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Install in OpenClaw
/install parallel-tfidf-search-workload-balancing
Description
Optimize workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to balance work distribution, improve par...
Usage Guidance
This skill appears coherent and safe: it contains example algorithms and patterns for balancing workloads and asks for no credentials or installs. Before running any provided snippets in your environment, review and test them (they are illustrative and may need adaptation), ensure any referenced network calls (e.g., fetch in the I/O example) point to trusted endpoints, and avoid copying snippets directly into production without standard safety checks (timeouts, input validation, resource limits).
Capability Analysis
Type: OpenClaw Skill
Name: parallel-tfidf-search-workload-balancing
Version: 0.1.0
The skill bundle provides educational and functional code snippets for workload balancing and distributed system patterns, such as consistent hashing, circuit breakers, and work stealing. The implementation uses standard Python libraries (asyncio, concurrent.futures, hashlib) and the instructions in SKILL.md are strictly aligned with the stated purpose of optimizing parallel execution without any indicators of malicious intent or prompt injection. Files analyzed: SKILL.md, references/advanced_techniques.md, and _meta.json.
Capability Assessment
Purpose & Capability
Name/description align with the content: the SKILL.md and references provide standard load‑balancing strategies and helper algorithms. No unrelated libs, credentials, or system access are requested.
Instruction Scope
Runtime instructions and code snippets are limited to partitioning, scheduling, and monitoring logic. They do not direct the agent to read system files, access credentials, or exfiltrate data. Example snippets reference a fetch() in an I/O example but do not instruct external data submission beyond normal network I/O patterns expected for I/O-bound tasks.
Install Mechanism
Instruction-only skill with no install spec and no code files to be executed by the platform; nothing is downloaded or written to disk by an installer.
Credentials
No required environment variables, credentials, or config paths are declared. The strategies shown do not require secrets and the declared requirements are minimal and proportionate.
Persistence & Privilege
always is false and the skill does not request persistent or elevated platform privileges, nor does it modify other skills or system-wide configuration.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install parallel-tfidf-search-workload-balancing - After installation, invoke the skill by name or use
/parallel-tfidf-search-workload-balancing - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Bulk publish from all-task-skills-dedup
Metadata
Frequently Asked Questions
What is workload-balancing?
Optimize workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to balance work distribution, improve par... It is an AI Agent Skill for Claude Code / OpenClaw, with 79 downloads so far.
How do I install workload-balancing?
Run "/install parallel-tfidf-search-workload-balancing" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is workload-balancing free?
Yes, workload-balancing is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does workload-balancing support?
workload-balancing is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created workload-balancing?
It is built and maintained by lnj22 (@lnj22); the current version is v0.1.0.
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