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 seb1n/awesome-ai-agent-skills --skill task-automationgit clone --depth 1 https://github.com/seb1n/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/seb1n/awesome-ai-agent-skills/task-automation)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/task-automation"><img src="https://agentmods.dev/badge/skills/seb1n/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/seb1n/awesome-ai-agent-skills/task-automation"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/task-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 125 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Data Exfiltration · line 142 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00045 | $0.02079 |
| Opus 5.5 | $0.00018 | $0.00832 |
| Sonnet 5.5 | $0.00009 | $0.00416 |
| Haiku 4.5 | $0.00005 | $0.00208 |
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 Copies of this mod
1 near-identical copy found in the catalogue:
- task-automation — 95% identical, 2 lines differ
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 · 45 tokens per session scan A 37ff143c0c18
task-automation is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (231 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 2,079 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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context-status
Show current context status and session health. Use to check how much context has been used, whether auto-compact is approaching, and what state will be preserved.
cicd-supply-chain
Use when attacking or auditing a CI/CD pipeline or software supply chain — pwn requests, poisoned pipeline execution, compromised/mutable-tag actions, dependency confusion, registry worms, runner backdoors, OIDC trust abuse, SLSA/provenance.