Docstring
pytorch/pytorch
Write docstrings for PyTorch functions and methods following PyTorch conventions.
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates.
Install AI Research Explore
$ npx skills add lllllllama/RigorPilot-Skills --skill ai-research-explore -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lllllllama/RigorPilot-Skills ai-research-explore --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/ai-research-explore .claude/skills/ai-research-explore && 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 "ai-research-explore" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/ai-research-explore into .claude/skills/ai-research-explore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-research-explore", 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/ai-research-exploreType 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 ai-research-explore -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lllllllama/RigorPilot-Skills ai-research-explore --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/ai-research-explore .agents/skills/ai-research-explore && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-research-explore" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/ai-research-explore into .agents/skills/ai-research-explore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-research-explore", 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 ai-research-explore -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lllllllama/RigorPilot-Skills ai-research-explore --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/ai-research-explore .cursor/skills/ai-research-explore && 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 "ai-research-explore" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/ai-research-explore into .cursor/skills/ai-research-explore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-research-explore", 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/ai-research-explore--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 ai-research-explore -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lllllllama/RigorPilot-Skills ai-research-explore --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/ai-research-explore .gemini/skills/ai-research-explore && 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 "ai-research-explore" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/ai-research-explore into .gemini/skills/ai-research-explore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-research-explore", 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 ai-research-exploreInstalls 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 ai-research-explore -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/ai-research-explore .github/skills/ai-research-explore && 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 "ai-research-explore" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/ai-research-explore into .github/skills/ai-research-explore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-research-explore", 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 ai-research-explore -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 ai-research-explore --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/ai-research-explore .opencode/skills/ai-research-explore && 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 "ai-research-explore" agent skill from https://github.com/lllllllama/RigorPilot-Skills/tree/main/skills/ai-research-explore into .opencode/skills/ai-research-explore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-research-explore", 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
ai-research-explore- GitHub stars
- 497
- Used in
- 1 other repo
- Token cost
- ~1.7k tokens
- SKILL.md length
- 666 words
- Files
- 35 (incl. scripts, references)
- Skills in repo
- 11
- Repo updated
- First seen
- Licence
- MIT
At a glance
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates.
- Works in 9 steps: Confirm current_research and explicit… → Accept either legacy variant_spec or… → In campaign mode, freeze the task,… → …
- The researcher has chosen the task family
- SKILL.md covers Purpose, Fit, Research Rhythm and Workflow, plus 3 more sections
- Runs Python scripts from its folder
What it does
AI Research Explore is an agent skill from lllllllama/RigorPilot-Skills. Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of currentresearch with auditable repo understanding, idea gating, fair comparison, and governed experiments written to exploreoutputs/. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only…
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 39 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/ai-research-explore-policy.md` and `references/idea-evaluation-framework.md`).
It sits in AI & LLM Engineering, covering Deep learning, Creative writing and fiction 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 has chosen the task family
- Evaluation method
- Provided SOTA references
- Wants candidate-only exploration on top of currentresearch with auditable repo understanding
Example prompts
- “/ai-research-explore”
Requirements
- Python 3
Workflow steps
9 steps, taken from the first numbered list in SKILL.md.
- Confirm current_research and explicit explore-lane authorization.
- Accept either legacy variant_spec or higher-level research_campaign.
- In campaign mode, freeze the task, dataset, benchmark, evaluation source,
- Build only the repo-understanding artifacts needed for the current campaign,
- Run bounded, cache-first source lookup when source support matters; prefer
- Preserve researcher-provided ideas, optionally add a small bounded set of
- Prefer one clear candidate at a time. Use explore-code for bounded code
- Use minimal-run-and-audit or run-train only when the exploratory plan
- Write candidate-only outputs to analysis_outputs/, sources/, and
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 8 files in scripts/ (Python, from the files we listed), 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
AI Research Explore loads about 1.7k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 158 tokens; SKILL.md has 666 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). 666 words, ~1,676 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-research-explore/SKILL.md (or your agent's skills folder). This skill also uses 34 other files; get the full folder from GitHub.- name
- ai-research-explore
- description
- Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.
ai-research-explore
Purpose
Use this as the Rigor Explore compatible skill slug after the researcher
explicitly authorizes candidate-only work on top of a durable
current_research anchor. The installed slug remains ai-research-explore for
compatibility. Rigor Explore is for meaningful and potentially novel deep
learning research candidates while preserving scientific rigor, comparability,
reproducibility, and auditable collaboration. Novelty and significance remain
hypotheses before literature contrast, ablation evidence, and fair comparison.
The skill does not promise autonomous discovery, global benchmark completeness,
novelty proof, or trusted reproduction success.
Start from the shared operating principles in
../ai-research-reproduction/references/agent-operating-principles.md, then load
../ai-research-reproduction/references/research-rigor-principles.md for research claims and
../ai-research-reproduction/references/deep-learning-experiment-principles.md when experiment
details affect comparability or reproducibility.
Fit
Use this skill only when the request has both:
- Explicit exploration authorization such as candidate-only work, isolated branch or worktree, sweep, several variants, or exploratory ranking.
- A durable
current_researchcontext such as a branch, commit, checkpoint, run record, or already-trained local model state.
Keep narrow code-only requests on explore-code. Keep narrow run-only requests
on explore-run. Keep passive repository analysis on analyze-project. Keep
README-first reproduction on ai-research-reproduction.
Research Rhythm
Use a two-loop rhythm:
- Outer loop: understand the repository, freeze task/dataset/evaluation/budget, preserve user ideas, map sources, gate ideas, and decide whether the next experiment is worth running.
- Inner loop: make one bounded candidate change or run, smoke-check it, collect evidence, rank it against the current anchor, and either stop or return to the outer loop with the new evidence.
This rhythm is a guide, not a rigid autonomous loop. Stop at explicit blockers, unclear scientific meaning, exhausted budget, missing anchor/evaluation, or a human checkpoint.
Workflow
- Confirm
current_researchand explicit explore-lane authorization. - Accept either legacy
variant_specor higher-levelresearch_campaign. - In campaign mode, freeze the task, dataset, benchmark, evaluation source, SOTA reference, and budget before candidate work.
- Build only the repo-understanding artifacts needed for the current campaign,
usually through
analyze-project. - Run bounded, cache-first source lookup when source support matters; prefer local curated literature such as Zotero if available, then seed sources, repo-local locators, public locators, or optional web lookup. Treat lookup as source resolution, not an open-ended literature search.
- Preserve researcher-provided ideas, optionally add a small bounded set of single-variable seed ideas, and rank ideas with explicit gates and score breakdowns.
- Prefer one clear candidate at a time. Use
explore-codefor bounded code adaptation andexplore-runfor short-cycle trials or sweeps. - Use
minimal-run-and-auditorrun-trainonly when the exploratory plan requires real execution evidence. - Write candidate-only outputs to
analysis_outputs/,sources/, andexplore_outputs/as appropriate; never present exploratory gains as trusted reproduction success. IncludeSCIENTIFIC_CHANGELOG.mdandCOMPARABILITY_REPORT.mdfor candidate scientific meaning and comparison boundaries.
Show full SKILL.md (232 more words)Show less
Ranking and Evidence
- Before execution, prioritize candidates by expected gain, cost, success likelihood, patch surface, dependency drag, evaluation risk, and rollback ease.
- After execution, rank by real evidence first: command status, observed metrics, artifacts, changed paths, smoke results, and reproducibility notes.
- Keep researcher-provided
evaluation_sourceandsota_referencefrozen for the campaign; do not claim they are globally complete. - If the top ideas are too close or the implementation cannot be decomposed into auditable units, stop for a checkpoint instead of silently choosing.
Campaign Inputs
research_campaign is preferred for Rigor Explore campaigns, but it should
stay minimal. The durable core is:
current_researchtask_familydatasetbenchmarkevaluation_sourcesota_referencecompute_budget
Use candidate_ideas, variant_spec, research_lookup, idea_policy,
idea_generation, source_constraints, feasibility_policy, baseline_gate,
and execution_policy as optional guidance, not as fields the agent must fill
for every campaign. See references/research-campaign-spec.md for the advanced
schema and artifact expectations.
Reference Loading
- Load
references/ai-research-explore-policy.mdfor lane safety and candidate semantics. - Load
references/research-campaign-spec.mdonly when a campaign file is present or the user asks for Rigor Explore campaign governance. - Load
../ai-research-reproduction/references/explore-variant-spec.mdfor run-level variant matrix details. - Load
../ai-research-reproduction/references/research-thinking-loop.mdbefore proposing or ranking candidate changes; it is the required greedy observe-ground-design-compare cycle. - Load
../ai-research-reproduction/references/research-rigor-principles.mdbefore making novelty, contribution, SOTA, or comparability statements. - Consult
~/.rigorpilot/PERSONAL_RIGOR.mdif present, under../ai-research-reproduction/references/continuous-learning-policy.md(advisory only; core wins). - Load
../ai-research-reproduction/references/deep-learning-experiment-principles.mdwhen training, evaluation, baseline, ablation, metric, checkpoint, or dataset details matter. - Use
scripts/orchestrate_explore.pyandscripts/write_outputs.pyfor the existing deterministic artifact workflow.
© 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 34 other files (scripts, references) in skills/ai-research-explore of lllllllama/RigorPilot-Skills.
- SKILL.md
- agents/openai.yaml
- references/ai-research-explore-policy.md
- references/idea-evaluation-framework.md
- references/research-campaign-spec.md
- references/smoke-validation-policy.md
- references/source-mapping-policy.md
- references/sources-naming-policy.md
- scripts/lookup/__init__.py
- scripts/lookup/cache_store.py
- scripts/lookup/inventory_writer.py
- scripts/lookup/normalizers.py
- scripts/lookup/providers/__init__.py
- scripts/lookup/providers/arxiv_provider.py
- scripts/lookup/providers/base.py
- scripts/lookup/providers/doi_provider.py
- … and 19 more
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
AI Research Explore 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 |
|---|---|---|---|---|---|---|
| AI Research Explore this skilllllllllama/RigorPilot-Skills | 497 | 1 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Docstringpytorch/pytorch | 104k | 2 repos | ~2.6k | Automated safety check: Pass | Custom licence | |
| ML Engineerdavila7/claude-code-templates | 33k | 9 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Perforatedai WandbPerforatedAI/PerforatedAI | 237 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Document Public APIspytorch/pytorch | 104k | — | ~4.2k | Automated safety check: Pass | Custom licence | |
| Ascendcascend-ai-coding/awesome-ascend-skills | 174 | — | ~3.5k | Automated safety check: Pass | None |
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Related
Questions about AI Research Explore
What does AI Research Explore do?
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. AI Research Explore is an agent skill from lllllllama/RigorPilot-Skills. Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates.
When should I use AI Research Explore?
AI Research Explore fits situations like: the researcher has chosen the task family; evaluation method; provided SOTA references; wants candidate-only exploration on top of currentresearch with auditable repo understanding.
How do I install AI Research Explore in Claude Code?
Run `npx skills add lllllllama/RigorPilot-Skills --skill ai-research-explore -a claude-code`. Or copy the skill folder (skills/ai-research-explore in lllllllama/RigorPilot-Skills) into .claude/skills/ai-research-explore in your project. Claude Code loads it when a task matches its description.
How do I install AI Research Explore in Codex?
Run `npx skills add lllllllama/RigorPilot-Skills --skill ai-research-explore -a codex`. Or copy the skill folder (skills/ai-research-explore in lllllllama/RigorPilot-Skills) into .agents/skills/ai-research-explore in your project. Codex loads it when a task matches its description.
Can I use AI Research Explore 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 ai-research-explore -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-research-explore, .gemini/skills/ai-research-explore, .github/skills/ai-research-explore and .opencode/skills/ai-research-explore in your project.
What does AI Research Explore need to run?
Going by SKILL.md and its folder, AI Research Explore needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Research Explore 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 AI Research Explore 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 AI Research Explore use?
AI Research Explore 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 AI Research Explore use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 3.7k tokens, read only when the agent opens those files.
What are the alternatives to AI Research Explore?
Skills that share tags, products or a category with AI Research Explore: Docstring (pytorch/pytorch, 104k stars), ML Engineer (davila7/claude-code-templates, 33k stars), Perforatedai Wandb (PerforatedAI/PerforatedAI, 237 stars) and Document Public APIs (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Research Explore?
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.