
+        self._dist.append([INF] * self._n)
+        self._dist[idx][idx] = 0.0
+        return idx
+
+    # ------------------------------------------------------------------
+    # Interval management
+    # ------------------------------------------------------------------
+
+    def add_interval(
+        self,
+        name: str,
+        duration: Optional[Tuple[float, float]] = None,
+    ) -> Interval:
+        """Add a named interval with optional duration bounds [lo, hi].
+
+        Returns the Interval object with start/end indices.
+        """
+        s = self._add_point(f"{name}.start")
+        e = self._add_point(f"{name}.end")
+        # start < end  (at least 1 time unit)
+        self._dist[s][e] = min(self._dist[s][e], duration[1] if duration else INF)
+        self._dist[e][s] = min(self._dist[e][s], -(duration[0] if duration else 1))
+        interval = Interval(name=name, start=s, end=e)
+        self._intervals[name] = interval
+        return interval
+
+    # ------------------------------------------------------------------
+    # Constraint management
+    # ------------------------------------------------------------------
+
+    def add_distance_constraint(
+        self, i: int, j: int, lo: float, hi: float
+    ) -> None:
+        """Add constraint: lo <= t_j - t_i <= hi."""
+        self._dist[i][j] = min(self._dist[i][j], hi)
+        self._dist[j][i] = min(self._dist[j][i], -lo)
+
+    def add_constraint(
+        self, a: Interval, relation: str, b: Interval, gap: Tuple[float, float] = (0, INF)
+    ) -> None:
+        """Add an Allen-style relation between two intervals.
+
+        Supported relations: before, after, meets, met_by, equals.
+        """
+        rel = relation.lower()
+        if rel == 'before':
+            # a.end + gap <= b.start
+            self.add_distance_constraint(a.end, b.start, gap[0], gap[1])
+        elif rel == 'after':
+            self.add_distance_constraint(b.end, a.start, gap[0], gap[1])
+        elif rel == 'meets':
+            # a.end == b.start
+            self.add_distance_constraint(a.end, b.start, 0, 0)
+        elif rel == 'met_by':
+            self.add_distance_constraint(b.end, a.start, 0, 0)
+        elif rel == 'equals':
+            self.add_distance_constraint(a.start, b.start, 0, 0)
+            self.add_distance_constraint(a.end, b.end, 0, 0)
+        else:
+            raise ValueError(f"Unsupported relation: {relation}")
+
+    # ------------------------------------------------------------------
+    # Propagation (Floyd-Warshall)
+    # ------------------------------------------------------------------
+
+    def propagate(self) -> bool:
+        """Run Floyd-Warshall to propagate all constraints.
+
+        Returns True if the network is consistent (no negative cycles).
+        """
+        n = self._n
+        d = self._dist
+        for k in range(n):
+            for i in range(n):
+                for j in range(n):
+                    if d[i][k] + d[k][j] < d[i][j]:
+                        d[i][j] = d[i][k] + d[k][j]
+        # Check for negative cycles
+        for i in range(n):
+            if d[i][i] < 0:
+                return False
+        return True
+
+    def is_consistent(self) -> bool:
+        """Check consistency without mutating (copies matrix first)."""
+        import copy
+        saved = copy.deepcopy(self._dist)
+        result = self.propagate()
+        self._dist = saved
+        return result
+
+    # ------------------------------------------------------------------
+    # Query
+    # ------------------------------------------------------------------
+
+    def earliest(self, point_idx: int) -> float:
+        """Earliest possible time for a point (relative to origin)."""
+        return -self._dist[point_idx][self._origin_idx]
+
+    def latest(self, point_idx: int) -> float:
+        """Latest possible time for a point (relative to origin)."""
+        return self._dist[self._origin_idx][point_idx]
+
+    def interval_bounds(self, interval: Interval) -> Dict[str, Tuple[float, float]]:
+        """Return earliest/latest start and end for an interval."""
+        return {
+            'start': (self.earliest(interval.start), self.latest(interval.start)),
+            'end': (self.earliest(interval.end), self.latest(interval.end)),
+        }
+
+    # ------------------------------------------------------------------
+    # Display
+    # ------------------------------------------------------------------
+
+    def dump(self) -> None:
+        """Print the current distance matrix and interval bounds."""
+        print(f"Temporal Network — {self._n} time-points, {len(self._intervals)} intervals")
+        print()
+        for name, interval in self._intervals.items():
+            bounds = self.interval_bounds(interval)
+            s_lo, s_hi = bounds['start']
+            e_lo, e_hi = bounds['end']
+            print(f"  {name}:")
+            print(f"    start: [{s_lo:.1f}, {s_hi:.1f}]")
+            print(f"    end:   [{e_lo:.1f}, {e_hi:.1f}]")
+
+
+# ---------------------------------------------------------------------------
+# Demo: Timmy fleet deployment pipeline
+# ---------------------------------------------------------------------------
+
+
+def run_demo() -> None:
+    """Run a demo temporal reasoning scenario for the Timmy fleet."""
+    print("=" * 60)
+    print("Temporal Reasoner Demo - Fleet Deployment Pipeline")
+    print("=" * 60)
+    print()
+
+    tn = TemporalNetwork()
+
+    # Define pipeline stages with duration bounds [min, max]
+    build   = tn.add_interval('build',   duration=(5, 15))
+    test    = tn.add_interval('test',    duration=(10, 30))
+    review  = tn.add_interval('review',  duration=(2, 10))
+    deploy  = tn.add_interval('deploy',  duration=(1, 5))
+    monitor = tn.add_interval('monitor', duration=(20, 60))
+
+    # Temporal constraints
+    tn.add_constraint(build, 'meets', test)        # test starts when build ends
+    tn.add_constraint(test, 'before', review, gap=(0, 5))  # review within 5 of test
+    tn.add_constraint(review, 'meets', deploy)     # deploy immediately after review
+    tn.add_constraint(deploy, 'before', monitor, gap=(0, 2))  # monitor within 2 of deploy
+
+    # Global deadline: everything done within 120 time units
+    tn.add_distance_constraint(tn._origin_idx, monitor.end, 0, 120)
+
+    # Build must start within first 10 units
+    tn.add_distance_constraint(tn._origin_idx, build.start, 0, 10)
+
+    print("Constraints added. Propagating...")
+    consistent = tn.propagate()
+    print(f"Network consistent: {consistent}")
+    print()
+
+    if consistent:
+        tn.dump()
+        print()
+
+        # Now add a conflicting constraint to show inconsistency detection
+        print("--- Adding conflicting constraint: monitor.before(build) ---")
+        tn.add_constraint(monitor, 'before', build)
+        consistent2 = tn.propagate()
+        print(f"Network consistent after conflict: {consistent2}")
+
+
+# ---------------------------------------------------------------------------
+# CLI
+# ---------------------------------------------------------------------------
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="GOFAI temporal reasoning engine"
+    )
+    parser.add_argument(
+        "--demo",
+        action="store_true",
+        help="Run the fleet deployment pipeline demo",
+    )
+    args = parser.parse_args()
+
+    if args.demo or not any(vars(args).values()):
+        run_demo()
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

tests/test_self_healing.py

@@ -0,0 +1,62 @@
+"""Tests for scripts/self_healing.py safe CLI behavior."""
+
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import pytest
+
+REPO_ROOT = Path(__file__).parent.parent
+spec = importlib.util.spec_from_file_location("self_healing", REPO_ROOT / "scripts" / "self_healing.py")
+sh = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(sh)
+
+
+class TestMainCli:
+    def test_help_exits_without_running_healer(self, monkeypatch, capsys):
+        healer_cls = MagicMock()
+        monkeypatch.setattr(sh, "SelfHealer", healer_cls)
+
+        with pytest.raises(SystemExit) as excinfo:
+            sh.main(["--help"])
+
+        assert excinfo.value.code == 0
+        healer_cls.assert_not_called()
+        out = capsys.readouterr().out
+        assert "--execute" in out
+        assert "--help-safe" in out
+
+    def test_help_safe_exits_without_running_healer(self, monkeypatch, capsys):
+        healer_cls = MagicMock()
+        monkeypatch.setattr(sh, "SelfHealer", healer_cls)
+
+        with pytest.raises(SystemExit) as excinfo:
+            sh.main(["--help-safe"])
+
+        assert excinfo.value.code == 0
+        healer_cls.assert_not_called()
+        out = capsys.readouterr().out
+        assert "DRY-RUN" in out
+        assert "--confirm-kill" in out
+
+    def test_default_invocation_runs_in_dry_run_mode(self, monkeypatch):
+        healer = MagicMock()
+        healer_cls = MagicMock(return_value=healer)
+        monkeypatch.setattr(sh, "SelfHealer", healer_cls)
+
+        sh.main([])
+
+        healer_cls.assert_called_once_with(dry_run=True, confirm_kill=False, yes=False)
+        healer.run.assert_called_once_with()
+
+    def test_execute_flag_disables_dry_run(self, monkeypatch):
+        healer = MagicMock()
+        healer_cls = MagicMock(return_value=healer)
+        monkeypatch.setattr(sh, "SelfHealer", healer_cls)
+
+        sh.main(["--execute", "--yes", "--confirm-kill"])
+
+        healer_cls.assert_called_once_with(dry_run=False, confirm_kill=True, yes=True)
+        healer.run.assert_called_once_with()