Explore Deep Learning Research Candidates
Research exploration can produce weak claims, unfair comparisons, and uncontrolled experiments. This skill structures candidate generation, evidence checks, bounded execution, and auditable reporting.
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Review the Skillstore skill "ai-research-explore" from https://skillstore.io/skills/lllllllama-ai-research-explore.md and its manifest at https://skillstore.io/api/skills/lllllllama-ai-research-explore/manifest. Verify the artifact. Do not auto-install. Inspect the skill and report your findings, then wait for an operator or manual installation decision.Your Agent should still show its plan and request any confirmation required by the security policy.
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Using "ai-research-explore". Rank three optimizer changes for a fixed image-classification benchmark with a two-hour compute budget.
Expected outcome:
Using "ai-research-explore". Map one paper's attention mechanism into an existing sequence model without changing evaluation.
Expected outcome:
Using "ai-research-explore". Review completed exploratory runs and recommend the next trial.
Expected outcome:
Most static alerts are false positives caused by Markdown formatting, identifiers, nested Python structures, and fixed argument-list subprocess calls. Confirmed risks include outbound lookup traffic, unrestricted user-derived URLs, and instructions to read files outside the skill package. Automatic imports execute target repository modules during feasibility checks, creating an unsandboxed arbitrary code execution risk.
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
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https://skillstore.io/skills/lllllllama-ai-research-explore/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/lllllllama-ai-research-explore?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/lllllllama-ai-research-explore?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/lllllllama-ai-research-explore/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/lllllllama-ai-research-explore.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>lllllllama. (2026). ai-research-explore security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lllllllama-ai-research-explore/audits/7@techreport{lllllllama-lllllllama-ai-research-explore-2026,
author = {lllllllama},
title = {ai-research-explore security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/lllllllama-ai-research-explore/audits/7},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "ai-research-explore security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "lllllllama"
date-released: "2026-08-08"
url: "https://skillstore.io/skills/lllllllama-ai-research-explore/audits/7"
identifiers:
- type: other
value: "skillstore:lllllllama-ai-research-explore:audit:7"
description: "Skillstore immutable audit report identifier"
Compare a small set of model changes against fixed evaluation rules before spending significant compute.
Create bounded variant plans, resource checks, branch isolation, and audit artifacts for a research campaign.
Trace candidate claims to sources, implementation surfaces, measured outcomes, and explicit comparison limits.
Evaluate this candidate idea against my current research anchor. Check single-variable fit, expected gain, cost, evidence needs, and rollback ease.
Rank these candidate ideas for the specified task, dataset, benchmark, metric, SOTA reference, and compute budget. Explain every gate and score.
Map the provided papers and repositories to target files, symbols, interfaces, and a minimal reversible patch plan. Preserve the frozen evaluation contract.
Plan a candidate-only campaign from this durable anchor. Include source lookup, idea gates, atomic changes, smoke checks, limited variants, comparability reporting, and checkpoints.
Author
lllllllamaLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
656be3040aef5c047555a908cd5c695d22a4a548
Maintenance freshness
8/8/2026
Usage
9 downloads · 1 views
File structure
Analyze Deep Learning Repositories
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