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/j4flmao/agent-skills/batch-processingnpx skills add j4flmao/agent-skills --skill batch-processinggit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/batch-processing)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/batch-processing"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/batch-processing.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 5.1 | $0.00098 | $0.04597 |
| Opus 5 | $0.00049 | $0.02299 |
| Sonnet 5 | $0.00020 | $0.00919 |
| Haiku 4.5 | $0.00010 | $0.00460 |
Grade A, and why
data-batch-processing 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 2d 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 — 525 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Batch Processing
Purpose
Design efficient batch processing architectures using Hive, Spark SQL, and optimized file formats. Master partitioning, bucketing, Catalyst optimizer tuning, vectorized reads, file format selection, and dynamic partition pruning for large-scale analytical workloads.
Agent Protocol
Trigger
Exact user phrases: "batch processing", "Hive", "Spark SQL", "Pig", "HQL", "Hive partition", "Spark partition", "bucketing", "query optimization", "ORC", "Parquet", "Avro", "vectorized read", "dynamic partition pruning", "Catalyst optimizer", "Tungsten", "Hive metastore", "reduce tasks".
Input Context
Before activating, verify:
- Query engine (Hive on Tez, Hive on MR, Spark SQL, Presto, Trino)
- File format currently used (text, Parquet, ORC, Avro, JSON)
- Table volume (row count, size in TB, partition count)
- Partition column(s) and cardinality
- Common query patterns (full scan, filtered, aggregated, joined)
- Cluster resources (cores, memory, number of nodes)
Output Artifact
Batch processing configuration with engine selection, partition strategy, and optimization parameters.
Response Format
Engine: {Hive on Tez | Spark SQL | Presto | Trino}
File Format: {Parquet | ORC | Avro}
Partition: {column: type, granularity: daily/hourly/monthly}
Bucketing: {column: cluster count}
Optimizations: {vectorized, CBO, DPP, broadcast join}
Tuning: {executor/container config, parallelism}
-- DDL with partition/bucket spec
-- Query with optimization hints
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Query engine selected with justification
- File format selected and configured
- Partition strategy defined (column, granularity, layout)
- Bucketing strategy defined if applicable
- Catalyst/BE optimizer settings configured
- Vectorized read enabled
Max Response Length
250 lines of config.
What ships with it
7 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.
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.
- 2d ago First seen · 525 lines · 98 tokens per session scan A 92a451776717
data-batch-processing is a skill published in the GitHub repository j4flmao/agent-skills (21 stars, last pushed yesterday), licensed MIT. It adds 98 tokens to every session and 4,597 once invoked, about $0.0005 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 2026-09-03.
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