feat: context engine plugins, scaffolding, and pricing fixes (#6)

* feat: add 4 context engine plugins

- topic-memory: keyword clustering for topic-aware memory recall
- episodic-memory: conversation segmentation and cross-session recall
- user-profile: persistent user profiling from conversation patterns
- context-decay: time-based memory decay with reinforcement dynamics

All plugins use the ingest/after_turn hook protocol with stdin/stdout JSON.

* chore: add plugin scaffolding, update docs and templates

- Add plugin.toml template with {{NAME}} placeholder
- Add new-plugin Makefile target with hooks/ scaffolding
- Update plugins/README.md with all 10 plugins
- Update README.md stats (10 plugins, 220+ models)
- Add Plugin checkbox and checklist to PR template
- Add Plugin to issue template content type dropdown
- Fix CONTRIBUTING.md: last_verified is recommended, not required

* fix: correct model pricing and remove deprecated entries

- openrouter/gemma-2-9b-it: fix pricing from 0.0 to 0.03/0.09 per M tokens
  (free variant correctly stays at 0.0)
- github-copilot: remove deprecated copilot/gpt-4 model entry
  (GPT-4 retired in favor of GPT-4o for Copilot)

* docs: annotate kimi-coding as membership-gated

Kimi Code CLI uses quota-based membership model (not per-token billing).
Free tier has limited weekly requests; underlying model is K2.5.
Pricing kept at 0.0 consistent with other subscription providers
(chatgpt, github-copilot) but with explanatory comments.

* style: fix trailing newline in github-copilot.toml

* fix: correct Moonshot/Kimi model pricing from official sources

All 5 models had incorrect pricing:
- moonshot-v1-8k: 0.10/0.10 → 0.20/2.00
- moonshot-v1-32k: 0.30/0.30 → 1.00/3.00
- moonshot-v1-128k: 0.80/0.80 → 2.00/5.00
- kimi-k2: 2.00/8.00 → 0.60/2.50
- kimi-k2.5: 2.00/8.00 → 0.45/2.20

Sources: platform.moonshot.ai/docs/pricing/chat, costgoat.com, getmaxim.ai

* feat: add MiniMax M2.7 and M2.7-highspeed models

Released 2026-03-18, MiniMax's latest flagship text model.
10B activated params, 200K context, 128K output, tool use, streaming.
Pricing: $0.30/$1.20 per M tokens (input/output).

Added to both international (minimax.io) and China (minimaxi.com) providers.

plugins/context-decay/hooks/ingest.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""Context-decay ingest hook.
+
+Loads the memory store for the current agent, applies time-based decay to
+all memories, scores them for relevance to the incoming message, and returns
+the top matches above the recall threshold.
+
+This hook updates last_accessed timestamps for recalled memories to implement
+"use it or lose it" dynamics -- the one exception where ingest modifies storage.
+
+Receives via stdin:
+    {"type": "ingest", "agent_id": "...", "message": "user message text"}
+
+Prints to stdout:
+    {"type": "ingest_result", "memories": [{"content": "..."}]}
+"""
+import json
+import os
+import re
+import sys
+from datetime import datetime, timezone
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+# Decay: 5% confidence loss per day
+DECAY_FACTOR = 0.95
+
+# Minimum final_score to recall a memory
+RECALL_THRESHOLD = 0.3
+
+# Maximum memories to return per ingest
+MAX_RECALL = 5
+
+# Minimum keyword length
+MIN_WORD_LEN = 3
+
+STOPWORDS = frozenset({
+    "a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for",
+    "of", "with", "by", "from", "is", "are", "was", "were", "be", "been",
+    "being", "have", "has", "had", "do", "does", "did", "will", "would",
+    "could", "should", "may", "might", "shall", "can", "need", "must",
+    "it", "its", "i", "me", "my", "you", "your", "he", "she", "we",
+    "they", "them", "their", "this", "that", "these", "those", "what",
+    "which", "who", "how", "when", "where", "why", "if", "then", "so",
+    "not", "no", "just", "also", "very", "too", "about", "up", "out",
+    "all", "some", "any", "each", "every", "into", "over", "after",
+})
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def extract_keywords(text):
+    """Extract deduplicated lowercase keywords from text."""
+    words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", text)
+    seen = set()
+    keywords = []
+    for w in words:
+        lower = w.lower()
+        if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN and lower not in seen:
+            seen.add(lower)
+            keywords.append(lower)
+    return keywords
+
+
+def store_dir():
+    """Return the storage directory path for context-decay."""
+    return os.path.join(
+        os.path.expanduser("~"), ".librefang", "plugins", "context-decay"
+    )
+
+
+def store_path(agent_id):
+    """Return the JSON store file path for a given agent."""
+    return os.path.join(store_dir(), f"{agent_id}.json")
+
+
+def load_store(agent_id):
+    """Load the memory store for an agent. Returns default on failure."""
+    path = store_path(agent_id)
+    if not os.path.isfile(path):
+        return {"memories": []}
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        if not isinstance(data, dict) or "memories" not in data:
+            return {"memories": []}
+        return data
+    except (json.JSONDecodeError, OSError):
+        return {"memories": []}
+
+
+def save_store(agent_id, data):
+    """Persist the memory store for an agent."""
+    dirpath = store_dir()
+    os.makedirs(dirpath, exist_ok=True)
+    path = store_path(agent_id)
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(data, f, indent=2, ensure_ascii=False)
+
+
+def now_iso():
+    """Return the current UTC time as an ISO 8601 string."""
+    return datetime.now(timezone.utc).isoformat()
+
+
+def parse_iso(ts):
+    """Parse an ISO 8601 timestamp string to a datetime object.
+
+    Handles both +00:00 and Z suffixes. Returns None on failure.
+    """
+    if not ts:
+        return None
+    try:
+        # Replace Z suffix for compatibility with fromisoformat on older Python
+        cleaned = ts.replace("Z", "+00:00")
+        return datetime.fromisoformat(cleaned)
+    except (ValueError, TypeError):
+        return None
+
+
+def hours_since(ts_str, now):
+    """Calculate hours elapsed between a timestamp string and now."""
+    dt = parse_iso(ts_str)
+    if dt is None:
+        return 0.0
+    delta = now - dt
+    return max(delta.total_seconds() / 3600.0, 0.0)
+
+
+def apply_decay(confidence, hours_elapsed):
+    """Apply exponential decay: confidence * 0.95^(hours / 24)."""
+    if hours_elapsed <= 0:
+        return confidence
+    return confidence * (DECAY_FACTOR ** (hours_elapsed / 24.0))
+
+
+def keyword_overlap(keywords_a, keywords_b):
+    """Compute Jaccard similarity between two keyword lists."""
+    if not keywords_a or not keywords_b:
+        return 0.0
+    set_a = set(keywords_a)
+    set_b = set(keywords_b)
+    intersection = set_a & set_b
+    union = set_a | set_b
+    if not union:
+        return 0.0
+    return len(intersection) / len(union)
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    try:
+        request = json.loads(sys.stdin.read())
+    except (json.JSONDecodeError, ValueError):
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    message = request.get("message", "")
+    agent_id = request.get("agent_id", "")
+
+    if not message.strip() or not agent_id:
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    query_keywords = extract_keywords(message)
+    if not query_keywords:
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    data = load_store(agent_id)
+    memories = data.get("memories", [])
+
+    if not memories:
+        print(json.dumps({"type": "ingest_result", "memories": []}))
+        return
+
+    now = datetime.now(timezone.utc)
+    scored = []
+    store_modified = False
+
+    for mem in memories:
+        # Apply time-based decay
+        elapsed = hours_since(mem.get("last_accessed", mem.get("created", "")), now)
+        decayed = apply_decay(mem.get("confidence", 0.0), elapsed)
+
+        # Score relevance via keyword overlap
+        relevance = keyword_overlap(mem.get("keywords", []), query_keywords)
+
+        # Composite score: decayed confidence weighted with relevance
+        final_score = decayed * (0.5 + 0.5 * relevance)
+
+        if final_score > RECALL_THRESHOLD:
+            scored.append((final_score, decayed, mem))
+
+    # Sort by final_score descending, take top MAX_RECALL
+    scored.sort(key=lambda x: x[0], reverse=True)
+    top = scored[:MAX_RECALL]
+
+    result_memories = []
+    for final_score, decayed, mem in top:
+        confidence_pct = int(round(decayed * 100))
+        content = mem.get("content", "")
+        result_memories.append({
+            "content": f"[context-decay] Recalled ({confidence_pct}%): {content}"
+        })
+
+        # Update last_accessed and save back -- "use it or lose it"
+        mem["last_accessed"] = now_iso()
+        mem["access_count"] = mem.get("access_count", 0) + 1
+        store_modified = True
+
+    # Persist access timestamp updates
+    if store_modified:
+        save_store(agent_id, data)
+
+    print(json.dumps({"type": "ingest_result", "memories": result_memories}))
+
+
+if __name__ == "__main__":
+    main()