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/diagrid-labs/dapr-skills/create-agent-pythonnpx skills add diagrid-labs/dapr-skills --skill create-agent-pythongit clone --depth 1 https://github.com/diagrid-labs/dapr-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/diagrid-labs/dapr-skills/create-agent-python)<a href="https://agentmods.dev/skills/diagrid-labs/dapr-skills/create-agent-python"><img src="https://agentmods.dev/badge/skills/diagrid-labs/dapr-skills/create-agent-python.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 | $0.00078 | $0.03024 |
| Opus 5 | $0.00039 | $0.01512 |
| Sonnet 5 | $0.00016 | $0.00605 |
| Haiku 4.5 | $0.00008 | $0.00302 |
Grade C, and why
create-agent-python scanned grade C with 2 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s https://pypi.org/pypi/diagrid/json | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['info']['version']); print('\n'.join(e for e in d['info']['provides_extra'] if e not in ('agent-core','all')))" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash(curl:*) How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create a Python Agent Application
Overview
This skill describes how to create a durable AI agent application in Python. Two paths are supported:
- Native — the
dapr-agentsSDK. This is the Dapr Agents framework proper; it is Python-only. ItsDurableAgentruns on Dapr Workflow, reaches the LLM through a Dapr conversation component, and persists conversation memory in a Dapr state store. - Framework wrappers — the
diagriddistribution, which runs an agent you have already written in another framework (LangGraph, CrewAI, Strands, …) on Dapr Workflow so that each node / LLM call / tool call becomes a durable activity. One distribution, one extra per framework.
Single agent or coordinator + specialists, either way.
Execution Order
You MUST follow these phases in strict order:
- Check specification — Check if the user specified what needs to be built.
- Project Setup — Create all files and folders.
- Verify — Verify that the project builds.
- Create README.md — Create a readme that summarizes what is built and how to run & test the application. Do not provide instructions at the end of this phase.
- Show final message — Your LAST output MUST be EXACTLY the message defined in the
## Show final messagesection. Do NOT add any other text, summary, or commentary after it.
Check specification
If you don't have enough context what to build, ask the user the following clarifying questions one by one using an interview style:
- What is the purpose of the agent (or agent team)? This becomes the agent's
roleandinstructions. - Topology: a single agent, or a coordinator + N specialists?
- Framework:
dapr-agents(native, the default) or one of thediagridwrapper extras. Resolve the wrapper list at this point rather than reciting one — see "Resolving the framework list" below. - Pattern (only if Q3 selected native
dapr-agentsAND Q2 selected single-agent):augmented-llm(default),prompt-chaining,routing,parallelization,orchestrator-workers, orevaluator-optimizer. Skip this question entirely if the user picked a wrapper extra — those frameworks define their own agent loop and the pattern concept does not apply. - Tool definitions: name, purpose, and argument schema for each tool the agent should expose.
- LLM provider: OpenAI, Anthropic, Google Gemini, or local Ollama. Native
dapr-agentsroutes this through a Dapr conversation component; wrappers usually let the wrapped framework call the provider directly with an API key from the environment. - Include observability by default? (recommended: yes for native
dapr-agents; defaultnofor wrappers — they ship their own observability.) - Project name — used as the folder name. Don't use spaces.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 230 lines · 78 tokens per session scan C cd79e76f43c4
create-agent-python is a skill published in the GitHub repository diagrid-labs/dapr-skills (12 stars, last pushed 12d ago), licensed MIT. It adds 78 tokens to every session and 3,024 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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