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/local-distillation-labnpx skills add understudylabs/understudy-agent-tools --skill local-distillation-labgit clone --depth 1 https://github.com/understudylabs/understudy-agent-toolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/understudylabs/understudy-agent-tools/local-distillation-lab)<a href="https://agentmods.dev/skills/understudylabs/understudy-agent-tools/local-distillation-lab"><img src="https://agentmods.dev/badge/skills/understudylabs/understudy-agent-tools/local-distillation-lab.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00092 | $0.01915 |
| Opus 5 | $0.00046 | $0.00958 |
| Sonnet 5 | $0.00018 | $0.00383 |
| Haiku 4.5 | $0.00009 | $0.00192 |
Grade A, and why
local-distillation-lab 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 4d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local Distillation Lab
Train a local student model and measure which post-training method actually moves a captured workload — without spending on hosted training. The student samples/learns on your Mac; the only optional spend is a frontier teacher you can almost always avoid.
This is the missing weight-update rung: optimize-workload (GEPA) explicitly does not
train; prepare-verifier-handoff jumps to hosted RL. This skill is what sits between them.
When To Use
- The developer has a captured workload with a verifiable reward (final-state validator, recall/precision-vs-gold, etc.) and frozen train/dev/holdout splits.
- Prompt optimization has plateaued and the question is now "can a weight update close the gap."
- They want a method comparison (SFT-RS vs off-policy distill vs pedagogical/OPSD), not a single blind training run.
If the workload only needs prompt/route changes, use
../optimize-workload/SKILL.md. If it genuinely needs hosted
multi-step RL, use ../prepare-verifier-handoff/SKILL.md.
Safety Gates
- Local-first: rollouts, training, and eval run on-device. The only network call is an optional teacher; prefer the privileged self-teacher (student + gold/ICL) so nothing leaves the box.
- Get explicit approval before any model download, frontier-teacher spend, or hosted handoff.
- Never claim a win on oracle-tool / answer-leaked settings (e.g. AutomationBench
limited_zapierhands the model the gold tools). Report the realistic-setting number alongside. - Holdout is sealed until a candidate adapter is frozen.
The method taxonomy (name the arms)
Dense-biased post-training methods differ by where the bias points and how concentrated it is (the concentration axis is what causes collapse):
| Arm | What | Bias | Note |
|---|---|---|---|
| B | baseline, no train | — | the floor |
| S | rejection-sampling SFT (STaR) | toward student's own passes | shifts curve up, same ceiling |
| O | off-policy distillation | toward a same-family teacher's completions | recipe-matched = cheap signal |
| P | pedagogical / OPSD-style | toward teacher, surprisal-gated | down-weights unlearnable tokens |
What ships with it
4 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.
- 4d ago First seen · 119 lines · 92 tokens per session scan A f2d12a463979
local-distillation-lab is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 92 tokens to every session and 1,915 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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