Gwen Digestor — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Gwen Digestor (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Model Context Protocol server for conversation compression.
Reduces token consumption by compressing conversation exchanges before they enter the LLM context window. Uses deterministic, embedding-free compression — no external APIs, no GPU required.
digest_input, compress_response, cache_reference, session_stats📊 View the Token Reduction Report — a professional breakdown with compression metrics and visual charts.
| Mode | Level | Strategy |
|---|---|---|
| checkin | 25% | Extract structured metrics (pain, sleep, energy, food, weight, stress) |
| task | 50% | Strip filler words, remove greetings/hedges |
| casual | 75% | Light structural compression |
| narrative | 95% | Preserve detail with minimal trimming |
digest_inputCompresses incoming messages by mode. Strips conversational filler, extracts health metrics in checkin mode, removes boilerplate in task mode.
compress_responseCompresses outgoing responses with mode-aware sentence truncation.
cache_referenceGzip-compressed key-value store for reference texts. Configurable TTL (default 24h).
session_statsReal-time token savings dashboard showing compression rates across all calls.
pip install mcp fastmcpRegister as an MCP server in your client config:
{
"mcpServers": {
"gwen-digestor": {
"command": "python3",
"args": ["/path/to/gwen_digestor.py"],
"transport": "stdio"
}
}
}Then call the tools from your LLM session:
digest_input("hey, just checking in — slept okay, pain 3/10 today, stress 5/10")
→ [MODE:checkin@25%] SLEEP:okay|PAIN:3/10|STRESS:5/10~/.gwen-digestor/cache.db (SQLite, gzip-compressed blobs)~/.gwen-digestor/stats.json (persistent across sessions)mcp, fastmcpMIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.