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 cxcscmu/SkillLearnBench --skill parallel-processinggit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/parallel-processing)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/parallel-processing"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/parallel-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 | $0.00019 | $0.00271 |
| Opus 5 | $0.00010 | $0.00135 |
| Sonnet 5 | $0.00004 | $0.00054 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
parallel-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 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
Parallel Processing with joblib
Grid Search Parallelization
from joblib import Parallel, delayed
import itertools
def evaluate_params(min_samples, epsilon, shape_weight, citsci_grouped, expert_grouped, all_images):
# ... evaluate one hyperparameter combination
return f1_avg, delta_avg, min_samples, epsilon, shape_weight
param_grid = list(itertools.product(
range(3, 10), # min_samples
range(4, 25, 2), # epsilon
[round(0.9 + i*0.1, 1) for i in range(11)] # shape_weight
))
results = Parallel(n_jobs=-1)(
delayed(evaluate_params)(ms, eps, sw, citsci_grouped, expert_grouped, all_images)
for ms, eps, sw in param_grid
)
Key Points
n_jobs=-1uses all available coresdelayed()wraps the function for lazy evaluation- Each call should be independent (no shared mutable state)
- Pass pre-grouped DataFrames to avoid redundant groupby in each worker
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 · 35 lines · 19 tokens per session scan A c863258192fd
parallel-processing is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 271 once invoked, about $0.0001 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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