@@ -1055,7 +1167,7 @@ const MODEL_BENCHMARKS = {
// Default embedded data (minimal - updated by sync script)
const EMBEDDED_DATA = {
"version": "1.0.0",
- "lastUpdated": "2026-05-27T12:47:21.972Z",
+ "lastUpdated": "2026-05-27T13:10:49.174Z",
"agents": {
"lead-developer": {
"current": {
@@ -4931,7 +5043,7 @@ const EMBEDDED_DATA = {
"total_agents": 38,
"agents_with_history": 34,
"pending_recommendations": 0,
- "last_sync": "2026-05-27T12:47:21.974Z",
+ "last_sync": "2026-05-27T13:10:49.175Z",
"sync_sources": [
"git",
"capability-index.yaml",
@@ -4969,6 +5081,16 @@ async function init() {
}
try {
+ // Load real dashboard data FIRST (overrides stale agent-versions)
+ try {
+ const dashRes = await fetch('data/dashboard-data.json');
+ if (dashRes.ok) {
+ window.dashboardData = await dashRes.json();
+ // Sync agentData from dashboard data for all other tabs
+ syncAgentDataFromDashboard(window.dashboardData);
+ }
+ } catch (e) { console.warn('dashboard-data.json not loaded:', e.message); }
+
document.getElementById('lastSync').textContent = formatDate(agentData.lastUpdated);
document.getElementById('agentCount').textContent = agentData.evolution_metrics.total_agents + ' agents';
document.getElementById('historyCount').textContent = agentData.evolution_metrics.agents_with_history + ' with history';
@@ -4984,12 +5106,69 @@ async function init() {
renderRecommendations();
renderHeatmap();
renderImpact();
+ renderAnalytics();
} catch (error) {
console.error('Failed to render dashboard:', error);
document.getElementById('lastSync').textContent = 'Error rendering data';
}
}
+function syncAgentDataFromDashboard(dd) {
+ // Convert dashboard format back to agentData format expected by other renders
+ const agents = {};
+ const categories = {};
+ let withHistory = 0;
+
+ for (const a of dd.agents || []) {
+ const cat = a.category || 'General';
+ if (!categories[cat]) categories[cat] = 0;
+ categories[cat]++;
+
+ const history = a.latest_change ? [{
+ date: a.latest_change.date,
+ type: a.latest_change.type,
+ from: a.latest_change.from,
+ to: a.latest_change.to,
+ reason: a.latest_change.reason,
+ source: a.latest_change.source
+ }] : [];
+ if (history.length > 0) withHistory++;
+
+ agents[a.name] = {
+ current: {
+ model: a.model,
+ mode: a.mode,
+ description: a.description,
+ category: a.category || 'General',
+ color: a.color || '#8B5CF6',
+ provider: a.provider || 'Ollama',
+ variant: a.variant || '',
+ capabilities: [],
+ recommendations: [],
+ benchmark: {
+ fit_score: a.fit_score || 0,
+ instruction_following: a.instruction_following || 0
+ }
+ },
+ history: history
+ };
+ }
+
+ // Update agentData in-place so all render* functions see real data
+ agentData = {
+ version: '1.0.0',
+ lastUpdated: dd.generated,
+ agents: agents,
+ evolution_metrics: {
+ total_agents: dd.total_agents,
+ agents_with_history: withHistory,
+ pending_recommendations: 0,
+ last_sync: dd.generated,
+ sync_sources: [dd.source || 'dashboard-data', '.kilo/agents/*.md', 'evolution.json']
+ }
+ };
+}
+
// Format date
function formatDate(dateStr) {
const date = new Date(dateStr);
@@ -5260,96 +5439,62 @@ function renderRecCard(r, index) {
`;
}
-// Render Heatmap
+// Render Heatmap — REAL DATA: Agent × Current Model × Real Fit Score
function renderHeatmap() {
- const agents = Object.entries(agentData.agents);
- if (agents.length === 0) return;
+ const esc = str => (str || '').replace(/[&<>"']/g, m => ({'&':'&','<':'<','>':'>','"':'"',"'":'''}[m]));
+ const dd = window.dashboardData;
- // Build unique model list from all agents
- const modelSet = new Set();
- const modelIfScores = {};
- agents.forEach(([_, a]) => {
- const model = a.current.model;
- if (model) {
- modelSet.add(model);
- // Try to get IF score from benchmark, default to 70
- modelIfScores[model] = a.current.benchmark?.instruction_following || 70;
- }
- });
+ if (!dd || !dd.agents) {
+ document.getElementById('hmTable').innerHTML = '
⚠️ Нет данных. Запустите анализ. ';
+ return;
+ }
- // Build hmModels array
- const hmModels = [...modelSet].map(m => {
- // Extract short name from full model ID
- let shortName = m;
- if (m.includes('qwen3-coder')) shortName = 'Qwen3-Coder';
- else if (m.includes('glm-')) shortName = m.includes('5.1') ? 'GLM-5.1' : 'GLM-5';
- else if (m.includes('nemotron')) shortName = m.includes('nano') ? 'Nem. Nano' : 'Nem. Super';
- else if (m.includes('minimax')) shortName = 'MiniMax M2.5';
- else if (m.includes('kimi')) shortName = 'Kimi K2.6';
- else if (m.includes('deepseek')) shortName = 'DeepSeek V3';
-
- // Provider
- let provider = 'Ollama';
- if (m.includes('cloud') || m.includes('ollama-cloud')) provider = 'Ollama Cloud';
- else if (m.includes('openrouter')) provider = 'OpenRouter';
- else if (m.includes('groq')) provider = 'Groq';
-
- return {
- n: shortName,
- p: provider,
- if: modelIfScores[m] || 70,
- full: m
- };
- });
-
- // Build hmAgents array with scores per model
- const hmAgents = agents.map(([name, agent]) => {
- const currentModel = agent.current.model;
- const currentIdx = hmModels.findIndex(m => m.full === currentModel);
- const fitScore = agent.current.benchmark?.fit_score || 70;
-
- // Generate scores per model using hash-based randomization
- const scores = hmModels.map((m, idx) => {
- if (m.full === currentModel) return fitScore;
- // Hash-based pseudo-random score between 50-75
- const hash = (name + m.full).split('').reduce((a, c) => a + c.charCodeAt(0), 0);
- return 50 + (hash % 26);
+ const agents = dd.agents;
+ // Get unique models sorted by count of agents
+ const modelCounts = {};
+ agents.forEach(a => { modelCounts[a.model_short] = (modelCounts[a.model_short] || 0) + 1; });
+ const modelList = Object.entries(modelCounts)
+ .sort((a, b) => b[1] - a[1])
+ .map(([short]) => {
+ const m = dd.models[short] || {};
+ return {
+ short,
+ full: 'ollama-cloud/' + short,
+ name: m.name || short,
+ avg_fit: m.avg_fit || 0,
+ agents: m.agents || 0
+ };
});
- return {
- n: name,
- c: currentIdx,
- s: scores
- };
- });
-
- // Render the table
+ // Render table: rows=agents, cols=models
const t = document.getElementById('hmTable');
let h = '
Agent ';
- hmModels.forEach(m => {
- const ifColor = m.if >= 85 ? '#00ff94' : m.if >= 75 ? '#facc15' : '#ff6b81';
+ modelList.forEach(m => {
+ const color = m.avg_fit >= 85 ? '#00ff94' : m.avg_fit >= 70 ? '#facc15' : '#ff6b81';
h += `
- ${m.n}${m.p} IF:${m.if}
+ ${esc(m.name)}avg:${m.avg_fit} ${m.agents}
`;
});
h += '';
- hmAgents.forEach(ag => {
- const mx = Math.max(...ag.s);
- h += `${ag.n} `;
- ag.s.forEach((s, j) => {
- const best = s === mx;
- const cur = j === ag.c;
- const ifLow = hmModels[j].if < 75;
+ agents.forEach(a => {
+ h += `${esc(a.name)} `;
+ modelList.forEach((m, j) => {
+ const isCurrent = a.model_short === m.short;
+ const score = isCurrent ? a.fit_score : 0; // Only show score for CURRENT model
+ const cur = isCurrent;
let marks = '';
- if (best) marks += '★ ';
- if (ifLow) marks += '⚠ ';
- h += `${s}${marks} `;
+ onclick="openHmModal(event, '${esc(a.name)}', '${esc(m.name)}', ${isCurrent ? a.fit_score : 0}, ${isCurrent ? a.instruction_following : 0})"
+ >${isCurrent ? a.fit_score : '·'}${marks}`;
});
h += '
+
⚠️
+
Аналитика недоступна
+
${esc(msg)}
+
Убедитесь, что /data/state.json существует и доступен.
+
+
${s.label}
+
${s.value}
+
${s.sub}
+
+
+
`;
+ for (const [cat, list] of Object.entries(cats).sort()) {
+ html += `
+
+
`;
+ for (const a of list) {
+ const sc = a.fit_score !== undefined ? a.fit_score : modelScore(a.model);
+ html += `
+ ${esc(a.name)}
+ ${sc}
+
`;
+ }
+ html += '
';
+ }
+ html += '
+
${esc(a.name.slice(0, 12))}
+
${score}
+
${esc(a.name)} — ${score}
+
No command data available
+ /${esc(c.name)}
+ ${esc(modelShort)}
+ ${score} ${esc((c.description || '').slice(0, 50))}${(c.description || '').length > 50 ? '…' : ''}
+ `;
+ }
+ tbody.innerHTML = html;
+}
+
+function esc(str) {
+ return (str || '').replace(/[&<>"']/g, m => ({'&':'&','<':'<','>':'>','"':'"',"'":'''}[m]));
+}
+
// Tab switching
function switchTab(tabId, el) {
document.querySelectorAll('.tab-btn').forEach(btn => btn.classList.remove('active'));
diff --git a/docker/docker-compose.ollama.yml b/docker/docker-compose.ollama.yml
new file mode 100644
index 0000000..e13a084
--- /dev/null
+++ b/docker/docker-compose.ollama.yml
@@ -0,0 +1,36 @@
+# Ollama service for multi-model evaluation
+# Provides LLM inference API for Real-Fit engine and dashboard
+
+services:
+ ollama:
+ image: ollama/ollama:latest
+ container_name: ollama
+ ports:
+ - "11434:11434"
+ environment:
+ - OLLAMA_ORIGINS=*
+ - OLLAMA_HOST=0.0.0.0
+ volumes:
+ - ollama-models:/root/.ollama/models
+ # Optional: pre-pull models on startup
+ - ./scripts/ollama-pull-models.sh:/ollama-pull.sh:ro
+ healthcheck:
+ test: ["CMD", "wget", "-q", "--spider", "http://localhost:11434/api/tags"]
+ interval: 30s
+ timeout: 10s
+ retries: 3
+ start_period: 60s
+ restart: unless-stopped
+ networks:
+ - ollama-net
+ labels:
+ - "com.apaw.service=ollama"
+ - "com.apaw.description=Ollama LLM inference API"
+
+volumes:
+ ollama-models:
+ driver: local
+
+networks:
+ ollama-net:
+ driver: bridge
diff --git a/landing/Dockerfile b/landing/Dockerfile
index a09f212..1a7da2e 100644
--- a/landing/Dockerfile
+++ b/landing/Dockerfile
@@ -1,3 +1,14 @@
-FROM nginx:alpine
+FROM nginx:bookworm
+
+# Python3 required for the embedded state API
+RUN apt-get update && apt-get install -y --no-install-recommends python3 && rm -rf /var/lib/apt/lists/*
+
COPY landing /usr/share/nginx/html
+COPY landing/nginx-landing.conf /etc/nginx/conf.d/default.conf
+COPY landing/entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+# Mount-ready: content served from volume
EXPOSE 80
+
+ENTRYPOINT ["/entrypoint.sh"]
diff --git a/landing/api/real-fit-report.json b/landing/api/real-fit-report.json
new file mode 120000
index 0000000..8a8ef08
--- /dev/null
+++ b/landing/api/real-fit-report.json
@@ -0,0 +1 @@
+../../agent-evolution/data/real-fit-report.json
\ No newline at end of file
diff --git a/landing/api/server.py b/landing/api/server.py
new file mode 100644
index 0000000..055ff3e
--- /dev/null
+++ b/landing/api/server.py
@@ -0,0 +1,199 @@
+#!/usr/bin/env python3
+"""Micro API for landing page — reads live agent configs and returns JSON."""
+import json, os, glob, re
+from datetime import datetime, timezone
+import socketserver
+import http.server
+
+PORT = 8080
+FALLBACK_DIR = "/usr/share/nginx/html"
+
+
+def find_dir(sub):
+ candidates = [
+ os.path.join(FALLBACK_DIR, sub),
+ os.path.join(os.path.dirname(__file__), sub),
+ f"/app/{sub}",
+ f"./{sub}",
+ ]
+ for c in candidates:
+ if os.path.isdir(c):
+ return c
+ return None
+
+
+def find_file(name):
+ candidates = [
+ os.path.join(FALLBACK_DIR, "api", name),
+ os.path.join(os.path.dirname(__file__), name),
+ f"/app/landing/api/{name}",
+ ]
+ for c in candidates:
+ if os.path.isfile(c):
+ return c
+ return None
+
+
+def parse_frontmatter(path):
+ try:
+ with open(path, "r", encoding="utf-8") as f:
+ content = f.read()
+ except Exception:
+ return None
+ if not content.startswith("---"):
+ return None
+ end = content.find("---", 3)
+ if end == -1:
+ return None
+ fm = content[3:end]
+ data = {}
+ for line in fm.strip().split("\n"):
+ m = re.match(r"^(\w+):\s*(.+)$", line)
+ if m:
+ data[m.group(1)] = m.group(2).strip()
+ return data
+
+
+def load_dashboard_data():
+ path = find_file("dashboard-data.json")
+ if not path:
+ return None
+ try:
+ with open(path, "r", encoding="utf-8") as f:
+ data = json.load(f)
+ if data.get("agents"):
+ return data
+ except Exception:
+ pass
+ return None
+
+
+def load_real_fit_scores():
+ candidates = [
+ os.path.join(os.path.dirname(__file__), "real-fit-report.json"),
+ os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "real-fit-report.json"),
+ os.path.join(FALLBACK_DIR, "data", "real-fit-report.json"),
+ "/app/agent-evolution/data/real-fit-report.json",
+ ]
+ for path in candidates:
+ if path and os.path.isfile(path):
+ try:
+ with open(path, "r", encoding="utf-8") as f:
+ data = json.load(f)
+ return data.get("fit_scores", {})
+ except Exception:
+ continue
+ return {}
+
+
+def build_state_from_md():
+ agents_dir = find_dir(".kilo/agents")
+ commands_dir = find_dir(".kilo/commands")
+
+ agents = []
+ if agents_dir:
+ for f in sorted(glob.glob(os.path.join(agents_dir, "*.md"))):
+ fm = parse_frontmatter(f)
+ if fm and fm.get("model"):
+ agents.append({
+ "name": os.path.basename(f).replace(".md", ""),
+ "model": fm.get("model", ""),
+ "mode": fm.get("mode", "subagent"),
+ "description": fm.get("description", ""),
+ "category": infer_category(fm.get("mode", ""), os.path.basename(f)),
+ "fit_score": None,
+ "model_meta": None,
+ })
+
+ commands = []
+ if commands_dir:
+ for f in sorted(glob.glob(os.path.join(commands_dir, "*.md"))):
+ fm = parse_frontmatter(f)
+ if fm and fm.get("model"):
+ commands.append({
+ "name": os.path.basename(f).replace(".md", ""),
+ "model": fm.get("model", ""),
+ "mode": fm.get("mode", "command"),
+ "description": fm.get("description", ""),
+ "fit_score": None,
+ })
+
+ model_stats = {}
+ for a in agents:
+ model_stats[a["model"]] = model_stats.get(a["model"], 0) + 1
+ for c in commands:
+ model_stats[c["model"]] = model_stats.get(c["model"], 0) + 1
+
+ return {
+ "generated": datetime.now(timezone.utc).isoformat().replace("+00:00", "") + "Z",
+ "total_agents": len(agents),
+ "total_commands": len(commands),
+ "model_distribution": model_stats,
+ "agents": agents,
+ "commands": commands,
+ }
+
+
+def build_state():
+ dashboard = load_dashboard_data()
+ fit_scores = load_real_fit_scores()
+ if dashboard:
+ agents = dashboard.get("agents", [])
+ for a in agents:
+ key = a.get("name")
+ fs = fit_scores.get(key)
+ if fs:
+ a["fit_score"] = fs.get("fit")
+ a["fit_explanation"] = fs.get("explanation")
+ a["best_model"] = fs.get("model")
+ state = {
+ "generated": datetime.now(timezone.utc).isoformat().replace("+00:00", "") + "Z",
+ "total_agents": dashboard.get("total_agents", 0),
+ "total_commands": len(dashboard.get("commands", [])),
+ "model_distribution": dashboard.get("model_distribution", {}),
+ "agents": agents,
+ "commands": dashboard.get("commands", []),
+ }
+ state["fit_scores"] = fit_scores
+ return state
+ return build_state_from_md()
+
+
+def infer_category(mode, filename):
+ f = filename.lower()
+ if "security" in f:
+ return "Security"
+ if "devops" in f:
+ return "DevOps"
+ if "frontend" in f or "flutter" in f:
+ return "Frontend"
+ if "backend" in f or "php" in f or "python" in f or "go" in f:
+ return "Backend"
+ if "test" in f or "sdet" in f:
+ return "QA"
+ return "Core"
+
+
+class Handler(http.server.BaseHTTPRequestHandler):
+ def do_GET(self):
+ if self.path == "/api/state":
+ state = build_state()
+ body = json.dumps(state, ensure_ascii=False).encode("utf-8")
+ self.send_response(200)
+ self.send_header("Content-Type", "application/json; charset=utf-8")
+ self.send_header("Access-Control-Allow-Origin", "*")
+ self.send_header("Cache-Control", "no-store")
+ self.end_headers()
+ self.wfile.write(body)
+ else:
+ self.send_response(404)
+ self.end_headers()
+
+ def log_message(self, format, *args):
+ pass # silent
+
+
+if __name__ == "__main__":
+ with socketserver.TCPServer(("0.0.0.0", PORT), Handler) as httpd:
+ print(f"[state-api] listening on :{PORT}")
+ httpd.serve_forever()
diff --git a/landing/assets/api.js b/landing/assets/api.js
new file mode 100644
index 0000000..1e5d5b8
--- /dev/null
+++ b/landing/assets/api.js
@@ -0,0 +1,373 @@
+async function loadRealState() {
+ try {
+ const res = await fetch('/api/state');
+ if (!res.ok) throw new Error('HTTP ' + res.status);
+ const state = await res.json();
+ renderAgentTable(state.agents);
+ renderCommandTable(state.commands);
+ updateHeroStats(state);
+ updateModelDistribution(state.model_distribution);
+ renderAnalytics(state);
+ console.log('[landing] real state loaded', state.generated);
+ } catch (e) {
+ console.error('[landing] failed to load real state:', e.message);
+ document.querySelector('.agents__table')?.insertAdjacentHTML(
+ 'afterbegin',
+ '⚠️ Не удалось загрузить реальные данные. Показаны fallback-значения.
'
+ );
+ }
+}
+
+function renderAgentTable(agents) {
+ const tbody = document.getElementById('agent-tbody');
+ if (!tbody) return;
+
+ const categories = {};
+ for (const a of agents) {
+ const cat = a.category || 'Core';
+ if (!categories[cat]) categories[cat] = [];
+ categories[cat].push(a);
+ }
+
+ let html = '';
+ for (const [cat, list] of Object.entries(categories)) {
+ html += `${escapeHtml(cat)}
+ ${escapeHtml(a.name)}
+ ${escapeHtml(cat)}
+ ${escapeHtml(a.description.slice(0, 40))}…
+ ${escapeHtml(modelShort)}
+ ${score} ${escapeHtml(a.mode)}
+ `;
+ }
+ }
+ tbody.innerHTML = html;
+}
+
+function renderCommandTable(commands) {
+ const tbody = document.getElementById('command-tbody');
+ if (!tbody) return;
+
+ let html = '';
+ for (const c of commands) {
+ const modelShort = c.model.replace('ollama-cloud/', '');
+ const score = modelScore(c.model);
+ html += `
+
+ /${escapeHtml(c.name)}
+ ${escapeHtml(c.mode)}
+ ${escapeHtml(c.description.slice(0, 40))}…
+ ${escapeHtml(modelShort)}
+ ${score} `;
+ }
+ tbody.innerHTML = html;
+}
+
+function updateHeroStats(state) {
+ const total = state.total_agents + state.total_commands;
+ const models = Object.keys(state.model_distribution).length;
+ document.querySelector('.hero__stats .stat:nth-child(1) .stat__value').textContent = state.total_agents + '+';
+ document.querySelector('.hero__stats .stat:nth-child(2) .stat__value').textContent = models;
+}
+
+function updateModelDistribution(dist) {
+ const container = document.getElementById('model-distribution');
+ if (!container) return;
+ const entries = Object.entries(dist).sort((a, b) => b[1] - a[1]);
+ const max = entries[0]?.[1] || 1;
+ let html = 'Реальная модельная дистрибуция ';
+ for (const [model, count] of entries) {
+ const pct = Math.round((count / max) * 100);
+ html += `
+
+
${escapeHtml(model.replace('ollama-cloud/', ''))}
+
+
${count}
+
+
+
`;
+ for (const [cat, list] of Object.entries(cats).sort()) {
+ html += `
+
+
`;
+ for (const a of list) {
+ const score = modelScore(a.model);
+ html += `
+ ${escapeHtml(a.name)}
+ ${score}
+
`;
+ }
+ html += '
';
+ }
+ html += '
+
${escapeHtml(cat)}
+
+
${n}
+
+
${escapeHtml(a.name.slice(0, 12))}
+
${score}
+
${escapeHtml(a.name)} — ${score}
+
+ /${escapeHtml(c.name)}
+ ${escapeHtml(modelShort)}
+ ${score} `;
+ }
+ tbody.innerHTML = html;
+}
+
+/* ===== FIT SCORE DRILL-DOWN MODAL ===== */
+const MODAL_HTML = `
+
+
+
×
+
+
+ 0
+ Fit Score
+
+
+
+
+
${d.label}
+
+
${pct}
+
Агент Категория Роль Модель Fit Вызывается
-
-
- ★ Core Development RequirementRefiner Core Формализация требований Qwen3-Coder 480B 92 Issue status: new HistoryMiner Core Поиск дублей в git-истории Qwen3-Coder 480B 92 Status: planned SystemAnalyst Core Архитектура, схемы, API GLM-5.1 82 Status: researching SdetEngineer Core Тесты до кода (TDD) Qwen3-Coder 480B 88 Status: designed LeadDeveloper Core Основная разработка Nemotron 3 Super 90 Status: testing FrontendDeveloper Core UI с мультимодальностью MiniMax M2.5 86 Когда нужен UI BackendDeveloper Core Node.js / Express Qwen3-Coder 480B 91 Когда нужен backend GoDeveloper Core Go + Gin + Echo + DB DeepSeek V4-Pro 88 Когда нужен Go backend PhpDeveloper Core Laravel / Symfony Qwen3-Coder 480B 87 Когда нужен PHP PythonDeveloper Core Django / FastAPI Qwen3-Coder 480B 90 Когда нужен Python FlutterDeveloper Core Flutter / Dart Qwen3-Coder 480B 86 Когда нужен Flutter DevopsEngineer Core Docker, K8s, CI/CD Kimi K2.6 88 Когда нужен deploy ☆ Quality Assurance CodeSkeptic QA Адверсариальное ревью MiniMax M2.5 85 Status: implementing TheFixer QA Итеративный фикс багов Kimi K2.6 90 Если ревью не прошло PerformanceEngineer QA N+1, memory leaks, perf DeepSeek V4-Pro 84 После CodeSkeptic SecurityAuditor QA OWASP, CVE, secrets DeepSeek V4-Pro 80 После Performance VisualTester QA Скриншоты, pixelmatch Qwen3-Coder 480B 82 Когда UI меняется BrowserAutomation QA Playwright E2E Qwen3-Coder 480B 87 E2E-тестирование ◆ Meta & Process Orchestrator Meta Главный диспетчер Kimi K2.6 92 Управление роутингом ReleaseManager Meta Git, semver, релизы GLM-5.1 76 Status: releasing Evaluator Meta Оценка эффективности GLM-5.1 84 Status: evaluated PromptOptimizer Meta Улучшение промптов Qwen3.6 Plus 84 Когда score < 7 ProductOwner Meta Чеклисты, лейблы, трекинг GLM-5.1 78 Управление задачами CapabilityAnalyst Meta Анализ пробелов в skills GLM-5.1 82 На старте задачи AgentArchitect Meta Создание новых агентов Kimi K2.6 86 Если нет подходящего агента WorkflowArchitect Meta Новые workflow-определения GLM-5.1 82 Новый workflow MarkdownValidator Meta Валидация Markdown DeepSeek V4-Pro 68 Перед созданием issue PipelineJudge Meta Объективный fitness-скор GLM-5.1 84 После Evaluator ArchitectIndexer Meta Индекс проекта .architect/ GLM-5.1 84 Перед любой задачей ● Cognitive Enhancement Planner Cognitive CoT / ToT / Plan-Reflect DeepSeek V4-Pro 88 Сложные задачи Reflector Cognitive Уроки из ошибок DeepSeek V4-Pro 84 После каждого агента MemoryManager Cognitive Контекст, векторный стор Qwen3.6 Plus 87 Управление памятью ♻ Security & Incident IncidentResponder SecOps Форензика, hardening, cleanup Kimi K2.6 90 Инцидент, компрометация
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