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 skills add rchaz/tunelab --skill tune-traingit clone --depth 1 https://github.com/rchaz/tunelabWrote 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/rchaz/tunelab/tune-train)<a href="https://agentmods.dev/skills/rchaz/tunelab/tune-train"><img src="https://agentmods.dev/badge/skills/rchaz/tunelab/tune-train.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 5.1 | $0.00129 | $0.05179 |
| Opus 5 | $0.00064 | $0.02589 |
| Sonnet 5 | $0.00026 | $0.01036 |
| Haiku 4.5 | $0.00013 | $0.00518 |
Grade A, and why
tune-train scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Verify before ANY download** — it is one curl; repo names churn, and a dead multi-GB pull is just the expensive version: How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tune-train — local training on Apple Silicon (MLX-LM)
Drives mlx_lm.lora over a validated data/ directory from tune-data (train.jsonl/valid.jsonl/test.jsonl). Full verified CLI reference: references/mlx-reference.md (mlx-lm 0.31.3). <skill-dir> below = the directory containing this SKILL.md; run commands from the user's project workdir.
Teaching default (explain-why protocol): every step you run gets four short lines before — What we're doing · Why (the failure it prevents) · Expect (healthy output) · Read (how to interpret what came out) — and one line after connecting result → next decision. One-liners, not essays. Define jargon inline on first use, pointing at the bundled concepts files for depth (plugin root, ../../concepts/ relative to this file). If the user says "skip the teaching" (or is clearly expert): drop Why/Expect/Read, keep What + the result reading.
Stop-and-ask points (pre-registration; these exactly, nowhere else): the level recommendation (tune-decide), the labeling prompt (tune-data), the acceptance bar AND metric set (registered by tune-decide at decision time; must be on disk before any training launch), and any expensive run — which here means every training launch (Step 4).
Step 0 — Read the project state from disk FIRST
Before asking the user anything:
- Read
EXPERIMENT-LOG.mdin the workdir. tune-decide wrote the interview summary and level decision there; tune-data wrote data provenance. Never re-ask what's already answered. No level decision for this task → do not train; route to tune-decide first. tune-train assumes a validated Level 2/3 decision — for fixed-label outputs especially, a Level-1 classifier usually makes this whole skill unnecessary. - Scan
runs/*/state.json. For any run with"status": "running": is the PID alive (ps -p <pid>)? Is the log tail fresh (tail -n 30 <log_path>, recent mtime)? Alive + fresh → offer to re-attach and go straight to Step 5 monitoring. Dead with iters remaining → set"status": "interrupted"and offer the Step 6 resume.
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.
- 7d ago First seen · 219 lines · 129 tokens per session scan A a98492654ecb
tune-train is a skill published in the GitHub repository rchaz/tunelab (6 stars, last pushed 1mo ago), licensed MIT. It adds 129 tokens to every session and 5,179 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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