Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Rigor Train skill for deep learning research repositories. An agent skill from lllllllama/RigorPilot-Skills.
Install Run Train
$ npx skills add lllllllama/RigorPilot-Skills --skill run-train -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lllllllama/RigorPilot-Skills run-train --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/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/run-train .claude/skills/run-train && 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 "run-train" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/run-train into .claude/skills/run-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-train", 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/lllllllama/RigorPilot-Skills/tree/main/skills/run-trainType 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 lllllllama/RigorPilot-Skills --skill run-train -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lllllllama/RigorPilot-Skills run-train --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/run-train .agents/skills/run-train && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "run-train" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/run-train into .agents/skills/run-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-train", 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 lllllllama/RigorPilot-Skills --skill run-train -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lllllllama/RigorPilot-Skills run-train --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/run-train .cursor/skills/run-train && 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 "run-train" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/run-train into .cursor/skills/run-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-train", 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/lllllllama/RigorPilot-Skills.git --path skills/run-train--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 lllllllama/RigorPilot-Skills --skill run-train -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lllllllama/RigorPilot-Skills run-train --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/run-train .gemini/skills/run-train && 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 "run-train" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/run-train into .gemini/skills/run-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-train", 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 lllllllama/RigorPilot-Skills run-trainInstalls 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 lllllllama/RigorPilot-Skills --skill run-train -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/run-train .github/skills/run-train && 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 "run-train" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/run-train into .github/skills/run-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-train", 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 lllllllama/RigorPilot-Skills --skill run-train -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lllllllama/RigorPilot-Skills run-train --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/run-train .opencode/skills/run-train && 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 "run-train" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/run-train into .opencode/skills/run-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-train", 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.
Facts
- Skill name
run-train- GitHub stars
- 497
- Used in
- 1 other repo
- Token cost
- ~633 tokens
- SKILL.md length
- 224 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 11
- Repo updated
- First seen
- Licence
- MIT
At a glance
Rigor Train skill for deep learning research repositories. An agent skill from lllllllama/RigorPilot-Skills.
- Selected training command should be run conservatively for startup verification
- SKILL.md covers When to apply, When not to apply, Clear boundaries and Input expectations, plus 2 more sections
- Runs Python scripts from its folder
- Short-run verification
What it does
Run Train is an agent skill from lllllllama/RigorPilot-Skills. Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized trainoutputs/. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.
Its SKILL.md is about 630 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/training-policy.md` and `scripts/run_training.py`).
It sits in AI & LLM Engineering, covering Deep learning. The repository describes itself as: README-first research reproduction skills with bounded execution, auditable evidence, and byte-preserving README annotations. The licence is MIT.
When your agent uses it
- Selected training command should be run conservatively for startup verification
- Short-run verification
- Metric evidence written to standardized trainoutputs/
- Environment setup
Example prompts
- “/run-train”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit fb3ccdf. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Run Train loads about 633 tokens when it runs, and up to ~908 if it reads all its reference files. Until then it costs about 114 tokens; SKILL.md has 224 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.
Safety
Auto-check passed
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); the scripts in this folder are not scanned.
SKILL.md
The full file from lllllllama/RigorPilot-Skills at commit fb3ccdf, republished under its MIT licence (© lllllllama). 224 words, ~633 tokens.
Download SKILL.mdSave it as
.claude/skills/run-train/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- run-train
- description
- Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.
run-train
Use this as the Rigor Train skill. The installed slug remains run-train for
compatibility.
Use the shared operating principles in
../ai-research-reproduction/references/agent-operating-principles.md; this skill should keep
training evidence bounded while leaving repository-specific monitoring details
to the model.
When to apply
- When the training command has already been selected and should be executed conservatively.
- When the researcher wants startup verification, short-run verification, full training kickoff, or resume handling.
- When the run needs structured training status, checkpoint, and metric reporting.
When not to apply
- When the main task is environment setup or asset download.
- When the researcher wants inference-only or evaluation-only execution.
- When the task is speculative exploration, multi-variant sweeps, or autonomous idea implementation.
- When the user still needs repository intake or paper gap resolution.
Clear boundaries
- This skill executes a selected training command and normalizes the resulting evidence.
- It does not choose the overall research goal on its own.
- It does not own exploratory branching or speculative code adaptation.
- It should record partial, blocked, resumed, and kicked-off states clearly.
- It should preserve reproducibility context such as configs, seeds, checkpoints, logs, metrics, and runtime assumptions when available.
Input expectations
- selected training goal
- runnable training command
- environment and asset assumptions
- run mode such as startup verification, short-run verification, full kickoff, or resume
Output expectations
train_outputs/SUMMARY.mdtrain_outputs/COMMANDS.mdtrain_outputs/LOG.mdtrain_outputs/SCIENTIFIC_CHANGELOG.mdtrain_outputs/COMPARABILITY_REPORT.mdtrain_outputs/status.json
Notes
Use references/training-policy.md, ../ai-research-reproduction/references/deep-learning-experiment-principles.md, scripts/run_training.py, and scripts/write_outputs.py.
© lllllllama, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/run-train of lllllllama/RigorPilot-Skills.
- SKILL.md
- agents/openai.yaml
- references/training-policy.md
- scripts/run_training.py
- scripts/write_outputs.py
Open the folder on GitHubat commit fb3ccdf
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in lllllllama/RigorPilot-Skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Run Train 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 |
|---|---|---|---|---|---|---|
| Run Train this skilllllllllama/RigorPilot-Skills | 497 | 1 repos | ~633 | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Add Oponnx/onnx | 22k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Function Bodyonnx/onnx | 22k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Run Train
What does Run Train do?
Rigor Train skill for deep learning research repositories. An agent skill from lllllllama/RigorPilot-Skills. Run Train is an agent skill from lllllllama/RigorPilot-Skills. Rigor Train skill for deep learning research repositories.
When should I use Run Train?
Run Train fits situations like: selected training command should be run conservatively for startup verification; short-run verification; metric evidence written to standardized trainoutputs/; environment setup.
How do I install Run Train in Claude Code?
Run `npx skills add lllllllama/RigorPilot-Skills --skill run-train -a claude-code`. Or copy the skill folder (skills/run-train in lllllllama/RigorPilot-Skills) into .claude/skills/run-train in your project. Claude Code loads it when a task matches its description.
How do I install Run Train in Codex?
Run `npx skills add lllllllama/RigorPilot-Skills --skill run-train -a codex`. Or copy the skill folder (skills/run-train in lllllllama/RigorPilot-Skills) into .agents/skills/run-train in your project. Codex loads it when a task matches its description.
Can I use Run Train in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lllllllama/RigorPilot-Skills --skill run-train -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-train, .gemini/skills/run-train, .github/skills/run-train and .opencode/skills/run-train in your project.
What does Run Train need to run?
Going by SKILL.md and its folder, Run Train needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Run Train access the network?
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.
Is Run Train safe to install?
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Run Train use?
Run Train is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Run Train use?
About 633 tokens (SKILL.md is roughly 2.5k 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 275 tokens, read only when the agent opens those files.
What are the alternatives to Run Train?
Skills that share tags, products or a category with Run Train: Add Uint Support (pytorch/pytorch, 104k stars), Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Op (onnx/onnx, 22k stars) and CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Run Train?
lllllllama (a GitHub user) maintains it in lllllllama/RigorPilot-Skills, which has 497 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 23, 2026.
Source: lllllllama/RigorPilot-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.