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Synaptic-Tuner: Skill for Claude Code

.agents/skills/upload-deployment/SKILL.md

upload-deployment is a skill for Claude Code from ProfSynapse/Synaptic-Tuner. It costs 90 tokens per session (2,567 once invoked), scanned A, original, MIT.

Instructions for saving trained machine-learning models and publishing them to Hugging Face, a service for sharing models and datasets.

In plain words
What is it for?
Use it to upload LoRA or merged models, create GGUF files for compatible local tools, merge adapters, generate model cards, and run the full train-upload-evaluate workflow.
Why use it?
It organizes different ways to save a model or LoRA adapter, a small set of learned model changes, and avoids repeating the upload workflow manually.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: installed under .agents/ (shared by several agents).

This is ProfSynapse/Synaptic-Tuner's own configuration. It tells Claude Code how to work on Synaptic-Tuner itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Synaptic-Tuner configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python scripts/cloud_gguf_convert.py \.

not rated 28repo changed 1mo ago A scan SkillSpector: warn 90 tokens original MIT
View source ↗ ProfSynapse/Synaptic-Tuner
Reuse

Borrowing it

Nothing to install: this file belongs to ProfSynapse/Synaptic-Tuner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/ProfSynapse/Synaptic-Tuner/main/.agents/skills/upload-deployment/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ProfSynapse/Synaptic-Tuner

Made for: Claude Code.

Wrote 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.

agentmods badge for upload-deployment

README.md
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Your own site
<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/upload-deployment"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/upload-deployment/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for upload-deployment

Your own site · 80×15
<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/upload-deployment"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/upload-deployment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,567 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 142
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.02567
Opus 5.5 $0.00036 $0.01027
Sonnet 5.5 $0.00018 $0.00513
Haiku 4.5 $0.00009 $0.00257

Measured 1mo ago against content hash aa87054439fb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

upload-deployment 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/upload_model.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/upload-deployment/SKILL.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Upload & Deployment

Upload trained models to HuggingFace with optional GGUF conversion and model card generation.

For cloud training, provider-native storage remains the source of truth. Hugging Face Hub publishing is optional and only applies to final_model.

Quick Reference

Task Command
Interactive menu ./run.sh → Upload
Upload merged 16-bit python3 .skills/upload-deployment/scripts/upload_model.py MODEL_PATH user/repo --save-method merged_16bit
Upload with GGUF python3 .skills/upload-deployment/scripts/upload_model.py MODEL_PATH user/repo --save-method merged_16bit --create-gguf
Upload with imatrix-calibrated GGUF ... --create-gguf --gguf-quantizations Q4_K_M IQ3_XXS --gguf-calibration Datasets/train.jsonl
Upload LoRA only python3 .skills/upload-deployment/scripts/upload_model.py MODEL_PATH user/repo --save-method lora
Merge LoRA manually ./run.sh → Merge LoRA
Convert to GGUF only ./run.sh → Convert
Convert an already-merged model python -m shared.upload.converters.gguf_reliable MERGED_DIR OUT_DIR --merged --quants Q4_K_M [--calibration train.jsonl]
Cloud GGUF conversion python tuner.py cloud-run --job-config Trainers/recipes/gguf_conversion.yaml --yes
Full pipeline ./run.sh → Full Pipeline (Train → Upload → Eval)

Save Strategies

Strategy Size (7B) GPU Required Best For
lora ~100-500 MB No Sharing adapters, fast upload
merged_16bit ~14 GB Yes Production inference, GGUF source
merged_4bit ~4 GB Yes Smaller footprint, slight quality loss

GGUF Quantizations

Format Size (7B) Quality Use Case
Q8_0 ~7 GB Highest Best quality, more RAM
Q5_K_M ~5 GB High Good balance
Q4_K_M ~4 GB Good Most popular, efficient
IQ3_XXS / IQ2_* ~2.5-3 GB Lower Very low RAM; requires a calibration dataset (imatrix)

Read the full file on GitHub · 168 lines

Files

What ships with it

6 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.

Changes

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.

  1. 1mo ago Changed · +12 lines aa87054439fb
  2. 2mo ago First seen · 156 lines · 90 tokens per session scan A 1800749ecd99

Subscribe to this mod's changes

upload-deployment is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (28 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 2,567 once invoked, about $0.0004 per session on Opus 5.5. 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.

Data and AI
Related

upload-deployment compared with skills from other repositories

upload-deployment next to 6 skills from other repositories
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upload-deploymentProfSynapse/Synaptic-TunerSkill283d agoMITA90Claude Code
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training-checkwanshuiyin/Auto-claude-code-research-in-sleepSkill19k3d agoMITA35Claude Code
nemo-automodel-launcher-configNVIDIA/skillsSkill3.9kyesterdayApache-2.0A30Claude Code
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