jev-pipeline

• Outcome: Pass
• Latest audit: Oct 7, 2026 · 6:30 PM UTC
• Version: 0.1.0

Overview

This skill is a short Jev workflow guide with disclosed optional CLI install commands, but users should review the remote GitHub install before running it.

Install only if you are comfortable running unpinned code from the referenced GitHub repositories. Prefer pinning a commit, reviewing the package and tool-sync behavior, and running it in a project-local or isolated environment before using it on sensitive repositories.

A.I.G

Vulnerability Patterns

• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally
• Unauthorized Access and Privilege Escalation: Obtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns

• YARA Signatures: Malware Match, Webshell Match, Cryptominer Match
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code

Findings (1)

YARA rule 'agent_skill_remote_bootstrap_execution': Remote script or code download followed by execution/bootstrap installation [agent_skills]

High

• Category: YARA Match
• Confidence: 97% confidence

Finding

The skill instructs users to install code directly from a GitHub repository via pip install git+https://... and then run a module that performs additional repository cloning (tools-sync). This is a real supply-chain risk because it executes unpinned remote code outside a trusted package registry and can change over time; in an agent-skill context, users may copy these commands verbatim, increasing the chance of compromise.

Content

Scanner excerpt · SKILL.md (reported line 13)May include surrounding context.

ed Jev responses.

Jev pipeline

One workflow for Jev question design: find → fit → draft → lint → spread → threshold → cascade. Find decision-shaped LLM calls in a repo, judge whether Jev fits them, draft typed questions from field-tested patterns, lint them, then calibrate on real responses and plan which answers act, pass, or escalate.

If the CLI is installed

bash

pip install git+https://github.com/vicfei/jev-pipeline-skill   # stdlib only
python -m jev_pipeline tools-sync   # once: clones the upstream tool repos (MIT, by reference)

Command	Answers	Needs
run REPO	find+fit+draft+lint in one pass	nothing (offline)
find REPO	which LLM calls output decisions, not text?	nothing
fit	go / go-with-guards / no-go per candidate	editable *.answers.json
draft	first question template per candidate	nothing
lint	does the draft break known rules?	nothing
spread RESPONSES.jsonl	does the

Static analysis

No suspicious patterns detected.