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 devinilabs/pro-skill --skill operational-enterprise-aigit clone --depth 1 https://github.com/devinilabs/pro-skillWrote 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/devinilabs/pro-skill/operational-enterprise-ai)<a href="https://agentmods.dev/skills/devinilabs/pro-skill/operational-enterprise-ai"><img src="https://agentmods.dev/badge/skills/devinilabs/pro-skill/operational-enterprise-ai/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/devinilabs/pro-skill/operational-enterprise-ai"><img src="https://agentmods.dev/badge/skills/devinilabs/pro-skill/operational-enterprise-ai.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 | $0.00068 | $0.00907 |
| Opus 5 | $0.00034 | $0.00453 |
| Sonnet 5 | $0.00014 | $0.00181 |
| Haiku 4.5 | $0.00007 | $0.00091 |
Grade A, and why
operational-enterprise-ai 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 5d 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.
This is a copy
100% identical to operational-enterprise-ai — 0 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operational Enterprise AI
Build credibility by showing what the system does, where it stops, who approves actions, and how failures recover.
Establish the Story
- Open with the operational problem and one restrained product image or system trace.
- Use a white interlude to quantify the problem with verified metrics.
- Explain each capability as a workflow with permissions and controls.
- Address security, governance, exceptions, and rollback before the conversion ask.
- Move from verified case-study evidence into a qualified demo or waitlist handoff.
Replace source brands, customers, numbers, security badges, screenshots, and claims. Do not invent compliance or performance evidence.
Build the Visual System
- Use near-black, warm white, muted gray, and one restrained spectral treatment.
- Pair a high-x-height sans-serif with compact mono labels and tabular numerals.
- Use hard grid lines, square media, low radii, and minimal shadow.
- Keep data legibility ahead of atmosphere.
- Reserve white chapters for operational explanation and metric pauses.
- Avoid glowing AI orbs, particle fields, neon gradients, and generic cyber-security imagery.
Compose the Page
- Header: show product, solutions, security, case studies, and one qualified action.
- Hero: state the system boundary and pair it with one deliberate operational visual.
- Metrics: use only verified numbers with scope, source, and timeframe.
- Solution rows: summarize workflow, permissions, action, approval, output, audit, exception, and rollback.
- Product demo: show real or clearly labeled sample data and deterministic state changes.
- Security: connect controls to concrete risks; do not use unsupported badges.
- Case studies: separate verified implementation facts from marketing interpretation.
- Testimonials: use grayscale portrait evidence only when licensed and real.
- FAQ: resolve ownership, data handling, integrations, review, failure, and procurement questions.
- Final CTA: qualify who the product is for and explain what happens after submission.
What ships with it
5 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.
- 5d ago First seen · 81 lines · 68 tokens per session scan A fbbf4387d16c
operational-enterprise-ai is a skill published in the GitHub repository devinilabs/pro-skill (23 stars, last pushed 26d ago), licensed MIT. It adds 68 tokens to every session and 907 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to operational-enterprise-ai, differing in 0 lines, and is treated as a copy.
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