feat: add self-evolution plugin — agent self-optimization system

Add a comprehensive self-evolution system that enables Hermes Agent
to continuously improve through automated analysis and optimization:

Core components:
- reflection_engine: Nightly session analysis (1:00 AM)
- evolution_proposer: Generate improvement proposals from insights
- quality_scorer: Multi-dimensional session quality evaluation
- strategy_injector: Inject learned strategies into new sessions
- strategy_compressor: Strategy optimization and deduplication
- git_analyzer: Code change pattern analysis
- rule_engine: Pattern-based rule generation
- feishu_notifier: Feishu card notifications for evolution events

Storage:
- db.py: SQLite telemetry storage
- strategy_store: Persistent strategy storage
- models.py: Data models

Plugin integration:
- plugin.yaml, hooks.py, __init__.py for plugin system
- cron_jobs.py for scheduled tasks

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

self_evolution/hooks.py

@@ -0,0 +1,200 @@
+"""
+Self Evolution Plugin — Lifecycle Hooks
+========================================
+
+Registered hooks:
+
+  - post_tool_call:  Collect per-tool telemetry
+  - on_session_end:  Compute quality score + detect outcome signals
+  - pre_llm_call:    Inject learned strategy hints
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+import time
+from typing import Any, Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+# ── Correction detection patterns (inspired by Claude Code conversation-analyzer) ──
+
+CORRECTION_PATTERNS = re.compile(
+    r"(不对|错误|重试|不要|停|stop|wrong|retry|no|don't|not that|不是|不是这个|为什么|换一种)",
+    re.IGNORECASE,
+)
+
+FRUSTRATION_PATTERNS = re.compile(
+    r"(烦|慢|太慢|浪费时间|浪费时间|浪费时间|why did you|无语|算了|够了)",
+    re.IGNORECASE,
+)
+
+
+# ── post_tool_call ───────────────────────────────────────────────────────
+
+def on_tool_call(**kwargs) -> None:
+    """Collect per-tool invocation telemetry."""
+    from self_evolution.db import insert
+
+    tool_name = kwargs.get("tool_name", "unknown")
+    started_at = kwargs.get("started_at", time.time())
+    duration_ms = kwargs.get("duration_ms", 0)
+    success = kwargs.get("success", True)
+    error_type = kwargs.get("error_type") if not success else None
+    session_id = kwargs.get("session_id", "")
+    turn_number = kwargs.get("turn_number", 0)
+
+    try:
+        insert("tool_invocations", {
+            "session_id": session_id,
+            "tool_name": tool_name,
+            "duration_ms": duration_ms,
+            "success": success,
+            "error_type": error_type,
+            "turn_number": turn_number,
+            "created_at": started_at,
+        })
+    except Exception as exc:
+        logger.warning("telemetry insert failed: %s", exc)
+
+
+# ── on_session_end ───────────────────────────────────────────────────────
+
+def on_session_end(**kwargs) -> None:
+    """Compute quality score and detect outcome signals when session ends."""
+    from self_evolution.db import insert, insert_many
+    from self_evolution.quality_scorer import compute_score
+
+    session_data = kwargs.get("session_data", {})
+    session_id = session_data.get("session_id", "")
+
+    if not session_id:
+        return
+
+    # Compute quality score
+    score = compute_score(session_data)
+    try:
+        insert("session_scores", score.to_db_row())
+    except Exception as exc:
+        logger.warning("score insert failed: %s", exc)
+
+    # Detect and batch-insert outcome signals
+    signals = _detect_outcome_signals(session_data, kwargs)
+    if signals:
+        try:
+            insert_many("outcome_signals", signals)
+        except Exception as exc:
+            logger.warning("signal insert failed: %s", exc)
+
+
+def _detect_outcome_signals(session_data: dict, kwargs: dict) -> list:
+    """Detect implicit outcome signals from session behavior.
+
+    Inspired by Claude Code conversation-analyzer's signal detection:
+    - Explicit corrections: user says "不对", "重试"
+    - Frustration signals: user says "为什么", "太慢"
+    - Completion / interruption status
+    - Budget exhaustion
+    """
+    signals = []
+    session_id = session_data.get("session_id", "")
+
+    # Completion signal
+    completed = session_data.get("completed", False)
+    interrupted = session_data.get("interrupted", False)
+    partial = session_data.get("partial", False)
+
+    if completed:
+        signals.append({
+            "session_id": session_id,
+            "signal_type": "completed",
+            "signal_value": 1.0,
+            "metadata": "{}",
+        })
+    elif interrupted:
+        signals.append({
+            "session_id": session_id,
+            "signal_type": "interrupted",
+            "signal_value": 0.5,
+            "metadata": "{}",
+        })
+    elif partial:
+        signals.append({
+            "session_id": session_id,
+            "signal_type": "partial",
+            "signal_value": 0.3,
+            "metadata": "{}",
+        })
+
+    # Budget exhaustion
+    max_iterations = session_data.get("max_iterations", 0)
+    iterations = session_data.get("iterations", 0)
+    if max_iterations and iterations >= max_iterations:
+        signals.append({
+            "session_id": session_id,
+            "signal_type": "budget_exhausted",
+            "signal_value": 0.0,
+            "metadata": f'{{"iterations": {iterations}}}',
+        })
+
+    # User correction / frustration detection from messages
+    messages = session_data.get("messages", [])
+    for msg in messages:
+        if msg.get("role") != "user":
+            continue
+        content = msg.get("content", "")
+        if isinstance(content, list):
+            content = " ".join(
+                block.get("text", "") for block in content
+                if isinstance(block, dict) and block.get("type") == "text"
+            )
+
+        if CORRECTION_PATTERNS.search(content):
+            signals.append({
+                "session_id": session_id,
+                "signal_type": "correction",
+                "signal_value": 0.2,
+                "metadata": f'{{"text": {repr(content[:100])}}}',
+            })
+            break  # Only one correction signal per session
+
+        if FRUSTRATION_PATTERNS.search(content):
+            signals.append({
+                "session_id": session_id,
+                "signal_type": "frustration",
+                "signal_value": 0.1,
+                "metadata": f'{{"text": {repr(content[:100])}}}',
+            })
+            break
+
+    return signals
+
+
+# ── pre_llm_call ─────────────────────────────────────────────────────────
+
+def on_pre_llm_call(**kwargs) -> Optional[Dict[str, Any]]:
+    """Inject learned strategy hints into system prompt.
+
+    Inspired by Claude Code learning-output-style SessionStart hook pattern:
+    automatically inject behavioral context without user action.
+    """
+    from self_evolution.strategy_injector import inject_hints
+
+    try:
+        hints = inject_hints(kwargs)
+        if hints:
+            return {"system_hint": hints}
+    except Exception as exc:
+        logger.warning("strategy injection failed: %s", exc)
+
+    return None
+
+
+# ── Registration ─────────────────────────────────────────────────────────
+
+def register_all(ctx) -> None:
+    """Register all lifecycle hooks via PluginContext."""
+    ctx.register_hook("post_tool_call", on_tool_call)
+    ctx.register_hook("on_session_end", on_session_end)
+    ctx.register_hook("pre_llm_call", on_pre_llm_call)