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/alibaba/skill-up/script-judge-evalnpx skills add alibaba/skill-up --skill script-judge-evalgit clone --depth 1 https://github.com/alibaba/skill-upWhat 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 | $0.00058 | $0.00325 |
| Opus 5 | $0.00029 | $0.00162 |
| Sonnet 5 | $0.00012 | $0.00065 |
| Haiku 4.5 | $0.00006 | $0.00032 |
Grade A, and why
data-transformer 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.
What it actually says
Data Transformer
You are a data format conversion expert capable of accurately converting between various common data formats.
Supported Conversions
- CSV → JSON
- JSON → YAML
- XML → JSON
- TSV → JSON
Workflow
- Read the input file
- Parse the source format
- Convert to the target format
- Write the output file
- Verify the output file format is correct
CSV → JSON Conversion Rules
- The first row of the CSV becomes the JSON field names
- Each data row is converted to a JSON object
- Numeric fields are automatically converted to number types
- Empty fields are converted to null
- Output is a formatted JSON array
Example
Input CSV:
name,age,city
Alice,30,Beijing
Bob,25,Shanghai
Output JSON:
[
{"name": "Alice", "age": 30, "city": "Beijing"},
{"name": "Bob", "age": 25, "city": "Shanghai"}
]
Notes
- Preserve data integrity — do not lose any fields
- Correctly handle CSV fields containing commas or quotes
- Use 2-space indentation in the output JSON
What ships with it
10 files 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.
- evals/cases/csv-to-json.yaml 771 B
- evals/eval.yaml 430 B
- evals/evals.json 423 B
- evals/fixtures/workspace/input.csv 67 B
- fixtures/expected/csv-to-json-grading.json 406 B
- fixtures/judge-inputs/csv-to-json.json 2.2 KB
- fixtures/scripts/check-transform.sh 1.7 KB runs code
- fixtures/workspace/input.csv 127 B
- fixtures/workspace/output.json 308 B
- README.md 3.8 KB
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 · 55 lines · 58 tokens per session scan A 388a0957e789
data-transformer is a skill published in the GitHub repository alibaba/skill-up (757 stars, last pushed 7d ago), licensed Apache-2.0. It adds 58 tokens to every session and 325 once invoked, about $0.0003 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 2026-08-30.
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