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 arpitexplores/skills-super --skill super-automationgit clone --depth 1 https://github.com/arpitexplores/skills-superWrote 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/arpitexplores/skills-super/super-automation)<a href="https://agentmods.dev/skills/arpitexplores/skills-super/super-automation"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-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/arpitexplores/skills-super/super-automation"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-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 | $0.00019 | $0.00353 |
| Opus 5 | $0.00010 | $0.00177 |
| Sonnet 5 | $0.00004 | $0.00071 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
super-automation scanned grade A with 0 findings 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Super Automation
Overview
Design reliable automations with clear triggers, data flow, and monitoring.
User Intent Examples
- "Need help with GitHub Automation for my product/site."
- "Create a plan for Workflow Automation."
- "Not sure where to start, need a quick assessment."
Workflow
- Define the goal, trigger, and success criteria.
- Map required systems and permissions.
- Design workflow steps and error handling.
- Implement integrations and test with sample data.
- Add monitoring, alerts, and audit logs.
- Document ownership and maintenance steps.
Minimal Intake Questions
- Primary goal or outcome
- Scope (pages, systems, teams, or timeframe)
- Constraints (tools, budget, timeline)
Output Format
- Workflow diagram and trigger spec
- Integration and permissions checklist
- Error handling and retry strategy
- Test results and validation steps
- Monitoring and maintenance plan
Routing Map (Modules)
- GitHub Automation ->
references/modules/github-automation.md - Workflow Automation ->
references/modules/workflow-automation.md
Bundled References
references/modules/scripts/assets/agents/
Compatibility Notes
- If any module references slash commands or tool-specific paths, translate them into plain-language steps.
- Keep outputs platform-agnostic unless the user specifies a specific tool, stack, or agent.
Guardrails
- Do not store secrets in plain text.
- Use idempotent actions where possible.
- Log failures with enough context to debug.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 55 lines · 19 tokens per session scan A 31c49b310200
super-automation is a skill published in the GitHub repository arpitexplores/skills-super (2 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 353 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…
loop
Full execution protocol for MODE: LOOP — the compound-engineering loop: brainstorm → plan → build → review → improve, iterating under defense-in-depth stop conditions with generator/critic separation, durable resumable state, and mandatory compounding learning capture. Loaded on demand by the architect when the loop…
council
Full execution protocol for MODE: COUNCIL -- General Council research, parallel member dispatch, disagreement handling, and synthesis.
bundle-safety
Bundle transform safety — minification variant selection, consumer-constraint verification, identifier preservation, and namespace re-export coverage for build output.
safe-extraction
Apply when extracting code from a large monolith file into submodules. Covers barrel re-exports, internals DI seam proxy patterns, CI invariant allowlist updates, and cross-file test verification. Prevents CI failures, broken imports, and test regressions from code extraction.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…