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/cloudguo123/atomlane/optimize-python-parallelismnpx skills add cloudguo123/atomlane --skill optimize-python-parallelismgit clone --depth 1 https://github.com/cloudguo123/atomlaneWrote 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/cloudguo123/atomlane/optimize-python-parallelism)<a href="https://agentmods.dev/skills/cloudguo123/atomlane/optimize-python-parallelism"><img src="https://agentmods.dev/badge/skills/cloudguo123/atomlane/optimize-python-parallelism.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 | $0.00070 | $0.01064 |
| Opus 5 | $0.00035 | $0.00532 |
| Sonnet 5 | $0.00014 | $0.00213 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
optimize-python-parallelism 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AtomLane Python Advisor
Improve a program only after separating three questions: where time is spent, whether iterations are semantically independent, and whether the proposed executor is likely to beat its overhead. A long runtime is a reason to inspect, not proof that parallel execution is legal or useful.
Keep analysis and execution separate
Use python_parallel_advisor for bounded source analysis. It reads strict
project-local UTF-8 Python, builds a conservative same-module call/effect
summary, and may return a source-hash-bound rewrite preview. It never imports
or executes target code and never changes files.
Read references/python-program-ir.md before interpreting or applying a candidate. It defines classification, proof gates, GIL/spawn constraints, rewrite validity, and the verification certificate.
Call the advisor with:
- an absolute
project_path; - concrete
pathswhen the entrypoint is known, otherwise bounded discovery; - caller-observed hotspots only when they are real serial measurements;
- the actual
execution_context, so an inner pool is not multiplied by an AtomLane or native worker pool; - an explicit worker ceiling only as a ceiling, never as a safety override.
target_platformwhen the optimized program will deploy somewhere other than the analysis host.
Do not run a workload merely to obtain a profile when repeating it may mutate state, incur cost, or affect an external system.
Treat each classification precisely
reviewable_rewriteis the strongest static result, not a runtime proof. Its patch is still conditional on pickling, import, memory, correctness, and measured-performance checks.advisory_onlyidentifies a plausible I/O, network, subprocess, or otherwise conditional opportunity. Explain the missing guarantees; do not apply its outline as an automatic transformation.prefer_nativemeans vectorization or a library-owned worker pool should be considered before another Python pool.already_parallelrequires one coordinated outer/inner resource budget.blockedremains serial until every hard blocker is removed by evidence or a semantics-preserving redesign. A confidence score cannot override a blocker.
What ships with it
2 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 · 104 lines · 70 tokens per session scan A a7defb45a084
optimize-python-parallelism is a skill published in the GitHub repository cloudguo123/atomlane (1 stars, last pushed 2d ago), licensed MPL-2.0. It adds 70 tokens to every session and 1,064 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 2026-09-04.
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