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Headroom-inspired multi-type compression — text, JSON, logs, tool output, code. Reversible. MCP server compatible.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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)