Menü
Prefix prune le contexte — arbre de préfixes, diffing, élague les sections inutilisées. Use when you want to cut 5-10% more tokens by removing dead context.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
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 | prefix-pruner |
| description | Prefix prune le contexte — arbre de préfixes, diffing, élague les sections inutilisées. Use when you want to cut 5-10% more tokens by removing dead context. |
Élimine les sections de contexte que l'agent n'utilise jamais.
| Stratégie | Description | Gain |
|---|---|---|
auto | Prefix tree + usage tracking | 5-10% |
aggressive | Supprime toute section < 0.3 usefulness | 10-15% |
conservative | Ne supprime que les sections jamais utilisées | 2-5% |
python -m skills.prefix_pruner.cli prune < context.txt
python -m skills.prefix_pruner.cli tree
python -m skills.prefix_pruner.cli stats