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Agent skill

Optimize Python Parallelism

by hashgraph-online in hashgraph-online/awesome-codex-plugins

Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work.

MPL-2.0Auto-check passedDevelopment

Install Optimize Python Parallelism

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill optimize-python-parallelism -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins optimize-python-parallelism --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/cloudguo123/atomlane/skills/optimize-python-parallelism .claude/skills/optimize-python-parallelism && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
optimize-python-parallelism
GitHub stars
1.2k
Token cost
~1.3k tokens
SKILL.md length
656 words
Files
3 (incl. references)
Skills in repo
686
Repo updated
First seen
Licence
MPL-2.0

At a glance

Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work.

  • Works in 6 steps: Compile the source without importing it. → Run focused unit/integration tests. → Differentially compare serial and… → …
  • Explicit Python speedup
  • SKILL.md covers Keep analysis and execution…, Treat each classification…, Apply only a minimal, current… and Verify before claiming an…
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Optimize Python Parallelism is an agent skill from hashgraph-online/awesome-codex-plugins. Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work. Use for explicit Python speedup or parallelization requests and evidence-backed long-running entrypoints; skip ordinary Python edits, test execution alone, short scripts, and projects that merely contain Python files.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/python-program-ir.md`).

It sits in Development. It works with Python. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is MPL-2.0.

When your agent uses it

  • Explicit Python speedup
  • Parallelization requests and evidence-backed long-running entrypoints
  • Skip ordinary Python edits
  • Test execution alone

Example prompts

  • “/optimize-python-parallelism”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Compile the source without importing it.
  2. Run focused unit/integration tests.
  3. Differentially compare serial and parallel return values, ordering,
  4. Exercise process candidates with an explicit multiprocessing spawn
  5. For safe repeatable work, compare multiple serial and parallel samples and
  6. Revert only the newly proposed refactor if correctness fails or measured

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Optimize Python Parallelism loads about 1.3k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 656 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~98
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its MPL-2.0 licence (© hashgraph-online). 656 words, ~1,340 tokens.

Download SKILL.mdSave it as .claude/skills/optimize-python-parallelism/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
optimize-python-parallelism
description
Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work. Use for explicit Python speedup or parallelization requests and evidence-backed long-running entrypoints; skip ordinary Python edits, test execution alone, short scripts, and projects that merely contain Python files.
license
MPL-2.0

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 paths when 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_platform when 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_rewrite is the strongest static result, not a runtime proof. Its patch is still conditional on pickling, import, memory, correctness, and measured-performance checks.
  • advisory_only identifies a plausible I/O, network, subprocess, or otherwise conditional opportunity. Explain the missing guarantees; do not apply its outline as an automatic transformation.
  • prefer_native means vectorization or a library-owned worker pool should be considered before another Python pool.
  • already_parallel requires one coordinated outer/inner resource budget.
  • blocked remains serial until every hard blocker is removed by evidence or a semantics-preserving redesign. A confidence score cannot override a blocker.

If the current task does not authorize editing source, stop at advice. A request to analyze performance alone does not authorize a refactor.

Show full SKILL.md (336 more words)Show less

Apply only a minimal, current rewrite

Before using a preview, recompute the source SHA-256 and require an exact match. If source, runtime evidence, executor choice, or resource assumptions changed, discard the preview and analyze again.

For the initial supported CPU pattern, preserve ordered map semantics with ProcessPoolExecutor.map; require a module-level worker and a spawn-safe if __name__ == "__main__" boundary. Do not move logging, file writes, database work, randomness, environment reads, or unknown calls into speculative workers. Keep exception, cancellation, result order, and deterministic merge behavior explicit.

Choose by workload rather than syntax:

  • pure Python CPU work: processes, after spawn and pickling review;
  • blocking read-only I/O: bounded threads, after exception and resource review;
  • established async call chains: bounded async tasks and cancellation design;
  • NumPy, BLAS, OpenMP, PyTorch, and similar native kernels: vectorize or use their own workers, then cap outer concurrency;
  • subprocess batches: prefer AtomLane task atoms with declared effects when source modification adds no value.

Verify before claiming an improvement

After an authorized edit:

  1. Compile the source without importing it.
  2. Run focused unit/integration tests.
  3. Differentially compare serial and parallel return values, ordering, exceptions, stdout/stderr, files, hashes, random seeds, and numeric tolerance.
  4. Exercise process candidates with an explicit multiprocessing spawn context on every platform. Windows process pools have a 61-worker ceiling; treat it as an upper bound, not a useful default.
  5. For safe repeatable work, compare multiple serial and parallel samples and report p50/p90, throughput, peak memory, worker count, and break-even size.
  6. Revert only the newly proposed refactor if correctness fails or measured performance regresses; preserve unrelated user changes.

Use the existing AtomLane plan/execution path when several validation commands are independently runnable, but treat a Python program that owns an inner pool as one native-parallel compound atom unless the combined budget proves nested concurrency safe.

Finish with a parallelization certificate containing the source and candidate hashes, satisfied and unresolved proof obligations, executor/resource choice, correctness evidence, measured or explicitly modeled benefit, limitations, and rollback boundary. Never describe a modeled projection as measured speedup.

© hashgraph-online, MPL-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in plugins/cloudguo123/atomlane/skills/optimize-python-parallelism of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • agents/openai.yaml
  • references/python-program-ir.md

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Optimize Python Parallelism next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Optimize Python Parallelism compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Optimize Python Parallelism this skillhashgraph-online/awesome-codex-plugins1.2k—~1.3kAutomated safety check: PassMPL-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Summarise Ecosystem Resultsastral-sh/ruff50k1 repos~2.2kAutomated safety check: PassMIT
Minimizing Ty Ecosystem Changesastral-sh/ruff50k—~4.6kAutomated safety check: PassMIT
Merge Dependabot PRsonyx-dot-app/onyx32k1 repos~2.2kAutomated safety check: PassMIT
Code Graph Mermaid Diagramstrailofbits/skills7.4k1 repos~1.7kAutomated safety check: PassCC-BY-SA-4.0

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Works with

Categories

Questions about Optimize Python Parallelism

What does Optimize Python Parallelism do?

Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work. Optimize Python Parallelism is an agent skill from hashgraph-online/awesome-codex-plugins. Analyze and, when the task authorizes source optimization, refactor a concrete long-running Python entrypoint containing repeated independent work.

When should I use Optimize Python Parallelism?

Optimize Python Parallelism fits situations like: explicit Python speedup; parallelization requests and evidence-backed long-running entrypoints; skip ordinary Python edits; test execution alone.

How do I install Optimize Python Parallelism in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill optimize-python-parallelism -a claude-code`. Or copy the skill folder (plugins/cloudguo123/atomlane/skills/optimize-python-parallelism in hashgraph-online/awesome-codex-plugins) into .claude/skills/optimize-python-parallelism in your project. Claude Code loads it when a task matches its description.

How do I install Optimize Python Parallelism in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill optimize-python-parallelism -a codex`. Or copy the skill folder (plugins/cloudguo123/atomlane/skills/optimize-python-parallelism in hashgraph-online/awesome-codex-plugins) into .agents/skills/optimize-python-parallelism in your project. Codex loads it when a task matches its description.

Can I use Optimize Python Parallelism in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill optimize-python-parallelism -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/optimize-python-parallelism, .gemini/skills/optimize-python-parallelism, .github/skills/optimize-python-parallelism and .opencode/skills/optimize-python-parallelism in your project.

What does Optimize Python Parallelism need to run?

SKILL.md names no scripts, command-line tools or credentials: Optimize Python Parallelism is instructions for the agent only. Our summary lists: Python 3.

Does Optimize Python Parallelism access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Optimize Python Parallelism safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Optimize Python Parallelism use?

Optimize Python Parallelism is published under the MPL-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Optimize Python Parallelism use?

About 1.3k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Optimize Python Parallelism?

Skills that share tags, products or a category with Optimize Python Parallelism: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Summarise Ecosystem Results (astral-sh/ruff, 50k stars), Minimizing Ty Ecosystem Changes (astral-sh/ruff, 50k stars) and Merge Dependabot PRs (onyx-dot-app/onyx, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Optimize Python Parallelism?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,242 GitHub stars. The repository holds 686 skills in this directory. The repository was last updated on October 8, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.