Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/understudylabs/understudy-agent-tools/optimize-local-model-compressionnpx skills add understudylabs/understudy-agent-tools --skill optimize-local-model-compressiongit clone --depth 1 https://github.com/understudylabs/understudy-agent-toolsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00098 | $0.02256 |
| Opus 5 | $0.00049 | $0.01128 |
| Sonnet 5 | $0.00020 | $0.00451 |
| Haiku 4.5 | $0.00010 | $0.00226 |
Grade A, and why
optimize-local-model-compression scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Local Model Compression
Compression (quantization to 4-bit) is mandatory for local deployment — it is the difference between a 9.5 GB model and a 3.6 GB model, between 23 tok/s and 45 tok/s. But standard compression methods optimize for general text quality (perplexity, MMLU) and silently destroy tool-call fidelity. This skill teaches the method we invented to fix that: outcome-optimized, layer-aware compression that protects the circuits responsible for structured output.
This is not theory. Every recommendation below is backed by measured results on Gemma 4 E2B through 31B, on a 103-row tool-call benchmark and a 14-task multi-turn agent board, on Apple Silicon (M5 Max, 128 GB), at zero API cost.
When to use
- The user's compressed model emits broken JSON, fails to call tools, or scores lower on tool-calling tasks than the hosted version.
- The user is choosing between quantization methods (naive 4-bit, QAT, OptiQ, or a custom conversion).
- The user wants to convert a model and is asking which settings matter.
- A local eval shows a model "can't do tool calls" but the hosted version can.
Safety Gates
- No conversion without explicit approval. Conversions are compute-heavy (15-30 min GPU time) and produce large artifacts. State the target model, BPW, and expected output size first.
- Background long conversions. Sensitivity probing takes 15-30 min; background it and keep working.
- Do not run conversions during meetings. GPU-bound work can freeze external monitors on Apple Silicon. Check for active display connections before starting.
The three things that matter
1. Group size must match the training noise profile
If the model is a QAT checkpoint (Google's q4_0 format), the downstream
quantization group_size must match the QAT block structure (32). The
default in most frameworks (MLX: 64, llama.cpp: varies) silently breaks QAT's
training-time hardening.
| Setting | Group size | Tool-name accuracy | Errors |
|---|---|---|---|
| QAT g64 (broken) | 64 (default) | 0.369 | 19 |
| QAT g32 (correct) | 32 (matched) | 0.379 | 8 |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 186 lines · 98 tokens per session scan A 5e879513c115
optimize-local-model-compression is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 98 tokens to every session and 2,256 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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