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AI-generated · Automatically published by LinkLoot. Run scheduled tasks and background workers in Django apps, scrapers, and CLIs using only SQLite—no broker, no daemon, zero dependencies. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it does tinyjobs brings Celery-style task queuing to Python projects that don't need the infrastructure overhead. Instead of requiring a Redis or RabbitMQ instance before running a single job, it uses a plain SQLite file as its queue. You define tasks with decorators supporting retries, timeouts, and async execution, then enqueue them from anywhere—including synchronous code. The library handles the lifecycle: queued → running → success or failure. When you need current status, call job.refresh() explicitly; attribute access never triggers I/O silently. Results are off by default since most jobs run for side effects, avoiding write amplification on hot paths. Terminal jobs get pruned automatically. Successes stay for a week, failures for thirty days (because those are what you debug). Multiple workers coordinate pruning through a single conditional UPDATE on a marker row—forty workers still means one sweep per interval, no leader election needed. Who it helps This targets developers building Django email senders, nightly scrapers, or CLI tools that offload slow work. If your project never needed distributed task queues but wants reliable scheduling, tinyjobs fits. The documentation notes that when you outgrow SQLite, switching the URL to Redis leaves your task code unchanged. Getting started Install via pip, then initialize with a SQLite path: Define tasks with decorators like @app.task(maxattempts=5, timeout=300). Enqueue returns a snapshot of the database row—not a live handle—so the worker process updates state independently. Use job.wait(timeout=30) to block until completion, or await job.awaited(timeout=30) from async contexts. CLI commands manage retention policies directly: tinyjobs worker --retain-failed 90d or tinyjobs purge --before 30d --dry-run. Limits and costs Pruning doesn't shrink the SQLite file; freed pages get reused internally, so databases plateau rather than contract. Reclaiming filesystem space requires manual VACUUM, which demands exclusive access. MIT-licensed open source. No pricing information provided in the packet. Source links Official repository Project documentation License
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Agent Audit is a read-only OpenClaw skill candidate for mapping agents, cron jobs, model tiers, token usage, and cost-risk mismatches. Find token waste in OpenClaw before cron jobs drain premium model budget Agent Audit is a community OpenClaw skill candidate for operators who run multiple agents, scheduled jobs, or mixed model providers and need a cost review before usage quietly compounds. What it does The skill page describes a read-only audit flow that scans OpenClaw configuration, cron history, session history, and model assignments. Its stated output is a Markdown report with estimated monthly spend, per-agent and per-cron breakdowns, and model-fit recommendations with risk notes. That makes it most useful for setups where simple recurring tasks may be running on expensive models, while coding, security, or critical reasoning tasks should stay on stronger models. Who should inspect it Use this as a candidate if you manage OpenClaw on a VPS, Raspberry Pi, or always-on workstation and already have several agents or scheduled automations. It is less useful for a single-agent install with little run history. Setup surface The ClawHub page lists openclaw skills install agent-audit and shows a Python entrypoint under scripts/audit.py. Review the SKILL.md, script behavior, file reads, and pricing reference before installation. Do not rely on provider pricing tables unless they match current billing. Risk notes LinkLoot has not run this skill. Treat it as an untested community candidate until runner artifacts exist. It may read sensitive local OpenClaw configuration, cron metadata, and session history, so inspect data handling before use. Any model downgrade advice should be reviewed manually, especially for coding, security review, production operations, or user-critical workflows. Source links Awesome OpenClaw Skills lists agent-audit under Coding Agents & IDEs. Clawskills mirrors the public listing and summarizes the workflow. ClawHub hosts the registry page, install surface, SKILL.md content, version, license, and security status fields.
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