lllllllama/rigorpilot-skills133k installs

explore-code

Q: How do I install this agent skill?

Run npx skills add https://github.com/lllllllama/rigorpilot-skills --skill explore-code.

Q: Is this agent skill safe to install?

Review the displayed partner audits, complete source, permissions, dependencies, and exact revision before installing.

Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.

How do I install this agent skill?

npx skills add https://github.com/lllllllama/rigorpilot-skills --skill explore-code

view source ↗

Is this agent skill safe to install?

Gen Agent Trust Hubwarn
The skill includes functionality that dynamically loads and executes code from a shared directory located outside of its own package structure.
Socketwarn
1 alert: gptAnomaly
Snykpass
Risk: LOW · No issues

What does this agent skill do?

explore-code

Use this as the Rigor Improve implementation leaf skill. The installed slug remains explore-code for compatibility.

Use the shared operating principles in ../../references/agent-operating-principles.md; this skill should guide bounded candidate code work without over-prescribing implementation details.

When to apply

When the researcher explicitly authorizes exploratory code changes on an isolated branch or worktree.
When the task is source-anchored module transplant, backbone adaptation, LoRA or adapter insertion, or low-risk module combination.
When summary-level recording is sufficient and the result is a candidate, not a trusted conclusion.

When not to apply

When the request is for trusted baseline work, conservative debugging, or normal training execution.
When the user did not explicitly authorize exploratory modifications.
When the task is a broad refactor or a from-scratch idea implementation.

Clear boundaries

This skill owns exploratory code modifications only.
It must keep work isolated from the trusted baseline.
Use ai-research-explore instead when the task spans both current_research coordination and exploratory runs.
It may hand off execution to minimal-run-and-audit or run-train.
It should favor source-anchored copying and minimal adaptation over freeform rewrites.
It should record why a candidate change is meaningful, how to roll it back, and why it remains a candidate rather than a verified contribution.

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/explore-policy.md, ../../references/research-rigor-principles.md, scripts/plan_code_changes.py, and scripts/write_outputs.py.

How can the creator link this skill?

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/lllllllama/rigorpilot-skills/explore-code">View explore-code on skillZs</a>