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Agent skill

Explore Run

by lllllllama in lllllllama/RigorPilot-Skills

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories.

MITAuto-check passedAI & LLM Engineering

Install Explore Run

skills CLI
$ npx skills add lllllllama/RigorPilot-Skills --skill explore-run -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lllllllama/RigorPilot-Skills explore-run --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/explore-run .claude/skills/explore-run && rm -rf skills-src

Use ~/.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/

Facts

Skill name
explore-run
GitHub stars
497
Used in
2 other repos
Token cost
~833 tokens
SKILL.md length
290 words
Files
5 (incl. scripts, references)
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories.

  • The researcher explicitly authorizes exploratory runs such as small-subset validation
  • SKILL.md covers When to apply, When not to apply, Clear boundaries and Ranking Semantics, plus 3 more sections
  • Runs Python scripts from its folder
  • Short-cycle guess-and-check

What it does

Explore Run is an agent skill from lllllllama/RigorPilot-Skills. Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in exploreoutputs/. Do not use for end-to-end exploration orchestration on top of currentresearch, trusted baseline execution, conservative training…

Its SKILL.md is about 830 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/execution-policy.md` and `scripts/plan_variants.py`).

It sits in AI & LLM Engineering, covering Deep learning and A/B testing. 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

  • The researcher explicitly authorizes exploratory runs such as small-subset validation
  • Short-cycle guess-and-check
  • Idle-GPU search
  • Quick transfer-learning trials

Example prompts

  • “/explore-run”

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

Explore Run loads about 833 tokens when it runs, and up to ~1k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 290 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~153
When it runs · the whole SKILL.md, loaded when a task matches
~833
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1k

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). 290 words, ~833 tokens.

Download SKILL.mdSave it as .claude/skills/explore-run/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
explore-run
description
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

explore-run

Use this as the Rigor Improve / Rigor Explore run leaf skill. The installed slug remains explore-run for compatibility.

Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md; this skill should guide candidate run planning while preserving model judgment about the active repo.

When to apply

  • When the researcher explicitly authorizes exploratory runs.
  • When the task is a small-subset validation, short-cycle training probe, batch sweep, idle-GPU search, or quick transfer-learning trial.
  • When the output should rank candidate runs rather than certify trusted success.

When not to apply

  • When the user wants trusted training execution or conservative verification.
  • When there is no explicit exploratory authorization.
  • When the task is repository setup, intake, or debugging.

Clear boundaries

  • This skill owns exploratory execution planning and summary only.
  • Use ai-research-explore instead when the task spans both current_research coordination and exploratory code changes.
  • It may hand off actual command execution to minimal-run-and-audit or run-train.
  • It should keep experiment state isolated from the trusted baseline.
  • It should prefer small-subset and short-cycle checks before heavier exploratory runs.
  • It should label run results as bounded evidence and explain when a comparison is not directly fair.

Ranking Semantics

  • Pre-execution candidate selection uses three factors: cost, success_rate, and expected_gain.
  • Default weights should stay conservative unless the researcher explicitly provides selection_weights.
  • Budget pruning still applies after scoring through max_variants and max_short_cycle_runs.
  • If runs are executed later, downstream ranking should switch to real execution evidence, not stay purely heuristic.

Variant Spec Hints

  • Use variant_axes to define the candidate dimension grid.
  • Use subset_sizes and short_run_steps to express exploratory run scale.
  • Use selection_weights to rebalance cost, success_rate, and expected_gain.
  • Use primary_metric and metric_goal so downstream ranking can order executed candidates consistently.

Output expectations

  • explore_outputs/CHANGESET.md
  • explore_outputs/SCIENTIFIC_CHANGELOG.md
  • explore_outputs/COMPARABILITY_REPORT.md
  • explore_outputs/TOP_RUNS.md
  • explore_outputs/status.json

Notes

Use references/execution-policy.md, ../ai-research-reproduction/references/explore-variant-spec.md, ../ai-research-reproduction/references/deep-learning-experiment-principles.md, scripts/plan_variants.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/explore-run of lllllllama/RigorPilot-Skills.

  • SKILL.md
  • agents/openai.yaml
  • references/execution-policy.md
  • scripts/plan_variants.py
  • scripts/write_outputs.py

Open the folder on GitHubat commit fb3ccdf

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in lllllllama/RigorPilot-Skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Explore Run 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.

Explore Run compared with similar skills
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Tokenwisemajiayu000/claude-skill-registry6662 repos~932Automated safety check: PassMIT
Sparse Autoencoder Training with SAELensOrchestra-Research/AI-Research-SKILLs13k6 repos~3.2kAutomated safety check: PassMIT
TransformerLens InterpretabilityOrchestra-Research/AI-Research-SKILLs13k4 repos~3kAutomated safety check: PassMIT
pyvene Causal InterventionsOrchestra-Research/AI-Research-SKILLs13k3 repos~3.5kAutomated safety check: PassMIT

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Categories

Questions about Explore Run

What does Explore Run do?

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Explore Run is an agent skill from lllllllama/RigorPilot-Skills. Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories.

When should I use Explore Run?

Explore Run fits situations like: the researcher explicitly authorizes exploratory runs such as small-subset validation; short-cycle guess-and-check; idle-GPU search; quick transfer-learning trials.

How do I install Explore Run in Claude Code?

Run `npx skills add lllllllama/RigorPilot-Skills --skill explore-run -a claude-code`. Or copy the skill folder (skills/explore-run in lllllllama/RigorPilot-Skills) into .claude/skills/explore-run in your project. Claude Code loads it when a task matches its description.

How do I install Explore Run in Codex?

Run `npx skills add lllllllama/RigorPilot-Skills --skill explore-run -a codex`. Or copy the skill folder (skills/explore-run in lllllllama/RigorPilot-Skills) into .agents/skills/explore-run in your project. Codex loads it when a task matches its description.

Can I use Explore Run 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 explore-run -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/explore-run, .gemini/skills/explore-run, .github/skills/explore-run and .opencode/skills/explore-run in your project.

What does Explore Run need to run?

Going by SKILL.md and its folder, Explore Run needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Explore Run 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 Explore Run 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 Explore Run use?

Explore Run 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 Explore Run use?

About 833 tokens (SKILL.md is roughly 3.3k 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 169 tokens, read only when the agent opens those files.

What are the alternatives to Explore Run?

Skills that share tags, products or a category with Explore Run: ML Engineer (davila7/claude-code-templates, 32k stars), Tokenwise (majiayu000/claude-skill-registry, 666 stars), Sparse Autoencoder Training with SAELens (Orchestra-Research/AI-Research-SKILLs, 13k stars) and TransformerLens Interpretability (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 Explore Run?

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.