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 pproenca/dot-skills --skill io-bound-data-processinggit clone --depth 1 https://github.com/pproenca/dot-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/pproenca/dot-skills/io-bound-data-processing)<a href="https://agentmods.dev/skills/pproenca/dot-skills/io-bound-data-processing"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/io-bound-data-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/pproenca/dot-skills/io-bound-data-processing"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/io-bound-data-processing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00242 | $0.03489 |
| Opus 5 | $0.00121 | $0.01744 |
| Sonnet 5 | $0.00048 | $0.00698 |
| Haiku 4.5 | $0.00024 | $0.00349 |
Grade A, and why
io-bound-data-processing scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
description: Processing, transforming, or moving datasets that may exceed RAM on a single low-compute box — covers memory discipline (streaming, generators, dtype shrinkage), I/O access patterns (sequential vs random, mm How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Community I/O-bound data processing on constrained resources Best Practices
A reference for engineers processing datasets larger than RAM on a single low-compute box. Organized by execution-lifecycle impact: rules near the top of the table govern whether the job runs at all; rules near the bottom shave the last 10 %. Optimize from the top of the waterfall.
Scope: the patterns that show up in real ETL / data-engineering / batch work on a laptop, a 2-vCPU container, or a Raspberry Pi-class node — streaming, formats, chunking, spill, backpressure, codecs, and the concurrency model that actually matches an I/O-bound bottleneck. Out of scope (covered elsewhere): the algorithmic primitives themselves (see computer-science-algorithms), distributed compute beyond a single box (use Spark/Dask), and database-engine internals (see official docs).
Distilled from Apache Arrow / Parquet docs, Polars User Guide, DuckDB docs, pandas — Scaling to large datasets, Linux man pages (mmap(2), sendfile(2), posix_fadvise(2)), Brendan Gregg's USE method and Systems Performance, Kleppmann's Designing Data-Intensive Applications, and the zstd / lz4 reference benchmarks.
When to Apply
Reach for these rules when:
- A job OOM-kills, swaps, or runs much slower than expected on a small box
- Input is larger than RAM and you need to scan, filter, aggregate, sort, or join it
- A pipeline has unbounded buffers between stages, or memory grows linearly during a "streaming" job
- You see one-row-per-RTT writes (
INSERTper row,requests.getper URL,f.read(32)per record) - You're picking a format/codec/serializer and the choice matters at scale
- A
topshows low CPU and high iowait, or you don't know which it is - "It's slow but I don't know why" — start at the obs- category
What ships with it
46 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.
- AGENTS.md 11 KB
- assets/templates/_template.md 1.7 KB
- metadata.json 1.5 KB
- references/_sections.md 2.5 KB
- references/batch-coalesce-writes-with-buffered-output.md 3.3 KB
- references/batch-keyset-pagination-over-offset.md 3.4 KB
- references/batch-pick-chunk-size-by-memory-budget.md 3.1 KB
- references/batch-process-with-stable-iterators.md 3.0 KB
- references/batch-use-vectorized-apis-not-row-loops.md 2.8 KB
- references/codec-dictionary-encoding-for-repetitive-strings.md 3.4 KB
- references/codec-prefer-binary-protocols-over-json-for-rpc.md 3.5 KB
- references/codec-train-a-zstd-dictionary-for-many-small-payloads.md 3.4 KB
- references/codec-zstd-or-lz4-as-defaults-not-gzip.md 3.0 KB
- references/conc-asyncio-for-many-network-streams-not-for-cpu.md 3.7 KB
- references/conc-overlap-compute-with-prefetch.md 3.5 KB
- references/conc-thread-pools-for-blocking-io-libraries.md 3.4 KB
- references/conc-tune-parallelism-to-the-bottleneck.md 3.6 KB
- references/fmt-avoid-deeply-nested-json-for-hot-paths.md 2.7 KB
- references/fmt-columnar-for-analytical-scans.md 2.3 KB
- references/fmt-line-delimited-for-streaming-row-ingest.md 2.4 KB
- references/fmt-prefer-schema-on-write-when-possible.md 2.8 KB
- references/fmt-push-predicates-into-the-reader.md 2.9 KB
- references/io-async-for-many-concurrent-streams.md 2.6 KB
- references/io-batch-and-pipeline-network-roundtrips.md 2.9 KB
- references/io-buffer-explicitly-for-small-records.md 2.3 KB
- references/io-mmap-for-random-or-shared-large-files.md 2.7 KB
- references/io-prefer-sequential-over-random.md 3.0 KB
- references/io-stream-http-bodies-with-iter-content.md 4.3 KB
- references/io-zero-copy-when-moving-bytes-as-is.md 2.9 KB
- references/mem-bound-the-working-set.md 2.8 KB
- references/mem-prefer-generators-over-lists-for-pipelines.md 2.1 KB
- references/mem-release-references-explicitly.md 2.7 KB
- references/mem-shrink-dtypes-before-loading.md 2.1 KB
- references/mem-stream-dont-slurp.md 2.1 KB
- references/mem-use-views-not-copies.md 2.4 KB
- references/obs-instrument-throughput-rows-per-second.md 3.6 KB
- references/obs-measure-iowait-not-just-cpu.md 3.3 KB
- references/obs-profile-with-py-spy-or-strace-for-syscall-storms.md 4.5 KB
- references/pipe-apply-backpressure-from-slow-stages.md 3.6 KB
- references/pipe-checkpoint-progress-for-resumability.md 4.0 KB
- references/pipe-prefer-pull-iteration-over-push-callbacks.md 3.3 KB
- references/pipe-use-bounded-queues-for-producer-consumer.md 2.9 KB
- references/spill-external-merge-sort-when-data-exceeds-ram.md 3.7 KB
- references/spill-partition-by-hash-for-out-of-core-groupby-join.md 3.4 KB
- references/spill-use-engines-that-spill-automatically.md 3.5 KB
- references/spill-use-temp-files-not-process-memory.md 3.1 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.
- 5d ago First seen · 137 lines · 242 tokens per session scan A 1755a5e1e8e5
io-bound-data-processing is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 242 tokens to every session and 3,489 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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