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 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/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/diagrid-labs/dapr-skills/create-agent-python"><img src="https://agentmods.dev/badge/skills/diagrid-labs/dapr-skills/create-agent-python.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00069 | $0.04510 |
| Opus 5.5 | $0.00028 | $0.01804 |
| Sonnet 5.5 | $0.00014 | $0.00902 |
| Haiku 4.5 | $0.00007 | $0.00451 |
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 8d 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','ident Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
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','ident How it starts
The opening of the file, as written. The whole thing — 241 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 keeps the conversation of each run 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.diagridis BUSL-1.1 licensed (with an Additional Use Grant, converting to Apache-2.0 on its Change Date) and its runners send one anonymous usage event per process; the generated README says both (seeREFERENCE.md).dapr-agentsis Apache-2.0.
Single agent or coordinator + specialists, either way. With wrappers, the coordinator is a native dapr-agents agent that dispatches to wrapper specialists through the agent registry (a mixed project); wrappers that only exchange events over pub/sub are the event-driven variant.
Execution Order
You MUST follow these phases in strict order:
- Check specification — Check if the user specified what needs to be built.
- Check optional tools — Check whether the Diagrid Dapr Dev Dashboard is installed and remember the result. Never stop, ask or install for this.
- 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: the closing sentence followed by the Dev Dashboard recommendation. Do NOT add any other text, summary, or commentary.
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.
- 8d ago Changed · -7 lines · -41 tokens per session 29c4ad238b84
- 10d ago Changed · +3 lines 831124e9e3f8
- 11d ago Changed · +32 tokens per session 59a6ce36a6c2
- 12d ago Changed · +1 lines 6f6a988ca2f5
- 13d ago Changed · +14 lines 51325ac1e33b
- 1mo 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 (13 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 4,510 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
pandas
Use when analyzing or transforming tabular data in Python. Covers vectorized operations, memory-efficient dtypes, correct joins, groupby patterns, and avoiding the silent errors pandas makes easy. Trigger on "pandas is slow", "dataframe runs out of memory", "merge duplicated my rows", "SettingWithCopyWarning". Do not…
django
Use when building Django applications. Covers ORM query performance, model design, migrations, Django REST Framework, security defaults, and testing. Trigger on "django view is slow", "too many queries from the orm", "add a drf endpoint", "migration on a big table". Do not use for API contract decisions independent of…
fastapi
Use when building APIs with FastAPI. Covers dependency injection, Pydantic v2 validation, async database access, authentication, background tasks, and testing. Trigger on "fastapi endpoint is slow under load", "set up depends for the db session", "pydantic v2 validation error", "test my fastapi app". Do not use for…
python
Use when writing, reviewing, or modernizing Python 3.11+ code. Produces fully type-annotated modules, async I/O, dataclasses and protocols, pytest suites, and a lint/type gate built on ruff and mypy --strict. Trigger on "add type hints to this", "mypy is complaining", "convert this to asyncio", "set up pytest for this…
neo-python
Use this skill when writing, reviewing, debugging, or architecting Python 3.10+ code, including type hints, structural pattern matching, dataclasses, async/task groups, packaging-aware project structure, testability, and maintainability.
freecad-scripts
Expert skill for writing FreeCAD Python scripts, macros, and automation. Use when asked to create FreeCAD models, parametric objects, Part/Mesh/Sketcher scripts, workbench tools, GUI dialogs with PySide, Coin3D scenegraph manipulation, or any FreeCAD Python API task. Covers FreeCAD scripting basics, geometry creation…