Skill audit report
Runs an end-to-end AMD Quark post-training quantization workflow for PyTorch / Hugging Face LLMs: inspect a Hub or local model, choose a quantization plan, create reproducible artifacts, request execution approval, and produce a quantized model. Applies to Llama, Qwen, Mistral, and similar transformer LLM requests involving FP8, INT4, or another Quark scheme. Does not handle .onnx model inputs or ONNX PTQ.
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO62
395 GitHub stars
Stars/forks activity
WARN57
395 stars, 39 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
Pushed today
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
WARN54
command execution surface, external package install surface
Install availability
PASS92
npx skills add amd/skills --skill quark-torch-llm-ptq
Install command safety
PASS92
standard package or runtime install path
Permission surface
FAIL36
shell or command execution, filesystem or document access
Repository evidence
PASS86
https://github.com/amd/skills/tree/main/skills/quark-torch-llm-ptq
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add amd/skills --skill quark-torch-llm-ptq
Repository
88
https://github.com/amd/skills/tree/main/skills/quark-torch-llm-ptq
License
86
MIT
Maintenance
100
Pushed today
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
54
command execution surface, external package install surface
Install command safety
92
standard package or runtime install path
Permission surface
36
shell or command execution, filesystem or document access
Stars/forks activity
57
395 stars, 39 forks; issue activity unavailable in current metadata
Adoption
68
395 GitHub stars
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Warnings
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
Compare nearby options
Creates an Android app that runs an open text LLM on the device, on the CPU or the GPU, with the LiteRT-LM Kotlin API. Use this skill to build a new chat, summarization or extraction app with a .litertlm model from Hugging Face litert-community (Gemma, Qwen, Llama, Phi) - the dependency and manifest entries, getting the model file onto the device, engine initialization, a streamed multi-turn conversation, the ViewModel and screen.
458 Stars · Audit report
Lint AI agent instruction files (SKILL.md, CLAUDE.md, AGENTS.md, GEMINI.md), tool definitions, system prompts, and agent configs with the deterministic LintLang CLI. Use when writing, editing, or reviewing agent instructions to catch ambiguous tool descriptions, missing stop conditions, schema/description mismatches, mixed output formats, or prompts embedded in Python before they reach runtime. Zero-LLM static analysis; no model calls and no network calls during a scan.
137 Stars · Audit report
Use for \"interrogate\", \"adversarial review\", \"multi-model review\", \"challenge this\", \"stress test this code\", \"find blind spots\", or \"tear this apart\". Multiple LLM reviewers challenge changes from independent angles.
111 Stars · Audit report