AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sickn33/agentic-awesome-skills --skill agents-v2-pygit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agents-v2-py)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agents-v2-py"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agents-v2-py/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agents-v2-py"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agents-v2-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.02376 |
| Opus 5 | $0.00019 | $0.01188 |
| Sonnet 5 | $0.00008 | $0.00475 |
| Haiku 4.5 | $0.00004 | $0.00238 |
Grade A, and why
agents-v2-py scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- agents-v2-py — 100% identical, 28 lines differ
- agents-v2-py — 97% identical, 30 lines differ
- agents-v2-py — 97% identical, 32 lines differ
- agents-v2-py — 97% identical, 30 lines differ
- agents-v2-py — 97% identical, 30 lines differ
- agents-v2-py — 97% identical, 28 lines differ
- agents-v2-py — 97% identical, 30 lines differ
- agents-v2-py — 97% identical, 30 lines differ
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compatibility and maintenance
Compatibility alias of hosted-agents-v2-py; use that ID for new references when no existing contract requires this one. The full instructions and support files remain local so existing installations
continue to work offline. This is one shared procedure, not an additional capability.
Preserve the callable ID when an existing manifest or client configuration uses it.
Modified in AAS on 2026-09-05; original metadata and license notices are retained.
Azure AI Hosted Agents (Python)
Build container-based hosted agents using ImageBasedHostedAgentDefinition from the Azure AI Projects SDK.
Installation
pip install 'azure-ai-projects>=2.0.0b3,<3' azure-identity
These are preview-era SDK v2 sketches. Check the exact installed version and current Azure hosted-agent documentation before provisioning; a broad version range is not an integration test.
Environment Variables
AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
Prerequisites
Before creating hosted agents:
- Container Image - Build and push to Azure Container Registry (ACR)
- ACR Pull Permissions - Grant your project's managed identity
AcrPullrole on the ACR - Capability Host - Account-level capability host with
enablePublicHostingEnvironment=true - SDK Version - Ensure
azure-ai-projects>=2.0.0b3
Authentication
Use the approved Azure credential flow for the intended tenant/subscription; this sketch uses DefaultAzureCredential:
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
import os
credential = DefaultAzureCredential()
client = AIProjectClient(
endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
credential=credential
)
Core Workflow
1. Imports
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
ImageBasedHostedAgentDefinition,
ProtocolVersionRecord,
AgentProtocol,
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago Changed · +18 lines c80e673c9e89
- 7d ago First seen · 332 lines · 39 tokens per session scan A 6ded73934243
agents-v2-py is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,288 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 2,376 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
ai-evaluation-dataset
Build a versioned JSONL evaluation dataset for an AI workflow, with acceptance criteria, held-out cases, and leakage checks.
llm-cost-latency-benchmark
Measure an AI workflow's observed token cost and end-to-end latency across representative cases with reproducible configuration.
llm-json-contract-check
Validate AI-generated JSON against an application's schema and business rules, distinguishing refusals and truncation from malformed output.
prompt-regression-gate
Compare prompt revisions on a frozen AI evaluation set with paired runs, slice-level regressions, and explicit release thresholds.
rag-retrieval-audit
Diagnose missing evidence in a retrieval-augmented generation pipeline using labeled queries, chunk inspection, and retrieval metrics.
ai-content-filter
Professional Ai Content Filter Expert skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.