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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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