Qwen Mtp Gguf
R6410418/Jackrong-llm-finetuning-guide
Complete agent-ready workflow for Qwen-family MTP or nextn GGUF conversion and release.
Track and visualize ML training experiments with Trackio. An agent skill from sickn33/agentic-awesome-skills.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackio --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hugging-face-trackio .claude/skills/hugging-face-trackio && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "hugging-face-trackio" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackio into .claude/skills/hugging-face-trackio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-trackio", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackioType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackio --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/hugging-face-trackio .agents/skills/hugging-face-trackio && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hugging-face-trackio" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackio into .agents/skills/hugging-face-trackio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-trackio", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackio --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/hugging-face-trackio .cursor/skills/hugging-face-trackio && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hugging-face-trackio" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackio into .cursor/skills/hugging-face-trackio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-trackio", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/hugging-face-trackio--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackio --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/hugging-face-trackio .gemini/skills/hugging-face-trackio && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hugging-face-trackio" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackio into .gemini/skills/hugging-face-trackio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-trackio", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackioInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/hugging-face-trackio .github/skills/hugging-face-trackio && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-trackio" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackio into .github/skills/hugging-face-trackio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-trackio", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackio --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/hugging-face-trackio .opencode/skills/hugging-face-trackio && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-trackio" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-trackio into .opencode/skills/hugging-face-trackio/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-trackio", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
hugging-face-trackioTrack and visualize ML training experiments with Trackio. An agent skill from sickn33/agentic-awesome-skills.
Hugging Face Trackio is an agent skill from sickn33/agentic-awesome-skills. Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI).
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `.claude-plugin/plugin.json`, `references/alerts.md` and `references/logging_metrics.md`).
It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Python and Hugging Face. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Hugging Face Trackio loads about 1.4k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 55 tokens; SKILL.md has 463 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its Apache-2.0 licence (© sickn33). 463 words, ~1,351 tokens.
.claude/skills/hugging-face-trackio/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.
| Task | Interface | Reference |
|---|---|---|
| Logging metrics during training | Python API | references/logging_metrics.md |
| Firing alerts for training diagnostics | Python API | references/alerts.md |
| Retrieving metrics & alerts after/during training | CLI | references/retrieving_metrics.md |
Use import trackio in your training scripts to log metrics:
trackio.init()trackio.log() or use TRL's report_to="trackio"trackio.finish()Key concept: For remote/cloud training, pass space_id — metrics sync to a Space dashboard so they persist after the instance terminates.
→ See references/logging_metrics.md for setup, TRL integration, and configuration options.
Insert trackio.alert() calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable:
trackio.alert(title="...", level=trackio.AlertLevel.WARN) — fire an alertINFO, WARN, ERRORKey concept for LLM agents: Alerts are the primary mechanism for autonomous experiment iteration. An agent should insert alerts into training code for diagnostic conditions (loss spikes, NaN gradients, low accuracy, training stalls). Since alerts are printed to the terminal, an agent that is watching the training script's output will see them automatically. For background or detached runs, the agent can poll via CLI instead.
→ See references/alerts.md for the full alerts API, webhook setup, and autonomous agent workflows.
Use the trackio command to query logged metrics and alerts:
trackio list projects/runs/metrics — discover what's availabletrackio get project/run/metric — retrieve summaries and valuestrackio list alerts --project <name> --json — retrieve alertstrackio show — launch the dashboardtrackio sync — sync to HF SpaceKey concept: Add --json for programmatic output suitable for automation and LLM agents.
→ See references/retrieving_metrics.md for all commands, workflows, and JSON output formats.
import trackio
trackio.init(project="my-project", space_id="username/trackio")
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})
trackio.finish()trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --jsonWhen running experiments autonomously as an LLM agent, the recommended workflow is:
trackio.alert() calls for diagnostic conditionstrackio list alerts --project <name> --json --since <timestamp> to check for new alertstrackio get metric ... to inspect specific valuesimport trackio
trackio.init(project="my-project", config={"lr": 1e-4})
for step in range(num_steps):
loss = train_step()
trackio.log({"loss": loss, "step": step})
if step > 100 and loss > 5.0:
trackio.alert(
title="Loss divergence",
text=f"Loss {loss:.4f} still high after {step} steps",
level=trackio.AlertLevel.ERROR,
)
if step > 0 and abs(loss) < 1e-8:
trackio.alert(
title="Vanishing loss",
text="Loss near zero — possible gradient collapse",
level=trackio.AlertLevel.WARN,
)
trackio.finish()Then poll from a separate terminal/process:
trackio list alerts --project my-project --json --since "2025-01-01T00:00:00"© sickn33, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files (references) in skills/hugging-face-trackio of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit b84d35a
We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.
Hugging Face Trackio next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hugging Face Trackio this skillsickn33/agentic-awesome-skills | 47k | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Qwen Mtp GgufR6410418/Jackrong-llm-finetuning-guide | 1.7k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Hugging Face Vision Trainerhuggingface/skills | 11k | 1 repos | ~7.5k | Automated safety check: Pass | Apache-2.0 | |
| Edge Bringupexeex/edge-cores | 110 | — | ~1.7k | Automated safety check: Notes | Apache-2.0 | |
| Publish Tracelab Huggingfaceuw-syfi/TraceLab | 142 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 |
R6410418/Jackrong-llm-finetuning-guide
Complete agent-ready workflow for Qwen-family MTP or nextn GGUF conversion and release.
Orchestra-Research/AI-Research-SKILLs
Shows how to load, train and use fast Hugging Face tokenizers, with BPE, WordPiece and Unigram models, padding, truncation and alignment tracking.
huggingface/skills
Trains and fine-tunes object detection, image classification and SAM or SAM2 segmentation models on Hugging Face Jobs cloud GPUs and saves the results to the Hub.
exeex/edge-cores
Prepare a macOS or Ubuntu machine for edge-e3 development, diagnose missing Verilator/LLVM/Python dependencies, initialize the public repository, and answer or act on the example prompts in the root…
uw-syfi/TraceLab
Prepare, publish, refresh, or validate the TraceLab public dataset on Hugging Face under UW-SyFI/TraceLab.
huggingface/skills
Builds reusable command line scripts that fetch, enrich or process data from the Hugging Face API, aimed at chained, repeated or automated tasks.
sickn33/agentic-awesome-skills
Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.
sickn33/agentic-awesome-skills
Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.
sickn33/agentic-awesome-skills
Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.
sickn33/agentic-awesome-skills
Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.
sickn33/agentic-awesome-skills
Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.
sickn33/agentic-awesome-skills
Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.
Works with
Categories
Track and visualize ML training experiments with Trackio. An agent skill from sickn33/agentic-awesome-skills. Hugging Face Trackio is an agent skill from sickn33/agentic-awesome-skills. Track and visualize ML training experiments with Trackio.
Hugging Face Trackio fits situations like: logging metrics during training (Python API); firing alerts for training diagnostics; retrieving/analyzing logged metrics (CLI).
Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a claude-code`. Or copy the skill folder (skills/hugging-face-trackio in sickn33/agentic-awesome-skills) into .claude/skills/hugging-face-trackio in your project. Claude Code loads it when a task matches its description.
Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a codex`. Or copy the skill folder (skills/hugging-face-trackio in sickn33/agentic-awesome-skills) into .agents/skills/hugging-face-trackio in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hugging-face-trackio, .gemini/skills/hugging-face-trackio, .github/skills/hugging-face-trackio and .opencode/skills/hugging-face-trackio in your project.
SKILL.md names no scripts, command-line tools or credentials: Hugging Face Trackio is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Hugging Face Trackio is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Hugging Face Trackio: Qwen Mtp Gguf (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars), Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face Vision Trainer (huggingface/skills, 11k stars) and Edge Bringup (exeex/edge-cores, 110 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.
Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.