Llama
Meta Llama open-source LLM family. Use for local AI.
Install
$ agentstack add skill-g1joshi-agent-skills-llama ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-g1joshi-agent-skills-llama) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Llama
Meta Llama is the king of Open Weights models. Llama 4 (2025) pushes 405B+ parameters, rivaling closed models like GPT-5.
When to Use
- Privacy: Run it on your own VPC (AWS Bedrock, Azure, or self-hosted).
- Fine-Tuning: It is the default base model for fine-tuning on domain data.
- Cost: Inference on Groq/Together AI is significantly cheaper than GPT.
Core Concepts
Models
- 405B: Frontier intelligence. Requires massive GPU clusters (or API).
- 70B: The workhorse. Smart enough for most tasks.
- 8B: Runs on a laptop (MacBook M3).
Quantization
Running models at 4-bit or 8-bit precision to fit in VRAM with minimal quality loss (GGUF, EXL2).
Llama Stack
Standardized tooling for building agentic apps on Llama.
Best Practices (2025)
Do:
- Use via API: Groq (LPU) runs Llama Instantaneously (>1000 tok/s).
- Fine-Tune 8B: For specific tasks (classification, SQL generation), a fine-tuned 8B beats a generic 70B.
Don't:
- Don't self-host 405B: Unless you have 8xH100s. Use an API provider.
References
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: G1Joshi
- Source: G1Joshi/Agent-Skills
- License: MIT
- Homepage: https://skills.sh/g1joshi/agent-skills
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.