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/flyteorg/flyte-agent-plugins/flyte-migrate-data-ionpx skills add flyteorg/flyte-agent-plugins --skill flyte-migrate-data-iogit clone --depth 1 https://github.com/flyteorg/flyte-agent-pluginsWrote 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/flyteorg/flyte-agent-plugins/flyte-migrate-data-io)<a href="https://agentmods.dev/skills/flyteorg/flyte-agent-plugins/flyte-migrate-data-io"><img src="https://agentmods.dev/badge/skills/flyteorg/flyte-agent-plugins/flyte-migrate-data-io.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 | $0.00118 | $0.02891 |
| Opus 5 | $0.00059 | $0.01445 |
| Sonnet 5 | $0.00024 | $0.00578 |
| Haiku 4.5 | $0.00012 | $0.00289 |
Grade A, and why
flyte-migrate-data-io 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flyte 1 to 2 Migration: Data Types and I/O
Flyte 2 renames the offloaded-data types and makes their I/O async, but the mental model is the same: pass lightweight references to large data between tasks, not the materialized bytes. FlyteFile, FlyteDirectory, and StructuredDataset become flyte.io.File, flyte.io.Dir, and flyte.io.DataFrame. Plain dataclasses and Pydantic BaseModels work directly as task I/O with no JSON mixin.
Grounding References
| Resource | URL |
|---|---|
| Migration guide | https://www.union.ai/docs/v2/flyte/user-guide/migration/flyte-2/data-io/ |
| Official docs | https://www.union.ai/docs/v2/flyte |
| Docs index (LLMs) | https://www.union.ai/docs/v2/flyte/llms.txt |
| SDK API reference | https://www.union.ai/docs/v2/union/api-reference/flyte-sdk/ |
| Example code | https://github.com/unionai/unionai-examples |
| Flyte MCP tools | Available via flyte-mcp server |
Type Mapping
| Flyte 1 | Flyte 2 | Notes |
|---|---|---|
flytekit.types.file.FlyteFile |
flyte.io.File |
I/O is async |
flytekit.types.directory.FlyteDirectory |
flyte.io.Dir |
I/O is async |
flytekit.types.structured.StructuredDataset |
flyte.io.DataFrame |
build with from_df, read with open(...).all() |
@dataclass_json + @dataclass |
plain @dataclass |
no mixin needed |
Pydantic BaseModel (+ config) |
plain Pydantic BaseModel |
works directly as task I/O |
Offloaded Data: The Mental Model
File, Dir, and DataFrame are lightweight references (pointers) to data offloaded in blob storage — not the materialized bytes. In Flyte 2 the read/write operations are async: upload with await File.from_local(local_path), read with async with f.open("rb") as fh: await fh.read(), build a frame with flyte.io.DataFrame.from_df(df) (sync constructor), and read it with await fdf.open(pandas.DataFrame).all().
Files and Directories
FlyteFile and FlyteDirectory become flyte.io.File and flyte.io.Dir — the way you pass model artifacts and datasets between tasks. Use await File.from_local(...) to upload and async with file.open(...) to read.
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.
- 4d ago First seen · 310 lines · 118 tokens per session scan A 1eb54f23dfb8
flyte-migrate-data-io is a skill published in the GitHub repository flyteorg/flyte-agent-plugins (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 118 tokens to every session and 2,891 once invoked, about $0.0006 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 2026-08-31.
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