explore-code
Plan Auditable Exploratory Code Changes
Deep learning experiments can blur candidate changes with trusted baseline work. This skill guides isolated adaptations and creates rollback-aware implementation records.
Stop for confirmation before installing.
Review the plan and obtain explicit user consent before changing files.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "explore-code" from https://skillstore.io/skills/lllllllama-explore-code.md and its manifest at https://skillstore.io/api/skills/lllllllama-explore-code/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "explore-code". Plan a reversible LoRA insertion for the image encoder on an isolated branch.
Expected outcome:
- Candidate targets: encoder module, model registry, and experiment configuration.
- Minimal plan: add adapter wiring, expose rank settings, and preserve the baseline route.
- Validation: syntax parsing, import resolution, constructor checks, and one short candidate run.
- Rollback: remove adapter registration and restore the original configuration.
Using "explore-code". Assess a source-anchored segmentation head transplant.
Expected outcome:
The plan maps the head to likely model and configuration files, protects metric code, and records unresolved comparability evidence.
Using "explore-code". Summarize an exploratory backbone adaptation.
Expected outcome:
The summary identifies changed modules, research anchors, planned checks, rollback steps, and why results remain candidate evidence.
Security Audit
High RiskAll 25 static alerts are false positives caused by Markdown formatting, ordinary Python syntax, fixed documentation paths, and conservative policy wording. However, a separate high-severity issue exists because scripts/write_outputs.py dynamically executes an unbundled Python module from outside the skill directory.
Confirmed security concerns (1)
Risk Factors
⚙️ External commands (19)
📁 Filesystem access (2)
Share & cite this report
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https://skillstore.io/skills/lllllllama-explore-code/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/lllllllama-explore-code?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/lllllllama-explore-code/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/lllllllama-explore-code.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
lllllllama. (2026). explore-code security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lllllllama-explore-code/audits/7BibTeX citation
@techreport{lllllllama-lllllllama-explore-code-2026,
author = {lllllllama},
title = {explore-code security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/lllllllama-explore-code/audits/7},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "explore-code security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "lllllllama"
date-released: "2026-08-08"
url: "https://skillstore.io/skills/lllllllama-explore-code/audits/7"
identifiers:
- type: other
value: "skillstore:lllllllama-explore-code:audit:7"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: MediumWhat You Can Build
Adapt a Research Backbone
Plan a minimal backbone adaptation while preserving configuration wiring and rollback steps.
Review a Module Transplant
Map a source-anchored module into likely code and configuration targets before implementation.
Prepare an Adapter Experiment
Structure a reversible LoRA or adapter change with smoke checks and candidate-level documentation.
Try These Prompts
On my isolated experiment branch, identify likely files for this authorized adaptation: [change]. Keep the trusted baseline unchanged.
Create a minimal patch plan for [module or backbone change]. Include target files, configuration wiring, rollback steps, and planned smoke checks.
Use [variant specification], [idea card], and [analysis hints] to plan an isolated adaptation for [task family]. Preserve [research anchor].
Review this exploratory implementation against [research anchor]. Summarize changed surfaces, comparability risks, rollback actions, and evidence still needed before trusted claims.
Best Practices
- Use an isolated branch or worktree and record the trusted research anchor.
- Provide structured variant, idea, and analysis inputs when available.
- Keep adaptations small, reversible, and clearly labeled as exploratory.
Avoid
- Do not modify the trusted baseline by default.
- Do not use candidate results to claim verified reproduction or contribution.
- Do not use this skill for broad refactors or unsupported implementations from scratch.
Frequently Asked Questions
Does this skill modify the trusted baseline?
Can it run training experiments?
Which files can the planner inspect?
Does it verify scientific improvements?
What inputs improve the plan?
What records does it expect?
Developer Details
Author
lllllllamaLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
656be3040aef5c047555a908cd5c695d22a4a548
Maintenance freshness
8/8/2026
Usage
5 downloads · 0 views
File structure