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 h4vzz/awesome-ai-agent-skills --skill task-automationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/task-automation)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/task-automation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/task-automation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/task-automation"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/task-automation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00030 | $0.02064 |
| Opus 5.5 | $0.00012 | $0.00826 |
| Sonnet 5.5 | $0.00006 | $0.00413 |
| Haiku 4.5 | $0.00003 | $0.00206 |
Grade A, and why
task-automation scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import urllib.request This is a copy
95% identical to task-automation — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Automation
This skill enables an AI agent to design and implement automations for repetitive tasks and workflows. The agent identifies manual processes suitable for automation, selects the right automation pattern (scripts, file watchers, cron jobs, CI/CD triggers, API polling), writes the implementation, and validates it works correctly. The goal is to eliminate toil — repetitive, manual work that scales linearly with workload — and replace it with reliable, hands-off automation.
Workflow
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Analyze the Task: Understand what the user wants to automate, including the trigger (what starts the task), the steps involved, the inputs and outputs, and the current frequency of manual execution. Determine whether the task is event-driven (triggered by a change) or time-driven (runs on a schedule).
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Select the Automation Pattern: Choose the appropriate automation approach based on the trigger type and environment. Common patterns include: shell scripts for one-off or sequential tasks, file watchers (fswatch, inotifywait, chokidar) for reacting to file changes, cron jobs or systemd timers for scheduled recurring tasks, CI/CD pipeline triggers for code-related automation, API polling or webhook listeners for reacting to external service events.
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Design the Implementation: Plan the automation in detail: define the inputs and configuration, error handling strategy (retry logic, alerting, fallback behavior), logging approach, and any secrets or credentials management needed. Consider idempotency — the automation should be safe to run multiple times without side effects.
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Write the Automation Code: Implement the automation using the appropriate tools and languages. Prefer well-established, widely-supported tools: bash/Python for scripts, crontab for scheduling, GitHub Actions or GitLab CI for CI triggers, and standard webhook frameworks for event listeners.
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Test and Validate: Run the automation in a safe environment first. Verify it handles the happy path correctly, then test edge cases: empty inputs, network failures, permission errors, and concurrent executions. Confirm that logging captures enough information for debugging.
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.
- 1mo ago First seen · 200 lines · 30 tokens per session scan A 4d7329f785a6
task-automation is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 21d ago), licensed MIT. It adds 30 tokens to every session and 2,064 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to task-automation, differing in 2 lines, and is treated as a copy.
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clueless-help
Use when the user asks how clueless works, what its commands or levels are, or types /clueless-help.