analyze-project
81Analyze Deep Learning Repositories
Research repositories can hide important model, configuration, and evaluation relationships. This skill maps those relationships and reports suspicious patterns without changing project code.
Run and Audit Reproduction Commands
Short reproduction runs often produce inconsistent evidence and unclear results. This skill executes a selected command and organizes outcomes, metrics, logs, and repository changes.
This skill is part of a pack
Install the whole pack to get every skill the task needs, in one command.
Stop for confirmation before installing.
Review the plan and obtain explicit user consent before changing files.
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "minimal-run-and-audit" from https://skillstore.io/skills/lllllllama-minimal-run-and-audit.md and its manifest at https://skillstore.io/api/skills/lllllllama-minimal-run-and-audit/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.
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Using "minimal-run-and-audit". Run the documented CPU smoke test with a 60 second timeout.
Expected outcome:
Status: successful. The command completed within the limit, produced one accuracy metric, and created no repository changes.
Using "minimal-run-and-audit". Run the evaluation command and record why it stops.
Expected outcome:
Status: blocked. The executable was unavailable, so no evaluation metric was observed and no files changed.
Using "minimal-run-and-audit". Audit a command that exceeds the allowed runtime.
Expected outcome:
Status: partial. The command timed out after the configured limit, and the available output was retained for review.
The runner intentionally executes a user-supplied command, which is a confirmed high-risk capability despite avoiding shell interpretation. Two parent-directory references cross the audited package boundary, and the output wrapper dynamically executes a Python module; Markdown backticks and reporting text are false positives.
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
https://skillstore.io/skills/lllllllama-minimal-run-and-audit/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/lllllllama-minimal-run-and-audit?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/lllllllama-minimal-run-and-audit?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/lllllllama-minimal-run-and-audit/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/lllllllama-minimal-run-and-audit.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). minimal-run-and-audit security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lllllllama-minimal-run-and-audit/audits/7@techreport{lllllllama-lllllllama-minimal-run-and-audit-2026,
author = {lllllllama},
title = {minimal-run-and-audit security audit report (audit version 7)},
institution = {Skillstore},
year = {2026},
number = {7},
url = {https://skillstore.io/skills/lllllllama-minimal-run-and-audit/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: "minimal-run-and-audit security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
- name: "lllllllama"
date-released: "2026-08-08"
url: "https://skillstore.io/skills/lllllllama-minimal-run-and-audit/audits/7"
identifiers:
- type: other
value: "skillstore:lllllllama-minimal-run-and-audit:audit:7"
description: "Skillstore immutable audit report identifier"
Run a repository inference example and capture its outcome, logs, metrics, and file changes.
Produce concise evidence showing whether a selected smoke test succeeded, failed, or timed out.
Convert a short evaluation attempt into consistent artifacts for team review and comparison.
Run the selected smoke command in [repository path] with a [timeout] second limit. Report the outcome and captured evidence.
Execute [inference command] in [repository path]. Capture logs, numeric metrics, timeout state, and repository changes without modifying scientific behavior.
Run [evaluation command] and compare the observed metric with [expected baseline]. Mark unsupported comparisons and document any changed files.
Execute [command] after the approved patches. Separate execution facts from inferences and document patch effects, comparability limits, metrics, and blockers.
Author
lllllllamaLicense
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/minimal-run-and-audit/Ref
656be3040aef5c047555a908cd5c695d22a4a548
Maintenance freshness
8/8/2026
Usage
10 downloads ยท 105 views
File structure
๐ agents/
๐ openai.yaml
๐ references/
๐ reporting-policy.md
๐ scripts/
๐ run_command.py
๐ write_outputs.py
๐ SKILL.md
Analyze Deep Learning Repositories
Research repositories can hide important model, configuration, and evaluation relationships. This skill maps those relationships and reports suspicious patterns without changing project code.
Plan a Trustworthy Repository Reproduction
Repository reproduction often starts without a clear map of documented setup and experiment paths. This skill scans repository signals and recommends a minimal reproduction target.
Prepare Reproduction Environments and Assets
Research repositories often leave environment and asset requirements incomplete. This skill builds a conservative setup plan and records unresolved dependency and asset risks.
Plan Bounded Deep Learning Experiments
Exploratory training can consume budgets without producing comparable evidence. This skill ranks limited variants and records results for careful human review.
Diagnose Deep Learning Failures Safely
Deep learning failures can invite risky, premature changes. This skill classifies concrete errors and prepares conservative guidance before any code modification.
Resolve Paper Details for Reliable Reproduction
Repository documentation can omit details that are essential for reproducing deep learning results. This skill resolves narrow gaps from primary paper sources while documenting conflicts.
Build Therapeutic ML Workflows with PyTDC
by davila7
Therapeutic machine learning requires reliable datasets, meaningful splits, and consistent evaluation. This skill guides PyTDC workflows from dataset selection through molecular optimization.
Build Scalable PyTorch Lightning Training Workflows
by davila7
Deep learning projects become difficult to maintain when training, data, logging, and scaling logic are mixed. This skill provides structured PyTorch Lightning guidance and templates.
Compress Long Agent Contexts Reliably
by muratcankoylan
Long agent sessions can lose decisions, file history, and next steps during compression. This skill provides structured methods and probes that preserve operational context.
Design Data Science Experiments and Models
by alirezarezvani
Data science projects often fail when experiments, features, and models lack clear statistical structure. This skill gives Claude, Codex, and Claude Code guided workflows for experimentation, feature engineering, and model review.
Validate ML Workflows for Hidden Antipatterns
by Doyajin174
ML projects can look accurate while data leakage or weak evaluation hides serious flaws. This skill guides structured checks for splits, metrics, training, and deployment readiness.
Design Production Computer Vision Systems
by davila7
Computer vision projects often fail when model choices ignore data quality, latency, deployment, or monitoring constraints. This skill structures architecture, optimization, and production planning around those constraints.