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 agentmods add skills/opencue/cuecards/agenticx-quickstartnpx skills add opencue/cuecards --skill agenticx-quickstartgit clone --depth 1 https://github.com/opencue/cuecardsWrote 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/opencue/cuecards/agenticx-quickstart)<a href="https://agentmods.dev/skills/opencue/cuecards/agenticx-quickstart"><img src="https://agentmods.dev/badge/skills/opencue/cuecards/agenticx-quickstart.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00064 | $0.00825 |
| Opus 5 | $0.00032 | $0.00413 |
| Sonnet 5 | $0.00013 | $0.00165 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
agenticx-quickstart 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 3d 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.
This is a copy
100% identical to agenticx-quickstart — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgenticX Quickstart
Guide for getting a user from zero to a running AgenticX agent in under 5 minutes.
Installation
# Core install (lightweight, ~27 deps, installs in seconds)
pip install agenticx
# Verify
agx --version
Optional extras — install only what you need:
| Extra | What it adds | Command |
|---|---|---|
memory |
Mem0, ChromaDB, Qdrant | pip install "agenticx[memory]" |
document |
PDF/PPT/Word parsing | pip install "agenticx[document]" |
server |
API server (agx serve) |
pip install "agenticx[server]" |
volcengine |
Volcengine AgentKit | pip install "agenticx[volcengine]" |
all |
Everything | pip install "agenticx[all]" |
Environment
export OPENAI_API_KEY="sk-..."
# Optional
export ANTHROPIC_API_KEY="sk-ant-..."
Create a Project
agx project create my-first-agent --template basic
cd my-first-agent
agx project info
Create Your First Agent (Python)
from agenticx import Agent, Task, AgentExecutor
from agenticx.llms import OpenAIProvider
agent = Agent(
id="data-analyst",
name="Data Analyst",
role="Data Analysis Expert",
goal="Help users analyze and understand data",
organization_id="my-org"
)
task = Task(
id="analysis-task",
description="Analyze sales data trends",
expected_output="Detailed analysis report"
)
llm = OpenAIProvider(model="gpt-4")
executor = AgentExecutor(agent=agent, llm=llm)
result = executor.run(task)
print(result)
Create via CLI
# Scaffold an agent
agx agent create researcher --role "Senior Research Analyst"
agx agent list
# Scaffold a workflow
agx workflow create research-pipeline --agents "researcher"
# Run it
agx run workflows/research-pipeline.py --verbose
Add Tools
from agenticx.tools import tool
@tool
def calculate_sum(x: int, y: int) -> int:
"""Calculate the sum of two numbers."""
return x + y
# Pass tools when creating agent or executor
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.
- 3d ago First seen · 122 lines · 64 tokens per session scan A f9c90b0588e9
agenticx-quickstart is a skill published in the GitHub repository opencue/cuecards (5 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 825 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agenticx-quickstart, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
release
Cut a paad release — pick the semver bump from what is in [Unreleased], run make release, merge, tag, and verify the published plugin.
backlog
EXPERIMENTAL. Use when working the project-wide out-of-scope backlog at paad/code-reviews/backlog.md — cleaning it of entries that are already fixed or gone, or picking the next entry and fixing it end-to-end. Not for producing backlog entries — that is /agentic-review — and not for reviewing a branch diff.
agentic-architecture
Use when assessing the architectural health of a codebase — before a major refactor, when onboarding to an unfamiliar repo, after rapid growth, when planning a redesign, or to surface structural strengths and risks before they become expensive. Not for fixing what it finds, and not for reviewing a branch diff.
agentic-review
Use when reviewing current branch for bugs before pushing or merging, when wanting a thorough multi-agent review of local changes, or when preparing work for human review. Not for codebase structure, not for code style, and not for fixing what it finds.
ccg-annotate
AI-driven annotation workflow for adding structured metadata to code. Annotations are indexed and searchable via FTS.
ccg-docs
Generate Markdown documentation from code graphs, build RAG indexes for AI consumption, and lint documentation quality.