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 itallstartedwithaidea/agent-skills --skill batch-processinggit clone --depth 1 https://github.com/itallstartedwithaidea/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/itallstartedwithaidea/agent-skills/batch-processing)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/batch-processing"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/batch-processing/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/itallstartedwithaidea/agent-skills/batch-processing"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/batch-processing.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 | $0.00024 | $0.01606 |
| Opus 5.5 | $0.00010 | $0.00642 |
| Sonnet 5.5 | $0.00005 | $0.00321 |
| Haiku 4.5 | $0.00002 | $0.00161 |
Grade A, and why
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 1mo 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Batch Processing
Part of Agent Skills™ by googleadsagent.ai™
Description
Batch Processing enables parallel AI task execution with progress tracking, error handling, rate limiting, and result aggregation. The agent processes large collections of items—documents, images, code files, API requests—through AI pipelines concurrently, managing throughput, failures, and partial results without losing work.
Single-item AI processing is straightforward; batch processing at scale introduces failures, rate limits, memory constraints, and the need for resumability. This skill handles these production realities: configurable concurrency limits, exponential backoff on rate limit errors, checkpoint-based resumability after crashes, and structured progress reporting that shows exactly which items succeeded, failed, or are pending.
The skill supports both homogeneous batches (same operation on every item) and heterogeneous batches (different operations routed by item type). Results are aggregated into structured reports with per-item status, timing, and error details. Failed items are automatically retried with backoff, and permanently failed items are collected into a dead-letter queue for manual inspection.
Use When
- Processing hundreds or thousands of items through an AI pipeline
- Translating, summarizing, or classifying large document collections
- Generating embeddings for a corpus of documents
- Running code analysis across an entire repository
- Batch-generating images, descriptions, or metadata
- Any task that processes items sequentially but could benefit from parallelism
How It Works
graph TD
A[Input Batch: N Items] --> B[Load Checkpoint: Resume if Exists]
B --> C[Partition into Work Chunks]
C --> D[Parallel Workers: Concurrency Limit]
D --> E[Worker 1: Process Item]
D --> F[Worker 2: Process Item]
D --> G[Worker K: Process Item]
E --> H{Success?}
F --> H
G --> H
H -->|Yes| I[Record Result]
H -->|Rate Limited| J[Backoff + Retry]
H -->|Failed| K[Retry Queue]
J --> D
K --> L{Retries Exhausted?}
L -->|No| D
L -->|Yes| M[Dead Letter Queue]
I --> N[Save Checkpoint]
N --> O[Progress Report]
O --> P{All Done?}
P -->|No| D
P -->|Yes| Q[Final Aggregation Report]
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.
- 1mo ago First seen · 183 lines · 24 tokens per session scan A ac15cc695ff9
batch-processing is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 1,606 once invoked, about $0.0001 per session on Opus 5.5. 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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