القائمة
Dar a cron jobs e sub-agentes isolados contexto completo da sessão principal via query direta ao Hermes sessions DB.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | context-aware-delegation |
| description | Dar a cron jobs e sub-agentes isolados contexto completo da sessão principal via query direta ao Hermes sessions DB. |
| hermes-adapted | true |
| hermes-version | 1.0.0 |
✅ Adaptado para Hermes — 20/04/2026
sessions_history → session_search() + query direta SQLitesessions_spawn → delegate_task()cronjob toolsend_message()Cron jobs e sub-agentes executam em sessões isoladas — modelos baratos mas sem ver o contexto da conversa principal. Alternativa (tudo no main session) = caro.
Cron jobs e sub-agentes consultam diretamente o hermes_sessions.db para obter contexto completo da sessão principal. Conseguem contexto rico a custo MiniMax.
Custo por execução:
Main session (MiniMax): ~$0.01-0.05
Cron isolado (MiniMax): ~$0.001-0.01
Contexto: ✅ Completo
Poupança: ~10x
# No prompt do cron job, usar:
session_search(query="o que fizemos hoje", limit=10)
# Ou query direta SQLite:
python3 -c "
import sqlite3
conn = sqlite3.connect('$HOME/.hermes/hermes_sessions.db')
cur = conn.cursor()
cur.execute(\"SELECT role, content FROM sessions WHERE session_key LIKE '%main%' ORDER BY ROWID DESC LIMIT 20\")
for row in cur.fetchall(): print(row[0], ':', row[1][:200])
"
# Sub-agente com contexto:
delegate_task(
goal="Analisar o código do projeto X. Primeiro usa session_search para entender o contexto da conversa sobre X.",
context="Projeto: Método TEN | Pilares: auto-evolução, proatividade",
toolsets=['terminal', 'file', 'web']
)
# Criar cron job que consulta sessão principal antes de executar
# Prompt inclui:
# 1. session_search("o que aconteceu ontem", limit=20)
# 2. ler ~/self-improving/memory/memory.md
# 3. executar tarefa (report, email, etc.)
# 4. send_message() com resultado
# Criar via cronjob tool:
# Schedule: 08:00 daily
# Prompt: "1. session_search('Álvaro Bianinho sessão', limit=30)
# 2. ler ~/self-improving/memory/memory.md
# 3. Gerar briefing com:
# - Resumo overnight (do session_search)
# - Tarefas pendentes (da wake-state)
# - Dados relevantes (da RAG knowledge base)
# 4. send_message(target='telegram', message=briefing)"
1. Cron job dispara (modelo barato)
↓
2. session_search() → consulta sessão principal
↓
3. Lê memory files → 获取 contexto persistente
↓
4. Executa tarefa com contexto completo
↓
5. send_message() → entrega resultado
| Abordagem | Modelo | Contexto | Custo/Execução |
|---|---|---|---|
| Main session | MiniMax | Full | ~$0.02 |
| Cron isolado (cego) | MiniMax | Nenhum | ~$0.002 |
| Cron + context-aware | MiniMax | Full | ~$0.002 |
~/.hermes/wake-state/tasks.json~/KnowledgeBase/hybrid_search.py para contexto vetorial~/self-improving/memory/memory.md para HOT memory~/KnowledgeBase/knowledge_db/ para conhecimento domain-specific#!/usr/bin/env python3
"""Helper para cron jobs obterem contexto da sessão principal."""
import sqlite3, os, sys
from pathlib import Path
def get_recent_context(limit=20, session_filter="main"):
db_path = Path.home() / ".hermes" / "hermes_sessions.db"
if not db_path.exists():
return "No session DB found."
conn = sqlite3.connect(str(db_path))
cur = conn.cursor()
cur.execute(f"""
SELECT role, content FROM sessions
WHERE session_key LIKE '%{session_filter}%'
ORDER BY ROWID DESC LIMIT {limit}
""")
rows = cur.fetchall()
conn.close()
if not rows:
return "No recent context found."
return "\n".join([f"[{r[0]}] {r[1][:300]}" for r in reversed(rows)])
if __name__ == "__main__":
limit = int(sys.argv[1]) if len(sys.argv) > 1 else 20
print(get_recent_context(limit))
Guardar em: ~/.hermes/skills/context-aware-delegation/scripts/get_context.py
| Job | Schedule | Descrição |
|---|---|---|
| Bianinho Morning Briefing | 0 8 * * 1-5 | Morning briefing: contexto Telegram + wake-state tasks + HOT memory |
Job ID: 5cc5608e58de — criado 20/04/2026
Source: memory/context-switch-optimizer/
Detects "continuous / gradual drift / hard switch" using keyword Jaccard similarity, weak-association substrings, and time decay. Monitors tokens, triggers compression and cleanup.
| Action | Condition | Behavior |
|---|---|---|
load_memory | No current topic and no history | Create topic, optionally load from memory/topic |
continuous | Similarity >= similarity_threshold (0.7) | Append to history, no compression |
drift_compress | Similarity in [continuity_threshold, similarity_threshold) | Compress weakly related historical turns |
switch_context | Similarity < continuity_threshold (0.35) | Hard switch, change memory, increment switch_count |
similarity_threshold: 0.7 (continuous vs drift boundary)continuity_threshold: 0.35 (drift vs switch boundary)compress_relevance_threshold: 0.3 (below this in drift_compress = compressed)token_limit: 80000, compression_threshold: 56000| Variable | Maps To | Notes |
|---|---|---|
CONTEXT_HISTORY_SIZE | max_topic_history | 1-100 |
MEMORY_SEARCH_DEPTH | search_depth | 1-3 |
TOKEN_OPTIMIZER_ENABLED | token_optimization.enabled | true/false |
CONTEXT_SWITCH_LOG_LEVEL | log_level | DEBUG/INFO/WARNING/ERROR |
~/.hermes/memory/context_switch_state.json — TopicSummary format with is_compressed flagpython3 context-switch-optimizer.py [--config path] --process|--status|--reset|--test
Source: knowledge-base/contextual-recall/
Context-aware memory recall during conversations: domain detection, re-ranking, session cache.
from contextual_recall import contextual_recall, format_contextual_facts, clear_cache
facts = contextual_recall("query", days=7, top_k=5)
print(format_contextual_facts(facts))
query (str): conversation context — more specific = betterdays (int, default=7): only facts from last N daystop_k (int, default=5): number of facts to returnforce_all (bool, default=False): includes facts outside consolidationbiografia → words: álvaro, maryanne, brasileiro, psicoterapeuta
comunicação → words: resposta, prefere, estilo, directo, tom, linguagem
instruções → words: orquestrador, autonomia, liberdade, decidir, regra
memória → words: memória, memory, recall, facts
projectos → words: ten, apc, método, formação, curso, negócio
social → words: maryanne, esposa, equipa, estudantes
monitor = ProactiveRecall(silence=True)
awareness = monitor.process("user message")
if awareness.triggered:
context = monitor.format_for_context(awareness)
# inject context into response
~/KnowledgeBase/proactive_recall.py — ProactiveRecall engine~/KnowledgeBase/contextual_recall.py — domain detection + re-rank + cache~/KnowledgeBase/recall.py — base (no cache, no re-rank)~/KnowledgeBase/consolidate_session.py — nocturnal cron jobModuleNotFoundError: No module named 'proactive_recall' → cd ~/KnowledgeBase && source venv/bin/activateSource: memory/context-management/
Context serialization and knowledge management for long-running agent sessions.
Serialize and restore conversation context between sessions. Handles checkpointing, state recovery, and cross-session continuity.
Autores
Transforms the Hermes agent from a reactive question-answerer into a proactive autonomous executor. ARCHITECT takes any high-level goal, decomposes it into a dependency-aware task graph, executes each step with validation, self-corrects on failure, and delivers results — all without hand-holding. The missing execution layer for personal AI agents. Zero dependencies. Zero config. Works with any model. Pairs with apex-agent and agent-memoria for the complete autonomous agent stack.
Auto-reflective self-improvement skill — extracts learnings from corrections and success patterns, permanently encodes them into memory and skills. Philosophy: Correct once, never again.
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
Integrate existing autonomous agents (like Hermes) into multi-agent orchestration platforms (AionUI, LangChain, etc.) via ACP over stdio.
MiniMax Agent Platform (agent.minimax.io) — MaxHermes, MaxClaw, Skills marketplace. Relacao com Hermes Agent (NousResearch) e OpenClaw.
Paperclip AI agent operations — creating agents with hierarchy, autonomous operation via hierarchical issues, and troubleshooting. Use when: creating agents, setting up org hierarchy, recovering from errors, or monitoring agent health.