القائمة
Headroom-inspired multi-type compression — text, JSON, logs, tool output, code. Reversible. MCP server compatible.
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 | universal-compressor |
| description | Headroom-inspired multi-type compression — text, JSON, logs, tool output, code. Reversible. MCP server compatible. |
| version | 1.0.0 |
Headroom-inspired multi-type compression for botte-secrete. Reduces token usage by 40-90% depending on content type. Works as library, CLI, or MCP server.
| Content Type | Strategy | Typical Savings |
|---|---|---|
text | Dedup lines, collapse blanks | 0-30% |
json | Compact + truncate large arrays | 20-60% |
log | Pattern dedup + sampling | 80-98% |
tool_output | Head+tail, strip ANSI | 50-90% |
code | Strip comments, collapse imports | 20-40% |
auto | Auto-detect content type | Best effort |
from skills.universal_compressor import compress, restore
# Compress with auto-detection
result = compress(content)
print(f"{result.original_size} → {result.compressed_size} ({result.ratio:.0%})")
# Compress with type hint + reversibility
result = compress(big_log, content_type="log", reversible=True)
# Restore original
original = restore(result.reversible_key)