القائمة
Trajectory Learning for Botte Secrète — stores solver trajectories and searches similar past optimizations to inform future decisions
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
Memory as a learnable skill — store, recall, compress, and consolidate agent memories. Inspired by Stanford AutoMem.
Auto-decide whether a task runs on a LOCAL model or a CLOUD model (DeepSeek, GLM, Nemotron, Grok, Gemma, …) from an automatic effort estimate, and run multi-model fusion (cascade, draft→refine, vote). Use when the user wants automatic local-vs-cloud routing, to add cloud LLM providers, to make local and cloud models collaborate, or mentions effort-based routing, model fusion/ensemble, OpenRouter, DeepSeek, GLM, Nemotron, or Grok.
Fenêtres de contexte pour boucles rétroactives — charge seulement les deltas.
Optimisations DAG/RAG — waves, pruning, memoization, routing.
Generate one self-contained, timestamped HTML dashboard of the system's cost picture — routing savings (control loop), metric trends, current metrics, and the cost of outstanding fixes. Also renders as a live ANSI terminal view (--tui, --watch) and serves a live HTTP API (api.py). Use when the user wants a single visual view of cost/savings/health over time, or a live terminal view they don't have to open a browser for.
Append-only JSONL decision log (.botte/events.jsonl) that every filter in the belt writes to — routing, cache hits, escalations, micro-NN outputs. The single source of truth demo mode, the live dashboard, and session replay all read from. Use when you want to see or emit a live feed of routing/cache/escalation decisions, or when building a tool that needs to watch the belt work in real time.
| name | trajectory |
| description | Trajectory Learning for Botte Secrète — stores solver trajectories and searches similar past optimizations to inform future decisions |
| version | 1.0.0 |
| author | Hermes Agent |
| license | MIT |
| metadata | {"hermes":{"tags":["botte","trajectory","memory","learning"],"related_skills":["solvers","trajectory-memory"]}} |
Stocke les trajectoires d'optimisation des solveurs déterministes. Permet de retrouver des solutions similaires déjà calculées → 0 token, 0 latence.
Compatible avec ~/.hermes/scripts/trajectory_search.py (Hermes Trajectory Memory).
| Fichier | Rôle |
|---|---|
skills/trajectory/__init__.py | capture(), search(), load(), stats() |
skills/trajectory/store/trajectories.jsonl | Stockage JSON Lines |
from skills.trajectory import capture, search, get_stats
# Capturer une trajectoire
tid = capture(
solver="bin_pack",
task="pack 3 database backups into 10GB capacity",
parameters={"items": [("db1", 4), ("db2", 7), ("db3", 3)], "capacity": 10},
result={"bins": [...], "bin_count": 2},
latency=0.002,
tokens_saved=500,
)
# Rechercher des trajectoires similaires
hits = search("pack items into capacity")
for h in hits:
print(f"[{h['score']:.2f}] {h['trajectory']['task']}")
# Statistiques
stats = get_stats()
print(f"Total: {stats['total']}, Tokens saved: {stats['total_tokens_saved']}")
Dans skills/solvers/solvers.py, chaque fonction peut capturer sa trajectoire:
from skills.trajectory import capture
def assign_balanced(tasks, workers):
result = ... # existing logic
capture("assign_balanced", f"assign {len(tasks)} tasks to {len(workers)} workers",
{"tasks": tasks, "workers": workers}, result, latency=latency())
return result
Les fichiers sont compatibles: Botte peut lire les trajectoires Hermes et vice-versa:
# Lire les trajectoires Hermes
from skills.trajectory import load
hermes_trajs = load("/home/redgamer/.hermes/trajectory_store/trajectories.jsonl")