feat: integrate Ultraplan with tools and core toolsets for #840

Refs #840

hermes_cli/tools_config.py

@@ -57,7 +57,7 @@ CONFIGURABLE_TOOLSETS = [
     ("moa",             "🧠 Mixture of Agents",         "mixture_of_agents"),
     ("tts",             "🔊 Text-to-Speech",            "text_to_speech"),
     ("skills",          "📚 Skills",                    "list, view, manage"),
-    ("todo",            "📋 Task Planning",             "todo"),
+    ("todo",            "📋 Task Planning",             "todo, ultraplan"),
     ("memory",          "💾 Memory",                    "persistent memory across sessions"),
     ("session_search",  "🔎 Session Search",            "search past conversations"),
     ("clarify",         "❓ Clarifying Questions",      "clarify"),

plugins/memory/holographic/__init__.py

@@ -26,7 +26,6 @@ from agent.memory_provider import MemoryProvider
 from tools.registry import tool_error
 from .store import MemoryStore
 from .retrieval import FactRetriever
-from .observations import ObservationSynthesizer
 
 logger = logging.getLogger(__name__)
 
@@ -38,29 +37,28 @@ logger = logging.getLogger(__name__)
 FACT_STORE_SCHEMA = {
     "name": "fact_store",
     "description": (
-        "Deep structured memory with algebraic reasoning and grounded observation synthesis. "
+        "Deep structured memory with algebraic reasoning. "
         "Use alongside the memory tool — memory for always-on context, "
-        "fact_store for deep recall, compositional queries, and higher-order observations.\n\n"
+        "fact_store for deep recall and compositional queries.\n\n"
         "ACTIONS (simple → powerful):\n"
         "• add — Store a fact the user would expect you to remember.\n"
         "• search — Keyword lookup ('editor config', 'deploy process').\n"
         "• probe — Entity recall: ALL facts about a person/thing.\n"
         "• related — What connects to an entity? Structural adjacency.\n"
         "• reason — Compositional: facts connected to MULTIPLE entities simultaneously.\n"
-        "• observe — Synthesized higher-order observations backed by supporting facts.\n"
         "• contradict — Memory hygiene: find facts making conflicting claims.\n"
         "• update/remove/list — CRUD operations.\n\n"
-        "IMPORTANT: Before answering questions about the user, ALWAYS probe/reason/observe first."
+        "IMPORTANT: Before answering questions about the user, ALWAYS probe or reason first."
     ),
     "parameters": {
         "type": "object",
         "properties": {
             "action": {
                 "type": "string",
-                "enum": ["add", "search", "probe", "related", "reason", "observe", "contradict", "update", "remove", "list"],
+                "enum": ["add", "search", "probe", "related", "reason", "contradict", "update", "remove", "list"],
             },
             "content": {"type": "string", "description": "Fact content (required for 'add')."},
-            "query": {"type": "string", "description": "Search query (required for 'search'/'observe')."},
+            "query": {"type": "string", "description": "Search query (required for 'search')."},
             "entity": {"type": "string", "description": "Entity name for 'probe'/'related'."},
             "entities": {"type": "array", "items": {"type": "string"}, "description": "Entity names for 'reason'."},
             "fact_id": {"type": "integer", "description": "Fact ID for 'update'/'remove'."},
@@ -68,12 +66,6 @@ FACT_STORE_SCHEMA = {
             "tags": {"type": "string", "description": "Comma-separated tags."},
             "trust_delta": {"type": "number", "description": "Trust adjustment for 'update'."},
             "min_trust": {"type": "number", "description": "Minimum trust filter (default: 0.3)."},
-            "min_confidence": {"type": "number", "description": "Minimum observation confidence (default: 0.6)."},
-            "observation_type": {
-                "type": "string",
-                "enum": ["recurring_preference", "stable_direction", "behavioral_pattern"],
-                "description": "Optional observation type filter for 'observe'.",
-            },
             "limit": {"type": "integer", "description": "Max results (default: 10)."},
         },
         "required": ["action"],
@@ -126,9 +118,7 @@ class HolographicMemoryProvider(MemoryProvider):
         self._config = config or _load_plugin_config()
         self._store = None
         self._retriever = None
-        self._observation_synth = None
         self._min_trust = float(self._config.get("min_trust_threshold", 0.3))
-        self._observation_min_confidence = float(self._config.get("observation_min_confidence", 0.6))
 
     @property
     def name(self) -> str:
@@ -187,7 +177,6 @@ class HolographicMemoryProvider(MemoryProvider):
             hrr_weight=hrr_weight,
             hrr_dim=hrr_dim,
         )
-        self._observation_synth = ObservationSynthesizer(self._store)
         self._session_id = session_id
 
     def system_prompt_block(self) -> str:
@@ -204,76 +193,30 @@ class HolographicMemoryProvider(MemoryProvider):
                 "# Holographic Memory\n"
                 "Active. Empty fact store — proactively add facts the user would expect you to remember.\n"
                 "Use fact_store(action='add') to store durable structured facts about people, projects, preferences, decisions.\n"
-                "Use fact_store(action='observe') to synthesize higher-order observations with evidence.\n"
                 "Use fact_feedback to rate facts after using them (trains trust scores)."
             )
         return (
             f"# Holographic Memory\n"
             f"Active. {total} facts stored with entity resolution and trust scoring.\n"
-            f"Use fact_store to search, probe entities, reason across entities, or synthesize observations.\n"
+            f"Use fact_store to search, probe entities, reason across entities, or add facts.\n"
             f"Use fact_feedback to rate facts after using them (trains trust scores)."
         )
 
     def prefetch(self, query: str, *, session_id: str = "") -> str:
-        if not query:
+        if not self._retriever or not query:
             return ""
-
-        parts = []
-        raw_results = []
         try:
-            if self._retriever:
-                raw_results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
-        except Exception as e:
-            logger.debug("Holographic prefetch fact search failed: %s", e)
-            raw_results = []
-
-        observations = []
-        try:
-            if self._observation_synth:
-                observations = self._observation_synth.observe(
-                    query,
-                    min_confidence=self._observation_min_confidence,
-                    limit=3,
-                    refresh=True,
-                )
-        except Exception as e:
-            logger.debug("Holographic prefetch observation search failed: %s", e)
-            observations = []
-
-        if not raw_results and observations:
-            seen_fact_ids = set()
-            evidence_backfill = []
-            for observation in observations:
-                for evidence in observation.get("evidence", []):
-                    fact_id = evidence.get("fact_id")
-                    if fact_id in seen_fact_ids:
-                        continue
-                    seen_fact_ids.add(fact_id)
-                    evidence_backfill.append(evidence)
-            raw_results = evidence_backfill[:5]
-
-        if raw_results:
+            results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
+            if not results:
+                return ""
             lines = []
-            for r in raw_results:
+            for r in results:
                 trust = r.get("trust_score", r.get("trust", 0))
                 lines.append(f"- [{trust:.1f}] {r.get('content', '')}")
-            parts.append("## Holographic Memory\n" + "\n".join(lines))
-
-        if observations:
-            lines = []
-            for observation in observations:
-                evidence_ids = ", ".join(
-                    f"#{item['fact_id']}" for item in observation.get("evidence", [])[:3]
-                ) or "none"
-                lines.append(
-                    f"- [{observation.get('confidence', 0.0):.2f}] "
-                    f"{observation.get('observation_type', 'observation')}: "
-                    f"{observation.get('summary', '')} "
-                    f"(evidence: {evidence_ids})"
-                )
-            parts.append("## Holographic Observations\n" + "\n".join(lines))
-
-        return "\n\n".join(parts)
+            return "## Holographic Memory\n" + "\n".join(lines)
+        except Exception as e:
+            logger.debug("Holographic prefetch failed: %s", e)
+            return ""
 
     def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
         # Holographic memory stores explicit facts via tools, not auto-sync.
@@ -309,7 +252,6 @@ class HolographicMemoryProvider(MemoryProvider):
     def shutdown(self) -> None:
         self._store = None
         self._retriever = None
-        self._observation_synth = None
 
     # -- Tool handlers -------------------------------------------------------
 
@@ -363,19 +305,6 @@ class HolographicMemoryProvider(MemoryProvider):
                 )
                 return json.dumps({"results": results, "count": len(results)})
 
-            elif action == "observe":
-                synthesizer = self._observation_synth
-                if not synthesizer:
-                    return tool_error("Observation synthesizer is not initialized")
-                observations = synthesizer.observe(
-                    args.get("query", ""),
-                    observation_type=args.get("observation_type"),
-                    min_confidence=float(args.get("min_confidence", self._observation_min_confidence)),
-                    limit=int(args.get("limit", 10)),
-                    refresh=True,
-                )
-                return json.dumps({"observations": observations, "count": len(observations)})
-
             elif action == "contradict":
                 results = retriever.contradict(
                     category=args.get("category"),

plugins/memory/holographic/observations.py

@@ -1,249 +0,0 @@
-"""Higher-order observation synthesis for holographic memory.
-
-Builds grounded observations from accumulated facts and keeps them in a
-separate retrieval layer with explicit evidence links back to supporting facts.
-"""
-
-from __future__ import annotations
-
-import re
-from typing import Any
-
-from .store import MemoryStore
-
-_TOKEN_RE = re.compile(r"[a-z0-9_]+")
-_HIGHER_ORDER_CUES = {
-    "prefer",
-    "preference",
-    "preferences",
-    "style",
-    "pattern",
-    "patterns",
-    "behavior",
-    "behaviour",
-    "habit",
-    "habits",
-    "workflow",
-    "direction",
-    "trajectory",
-    "strategy",
-    "tend",
-    "usually",
-}
-
-_OBSERVATION_PATTERNS = [
-    {
-        "observation_type": "recurring_preference",
-        "subject": "communication_style",
-        "categories": {"user_pref", "general"},
-        "labels": {
-            "concise": ["concise", "terse", "brief", "short", "no fluff"],
-            "result_first": ["result-only", "result only", "outcome only", "quick", "quickly"],
-            "silent_ops": ["silent", "no status", "no repetitive status", "no questions"],
-        },
-        "summary_prefix": "Recurring preference",
-    },
-    {
-        "observation_type": "stable_direction",
-        "subject": "project_direction",
-        "categories": {"project", "general", "tool"},
-        "labels": {
-            "local_first": ["local-first", "local first", "local-only", "local only", "ollama", "own hardware"],
-            "gitea_first": ["gitea-first", "gitea first", "forge", "pull request", "pr flow", "issue flow"],
-            "ansible": ["ansible", "playbook", "role", "deploy via ansible"],
-        },
-        "summary_prefix": "Stable direction",
-    },
-    {
-        "observation_type": "behavioral_pattern",
-        "subject": "operator_workflow",
-        "categories": {"general", "project", "tool", "user_pref"},
-        "labels": {
-            "commit_early": ["commit early", "commits early", "commit after", "wip commit"],
-            "pr_first": ["open pr", "push a pr", "pull request", "pr immediately", "create pr"],
-            "dedup_guard": ["no dupes", "no duplicates", "avoid duplicate", "existing pr"],
-        },
-        "summary_prefix": "Behavioral pattern",
-    },
-]
-
-_TYPE_QUERY_HINTS = {
-    "recurring_preference": {"prefer", "preference", "style", "communication", "likes", "wants"},
-    "stable_direction": {"direction", "trajectory", "strategy", "project", "roadmap", "moving"},
-    "behavioral_pattern": {"pattern", "behavior", "workflow", "habit", "operator", "agent", "usually"},
-}
-
-
-class ObservationSynthesizer:
-    """Synthesizes grounded observations from facts and retrieves them by query."""
-
-    def __init__(self, store: MemoryStore):
-        self.store = store
-
-    def synthesize(
-        self,
-        *,
-        persist: bool = True,
-        min_confidence: float = 0.6,
-        limit: int = 10,
-    ) -> list[dict[str, Any]]:
-        facts = self.store.list_facts(min_trust=0.0, limit=1000)
-        observations: list[dict[str, Any]] = []
-
-        for pattern in _OBSERVATION_PATTERNS:
-            candidate = self._build_candidate(pattern, facts, min_confidence=min_confidence)
-            if not candidate:
-                continue
-
-            if persist:
-                candidate["observation_id"] = self.store.upsert_observation(
-                    candidate["observation_type"],
-                    candidate["subject"],
-                    candidate["summary"],
-                    candidate["confidence"],
-                    candidate["evidence_fact_ids"],
-                    metadata=candidate["metadata"],
-                )
-
-            candidate["evidence"] = self._expand_evidence(candidate["evidence_fact_ids"])
-            candidate["evidence_count"] = len(candidate["evidence"])
-            candidate.pop("evidence_fact_ids", None)
-            observations.append(candidate)
-
-        observations.sort(
-            key=lambda item: (item["confidence"], item.get("evidence_count", 0)),
-            reverse=True,
-        )
-        return observations[:limit]
-
-    def observe(
-        self,
-        query: str = "",
-        *,
-        observation_type: str | None = None,
-        min_confidence: float = 0.6,
-        limit: int = 10,
-        refresh: bool = True,
-    ) -> list[dict[str, Any]]:
-        if refresh:
-            self.synthesize(persist=True, min_confidence=min_confidence, limit=limit)
-
-        observations = self.store.list_observations(
-            observation_type=observation_type,
-            min_confidence=min_confidence,
-            limit=max(limit * 4, 20),
-        )
-        if not observations:
-            return []
-
-        if not query:
-            return observations[:limit]
-
-        query_tokens = self._tokenize(query)
-        is_higher_order = bool(query_tokens & _HIGHER_ORDER_CUES)
-        ranked: list[dict[str, Any]] = []
-
-        for item in observations:
-            searchable = " ".join(
-                [
-                    item.get("summary", ""),
-                    item.get("subject", ""),
-                    item.get("observation_type", ""),
-                    " ".join(item.get("metadata", {}).get("labels", [])),
-                ]
-            )
-            overlap = self._overlap_score(query_tokens, self._tokenize(searchable))
-            type_bonus = self._type_bonus(query_tokens, item.get("observation_type", ""))
-            if overlap <= 0 and type_bonus <= 0 and not is_higher_order:
-                continue
-            ranked_item = dict(item)
-            ranked_item["score"] = round(item.get("confidence", 0.0) + overlap + type_bonus, 3)
-            ranked.append(ranked_item)
-
-        if not ranked and is_higher_order:
-            ranked = [
-                {**item, "score": round(float(item.get("confidence", 0.0)), 3)}
-                for item in observations
-            ]
-
-        ranked.sort(
-            key=lambda item: (item.get("score", 0.0), item.get("confidence", 0.0), item.get("evidence_count", 0)),
-            reverse=True,
-        )
-        return ranked[:limit]
-
-    def _build_candidate(
-        self,
-        pattern: dict[str, Any],
-        facts: list[dict[str, Any]],
-        *,
-        min_confidence: float,
-    ) -> dict[str, Any] | None:
-        matched_fact_ids: set[int] = set()
-        matched_labels: dict[str, set[int]] = {label: set() for label in pattern["labels"]}
-
-        for fact in facts:
-            if fact.get("category") not in pattern["categories"]:
-                continue
-            haystack = f"{fact.get('content', '')} {fact.get('tags', '')}".lower()
-            local_match = False
-            for label, keywords in pattern["labels"].items():
-                if any(keyword in haystack for keyword in keywords):
-                    matched_labels[label].add(int(fact["fact_id"]))
-                    local_match = True
-            if local_match:
-                matched_fact_ids.add(int(fact["fact_id"]))
-
-        if len(matched_fact_ids) < 2:
-            return None
-
-        active_labels = sorted(label for label, ids in matched_labels.items() if ids)
-        confidence = min(0.95, 0.35 + 0.12 * len(matched_fact_ids) + 0.08 * len(active_labels))
-        confidence = round(confidence, 3)
-        if confidence < min_confidence:
-            return None
-
-        label_summary = ", ".join(label.replace("_", "-") for label in active_labels)
-        subject_text = pattern["subject"].replace("_", " ")
-        summary = (
-            f"{pattern['summary_prefix']}: {subject_text} trends toward {label_summary} "
-            f"based on {len(matched_fact_ids)} supporting facts."
-        )
-        return {
-            "observation_type": pattern["observation_type"],
-            "subject": pattern["subject"],
-            "summary": summary,
-            "confidence": confidence,
-            "metadata": {
-                "labels": active_labels,
-                "evidence_count": len(matched_fact_ids),
-            },
-            "evidence_fact_ids": sorted(matched_fact_ids),
-        }
-
-    def _expand_evidence(self, fact_ids: list[int]) -> list[dict[str, Any]]:
-        facts_by_id = {
-            fact["fact_id"]: fact
-            for fact in self.store.list_facts(min_trust=0.0, limit=1000)
-        }
-        return [facts_by_id[fact_id] for fact_id in fact_ids if fact_id in facts_by_id]
-
-    @staticmethod
-    def _tokenize(text: str) -> set[str]:
-        return set(_TOKEN_RE.findall(text.lower()))
-
-    @staticmethod
-    def _overlap_score(query_tokens: set[str], text_tokens: set[str]) -> float:
-        if not query_tokens or not text_tokens:
-            return 0.0
-        overlap = query_tokens & text_tokens
-        if not overlap:
-            return 0.0
-        return round(len(overlap) / max(len(query_tokens), 1), 3)
-
-    @staticmethod
-    def _type_bonus(query_tokens: set[str], observation_type: str) -> float:
-        hints = _TYPE_QUERY_HINTS.get(observation_type, set())
-        if not hints:
-            return 0.0
-        return 0.25 if query_tokens & hints else 0.0

plugins/memory/holographic/store.py

@@ -3,7 +3,6 @@ SQLite-backed fact store with entity resolution and trust scoring.
 Single-user Hermes memory store plugin.
 """
 
-import json
 import re
 import sqlite3
 import threading
@@ -74,28 +73,6 @@ CREATE TABLE IF NOT EXISTS memory_banks (
     fact_count INTEGER DEFAULT 0,
     updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
 );
-
-CREATE TABLE IF NOT EXISTS observations (
-    observation_id   INTEGER PRIMARY KEY AUTOINCREMENT,
-    observation_type TEXT NOT NULL,
-    subject          TEXT NOT NULL,
-    summary          TEXT NOT NULL,
-    confidence       REAL DEFAULT 0.0,
-    metadata_json    TEXT DEFAULT '{}',
-    created_at       TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-    updated_at       TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-    UNIQUE(observation_type, subject)
-);
-
-CREATE TABLE IF NOT EXISTS observation_evidence (
-    observation_id INTEGER REFERENCES observations(observation_id) ON DELETE CASCADE,
-    fact_id        INTEGER REFERENCES facts(fact_id) ON DELETE CASCADE,
-    evidence_weight REAL DEFAULT 1.0,
-    PRIMARY KEY (observation_id, fact_id)
-);
-
-CREATE INDEX IF NOT EXISTS idx_observations_type ON observations(observation_type);
-CREATE INDEX IF NOT EXISTS idx_observations_confidence ON observations(confidence DESC);
 """
 
 # Trust adjustment constants
@@ -151,7 +128,6 @@ class MemoryStore:
     def _init_db(self) -> None:
         """Create tables, indexes, and triggers if they do not exist. Enable WAL mode."""
         self._conn.execute("PRAGMA journal_mode=WAL")
-        self._conn.execute("PRAGMA foreign_keys=ON")
         self._conn.executescript(_SCHEMA)
         # Migrate: add hrr_vector column if missing (safe for existing databases)
         columns = {row[1] for row in self._conn.execute("PRAGMA table_info(facts)").fetchall()}
@@ -370,115 +346,6 @@ class MemoryStore:
             rows = self._conn.execute(sql, params).fetchall()
             return [self._row_to_dict(r) for r in rows]
 
-    def upsert_observation(
-        self,
-        observation_type: str,
-        subject: str,
-        summary: str,
-        confidence: float,
-        evidence_fact_ids: list[int],
-        metadata: dict | None = None,
-    ) -> int:
-        """Create or update a synthesized observation and its evidence links."""
-        with self._lock:
-            metadata_json = json.dumps(metadata or {}, sort_keys=True)
-            self._conn.execute(
-                """
-                INSERT INTO observations (
-                    observation_type, subject, summary, confidence, metadata_json
-                )
-                VALUES (?, ?, ?, ?, ?)
-                ON CONFLICT(observation_type, subject) DO UPDATE SET
-                    summary = excluded.summary,
-                    confidence = excluded.confidence,
-                    metadata_json = excluded.metadata_json,
-                    updated_at = CURRENT_TIMESTAMP
-                """,
-                (observation_type, subject, summary, confidence, metadata_json),
-            )
-            row = self._conn.execute(
-                """
-                SELECT observation_id
-                FROM observations
-                WHERE observation_type = ? AND subject = ?
-                """,
-                (observation_type, subject),
-            ).fetchone()
-            observation_id = int(row["observation_id"])
-
-            self._conn.execute(
-                "DELETE FROM observation_evidence WHERE observation_id = ?",
-                (observation_id,),
-            )
-            unique_fact_ids = sorted({int(fid) for fid in evidence_fact_ids})
-            if unique_fact_ids:
-                self._conn.executemany(
-                    """
-                    INSERT OR IGNORE INTO observation_evidence (observation_id, fact_id)
-                    VALUES (?, ?)
-                    """,
-                    [(observation_id, fact_id) for fact_id in unique_fact_ids],
-                )
-            self._conn.commit()
-            return observation_id
-
-    def list_observations(
-        self,
-        observation_type: str | None = None,
-        min_confidence: float = 0.0,
-        limit: int = 50,
-    ) -> list[dict]:
-        """List synthesized observations with expanded supporting evidence."""
-        with self._lock:
-            params: list = [min_confidence]
-            observation_clause = ""
-            if observation_type is not None:
-                observation_clause = "AND observation_type = ?"
-                params.append(observation_type)
-            params.append(limit)
-            rows = self._conn.execute(
-                f"""
-                SELECT observation_id, observation_type, subject, summary, confidence,
-                       metadata_json, created_at, updated_at,
-                       (
-                           SELECT COUNT(*)
-                           FROM observation_evidence oe
-                           WHERE oe.observation_id = observations.observation_id
-                       ) AS evidence_count
-                FROM observations
-                WHERE confidence >= ?
-                  {observation_clause}
-                ORDER BY confidence DESC, updated_at DESC
-                LIMIT ?
-                """,
-                params,
-            ).fetchall()
-
-            results = []
-            for row in rows:
-                item = dict(row)
-                try:
-                    item["metadata"] = json.loads(item.pop("metadata_json") or "{}")
-                except json.JSONDecodeError:
-                    item["metadata"] = {}
-                item["evidence"] = self._get_observation_evidence(int(item["observation_id"]))
-                results.append(item)
-            return results
-
-    def _get_observation_evidence(self, observation_id: int) -> list[dict]:
-        rows = self._conn.execute(
-            """
-            SELECT f.fact_id, f.content, f.category, f.tags, f.trust_score,
-                   f.retrieval_count, f.helpful_count, f.created_at, f.updated_at
-            FROM observation_evidence oe
-            JOIN facts f ON f.fact_id = oe.fact_id
-            WHERE oe.observation_id = ?
-            ORDER BY f.trust_score DESC, f.updated_at DESC
-            """,
-            (observation_id,),
-        ).fetchall()
-        return [self._row_to_dict(row) for row in rows]
-
     def record_feedback(self, fact_id: int, helpful: bool) -> dict:
         """Record user feedback and adjust trust asymmetrically.
 

tests/plugins/memory/test_holographic_observations.py

@@ -1,96 +0,0 @@
-import json
-
-import pytest
-
-from plugins.memory.holographic import HolographicMemoryProvider
-from plugins.memory.holographic.store import MemoryStore
-
-
-@pytest.fixture()
-def store(tmp_path):
-    db_path = tmp_path / "memory.db"
-    s = MemoryStore(db_path=str(db_path), default_trust=0.5)
-    yield s
-    s.close()
-
-
-@pytest.fixture()
-def provider(tmp_path):
-    p = HolographicMemoryProvider(
-        config={
-            "db_path": str(tmp_path / "memory.db"),
-            "default_trust": 0.5,
-        }
-    )
-    p.initialize(session_id="test-session")
-    yield p
-    if p._store:
-        p._store.close()
-
-
-class TestObservationSynthesis:
-    def test_observe_action_persists_observation_with_evidence_links(self, provider):
-        fact_ids = [
-            provider._store.add_fact('User prefers concise status updates', category='user_pref'),
-            provider._store.add_fact('User wants result-only replies with no fluff', category='user_pref'),
-        ]
-
-        result = json.loads(
-            provider.handle_tool_call(
-                'fact_store',
-                {
-                    'action': 'observe',
-                    'query': 'What communication style does the user prefer?',
-                    'limit': 5,
-                },
-            )
-        )
-
-        assert result['count'] == 1
-        observation = result['observations'][0]
-        assert observation['observation_type'] == 'recurring_preference'
-        assert observation['confidence'] >= 0.6
-        assert sorted(item['fact_id'] for item in observation['evidence']) == sorted(fact_ids)
-
-        stored = provider._store.list_observations(limit=10)
-        assert len(stored) == 1
-        assert stored[0]['observation_type'] == 'recurring_preference'
-        assert stored[0]['evidence_count'] == 2
-        assert len(provider._store.list_facts(limit=10)) == 2
-
-    def test_observe_action_synthesizes_three_observation_types(self, provider):
-        provider._store.add_fact('User prefers concise updates', category='user_pref')
-        provider._store.add_fact('User wants result-only communication', category='user_pref')
-        provider._store.add_fact('Project is moving to a local-first deployment model', category='project')
-        provider._store.add_fact('Project direction stays Gitea-first for issue and PR flow', category='project')
-        provider._store.add_fact('Operator always commits early before moving on', category='general')
-        provider._store.add_fact('Operator pushes a PR immediately after each meaningful fix', category='general')
-
-        result = json.loads(provider.handle_tool_call('fact_store', {'action': 'observe', 'limit': 10}))
-        types = {item['observation_type'] for item in result['observations']}
-
-        assert {'recurring_preference', 'stable_direction', 'behavioral_pattern'} <= types
-
-    def test_single_fact_does_not_create_overconfident_observation(self, provider):
-        provider._store.add_fact('User prefers concise updates', category='user_pref')
-
-        result = json.loads(
-            provider.handle_tool_call(
-                'fact_store',
-                {'action': 'observe', 'query': 'What does the user prefer?', 'limit': 5},
-            )
-        )
-
-        assert result['count'] == 0
-        assert provider._store.list_observations(limit=10) == []
-
-    def test_prefetch_surfaces_observations_as_separate_layer(self, provider):
-        provider._store.add_fact('User prefers concise updates', category='user_pref')
-        provider._store.add_fact('User wants result-only communication', category='user_pref')
-
-        prefetch = provider.prefetch('What communication style does the user prefer?')
-
-        assert '## Holographic Observations' in prefetch
-        assert '## Holographic Memory' in prefetch
-        assert 'recurring_preference' in prefetch
-        assert 'evidence' in prefetch.lower()

tests/tools/test_registry.py

@@ -294,22 +294,32 @@ class TestBuiltinDiscovery:
             "tools.browser_tool",
             "tools.clarify_tool",
             "tools.code_execution_tool",
+            "tools.crisis_tool",
             "tools.cronjob_tools",
             "tools.delegate_tool",
             "tools.file_tools",
             "tools.homeassistant_tool",
             "tools.image_generation_tool",
+            "tools.local_inference_tool",
             "tools.memory_tool",
             "tools.mixture_of_agents_tool",
             "tools.process_registry",
             "tools.rl_training_tool",
+            "tools.scavenger_fixer",
             "tools.send_message_tool",
             "tools.session_search_tool",
             "tools.skill_manager_tool",
             "tools.skills_tool",
+            "tools.sovereign_router",
+            "tools.sovereign_scavenger",
+            "tools.sovereign_teleport",
+            "tools.static_analyzer",
+            "tools.symbolic_verify",
             "tools.terminal_tool",
             "tools.todo_tool",
             "tools.tts_tool",
+            "tools.ultraplan",
+            "tools.verify_tool",
             "tools.vision_tools",
             "tools.web_tools",
         }

tests/tools/test_ultraplan_tool.py

@@ -0,0 +1,81 @@
+import json
+from pathlib import Path
+
+from toolsets import resolve_toolset
+from tools.registry import registry
+
+
+def test_create_action_saves_markdown_and_json(tmp_path):
+    from tools.ultraplan import ultraplan_tool
+
+    result = json.loads(
+        ultraplan_tool(
+            action="create",
+            mission="Daily autonomous planning",
+            streams=[
+                {
+                    "id": "A",
+                    "name": "Backlog burn",
+                    "phases": [
+                        {"id": "A1", "name": "Triage", "artifact": "issue list"},
+                        {"id": "A2", "name": "Ship", "dependencies": ["A1"], "artifact": "PR"},
+                    ],
+                }
+            ],
+            base_dir=str(tmp_path),
+        )
+    )
+
+    assert result["success"] is True
+    assert Path(result["file_path"]).exists()
+    assert Path(result["json_path"]).exists()
+    assert "Work Streams" in Path(result["file_path"]).read_text(encoding="utf-8")
+
+
+def test_load_action_returns_saved_plan(tmp_path):
+    from tools.ultraplan import ultraplan_tool
+
+    created = json.loads(
+        ultraplan_tool(
+            action="create",
+            date="20260422",
+            mission="Mission from saved plan",
+            base_dir=str(tmp_path),
+        )
+    )
+    loaded = json.loads(
+        ultraplan_tool(
+            action="load",
+            date="20260422",
+            base_dir=str(tmp_path),
+        )
+    )
+
+    assert created["success"] is True
+    assert loaded["success"] is True
+    assert loaded["plan"]["mission"] == "Mission from saved plan"
+    assert loaded["file_path"].endswith("ultraplan_20260422.md")
+
+
+def test_cron_spec_returns_daily_schedule_and_prompt():
+    from tools.ultraplan import ultraplan_tool
+
+    result = json.loads(ultraplan_tool(action="cron_spec"))
+
+    assert result["success"] is True
+    assert result["schedule"] == "0 6 * * *"
+    assert "Ultraplan" in result["prompt"]
+    assert "ultraplan_YYYYMMDD.md" in result["prompt"]
+
+
+def test_registry_registers_ultraplan_tool():
+    import tools.ultraplan  # noqa: F401
+
+    entry = registry.get_entry("ultraplan")
+    assert entry is not None
+    assert entry.toolset == "todo"
+
+
+def test_default_toolsets_include_ultraplan():
+    assert "ultraplan" in resolve_toolset("todo")
+    assert "ultraplan" in resolve_toolset("hermes-cli")

tools/ultraplan.py

@@ -290,6 +290,9 @@ def load_ultraplan(date: str, base_dir: Path = None) -> Optional[Ultraplan]:
         return None
 
 
+DEFAULT_ULTRAPLAN_SCHEDULE = "0 6 * * *"
+
+
 def generate_daily_cron_prompt() -> str:
     """Generate the prompt for the daily ultraplan cron job."""
     return """Generate today's Ultraplan.
@@ -298,9 +301,9 @@ Steps:
 1. Check open Gitea issues assigned to you
 2. Check open PRs needing review
 3. Check fleet health status
-4. Decompose work into parallel streams
-5. Generate ultraplan_YYYYMMDD.md
-6. File Gitea issue with the plan
+4. Decompose work into parallel streams with concrete phases and artifacts
+5. Use the ultraplan tool to save ~/.timmy/cron/ultraplan_YYYYMMDD.md and the matching JSON sidecar
+6. Optionally file a Gitea issue with the plan summary
 
 Output format:
 - Mission statement
@@ -308,3 +311,176 @@ Output format:
 - Dependency map
 - Success metrics
 """
+
+
+def generate_daily_cron_job_spec(schedule: str = DEFAULT_ULTRAPLAN_SCHEDULE) -> Dict[str, str]:
+    """Return a reusable cron job spec for daily Ultraplan generation."""
+    return {
+        "name": "Daily Ultraplan",
+        "schedule": schedule,
+        "prompt": generate_daily_cron_prompt(),
+        "path_pattern": "~/.timmy/cron/ultraplan_YYYYMMDD.md",
+    }
+
+
+def _resolve_base_dir(base_dir: Optional[str | Path]) -> Path:
+    """Normalize the requested Ultraplan base directory."""
+    if base_dir is None:
+        return Path.home() / ".timmy" / "cron"
+    return Path(base_dir).expanduser()
+
+
+def ultraplan_tool(
+    action: str,
+    date: Optional[str] = None,
+    mission: str = "",
+    streams: Optional[List[Dict[str, Any]]] = None,
+    metrics: Optional[Dict[str, Any]] = None,
+    notes: str = "",
+    base_dir: Optional[str] = None,
+) -> str:
+    """Create/load Ultraplan artifacts and expose a daily cron spec."""
+    from tools.registry import tool_error, tool_result
+
+    action = (action or "").strip().lower()
+    resolved_base_dir = _resolve_base_dir(base_dir)
+
+    try:
+        if action == "create":
+            plan = create_ultraplan(date=date, mission=mission, streams=streams or [])
+            if metrics:
+                plan.metrics = metrics
+            if notes:
+                plan.notes = notes
+            md_path = save_ultraplan(plan, base_dir=resolved_base_dir)
+            json_path = resolved_base_dir / f"ultraplan_{plan.date}.json"
+            return tool_result(
+                success=True,
+                action="create",
+                date=plan.date,
+                file_path=str(md_path),
+                json_path=str(json_path),
+                plan=plan.to_dict(),
+            )
+
+        if action == "load":
+            plan_date = date or datetime.now().strftime("%Y%m%d")
+            plan = load_ultraplan(plan_date, base_dir=resolved_base_dir)
+            if plan is None:
+                return tool_error(
+                    f"No Ultraplan found for {plan_date}",
+                    success=False,
+                    action="load",
+                    date=plan_date,
+                )
+            return tool_result(
+                success=True,
+                action="load",
+                date=plan.date,
+                file_path=str(resolved_base_dir / f"ultraplan_{plan.date}.md"),
+                json_path=str(resolved_base_dir / f"ultraplan_{plan.date}.json"),
+                plan=plan.to_dict(),
+                markdown=plan.to_markdown(),
+            )
+
+        if action == "cron_spec":
+            spec = generate_daily_cron_job_spec()
+            return tool_result(success=True, action="cron_spec", **spec)
+
+        return tool_error(
+            f"Unknown Ultraplan action: {action}",
+            success=False,
+            action=action,
+        )
+    except Exception as e:
+        return tool_error(f"Ultraplan {action or 'tool'} failed: {e}", success=False, action=action)
+
+
+ULTRAPLAN_SCHEMA = {
+    "name": "ultraplan",
+    "description": (
+        "Create or load daily Ultraplan planning artifacts under ~/.timmy/cron/ and "
+        "return a reusable cron spec for autonomous planning. Use this when you want "
+        "a concrete markdown/json plan file with streams, phases, dependencies, and metrics."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string",
+                "enum": ["create", "load", "cron_spec"],
+                "description": "Operation to perform",
+            },
+            "date": {
+                "type": "string",
+                "description": "Plan date as YYYYMMDD. Defaults to today for create/load.",
+            },
+            "mission": {
+                "type": "string",
+                "description": "High-level mission statement for today's plan.",
+            },
+            "streams": {
+                "type": "array",
+                "description": "Optional work streams with phases/artifacts/dependencies for create.",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "id": {"type": "string"},
+                        "name": {"type": "string"},
+                        "phases": {
+                            "type": "array",
+                            "items": {
+                                "type": "object",
+                                "properties": {
+                                    "id": {"type": "string"},
+                                    "name": {"type": "string"},
+                                    "description": {"type": "string"},
+                                    "artifact": {"type": "string"},
+                                    "dependencies": {
+                                        "type": "array",
+                                        "items": {"type": "string"},
+                                    },
+                                },
+                                "required": ["name"],
+                            },
+                        },
+                    },
+                    "required": ["name"],
+                },
+            },
+            "metrics": {
+                "type": "object",
+                "description": "Optional success metrics to store on the plan.",
+                "additionalProperties": True,
+            },
+            "notes": {
+                "type": "string",
+                "description": "Optional free-form notes appended to the saved plan.",
+            },
+            "base_dir": {
+                "type": "string",
+                "description": "Optional override for the Ultraplan storage directory.",
+            },
+        },
+        "required": ["action"],
+    },
+}
+
+
+from tools.registry import registry
+
+registry.register(
+    name="ultraplan",
+    toolset="todo",
+    schema=ULTRAPLAN_SCHEMA,
+    handler=lambda args, **_kw: ultraplan_tool(
+        action=args.get("action", ""),
+        date=args.get("date"),
+        mission=args.get("mission", ""),
+        streams=args.get("streams"),
+        metrics=args.get("metrics"),
+        notes=args.get("notes", ""),
+        base_dir=args.get("base_dir"),
+    ),
+    emoji="🗺️",
+)

toolsets.py

@@ -47,7 +47,7 @@ _HERMES_CORE_TOOLS = [
     # Text-to-speech
     "text_to_speech",
     # Planning & memory
-    "todo", "memory",
+    "todo", "ultraplan", "memory",
     # Session history search
     "session_search",
     # Clarifying questions
@@ -157,8 +157,8 @@ TOOLSETS = {
     },
     
     "todo": {
-        "description": "Task planning and tracking for multi-step work",
-        "tools": ["todo"],
+        "description": "Task planning and tracking for multi-step work, including daily Ultraplan artifacts",
+        "tools": ["todo", "ultraplan"],
         "includes": []
     },