Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add geledek/enterprise-ai-transformation-skills/plugin install enterprise-ai-transformation-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/geledek/enterprise-ai-transformation-skills/process-pilot-design)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/process-pilot-design"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/process-pilot-design/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/geledek/enterprise-ai-transformation-skills/process-pilot-design"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/process-pilot-design.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 | $0.00132 | $0.01678 |
| Opus 5 | $0.00066 | $0.00839 |
| Sonnet 5 | $0.00026 | $0.00336 |
| Haiku 4.5 | $0.00013 | $0.00168 |
Grade A, and why
process-pilot-design 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process — AI Pilot Design
Design a 90-day AI pilot with pre-deployment metrics, stop conditions, and workflow redesign baked in. Seven questions. One-page output ready for sponsor approval.
The discipline: narrow scope, pre-deployment metrics, workflow redesign before technology. AI bolted onto a legacy workflow is the #1 process failure mode (Stanford AI Index 2026 — consult stanford-51-deployments.md).
Output contract (stable): a one-page plan with Day 30 / Day 60 / Day 90 gates, pre-deployment metrics, stop conditions, and a pass/fail verdict criterion.
Role 1: Scope and Problem Framing
Answer the two foundational questions before any technology decisions.
SCOPE CHECK — Scope × Execution Grid (consult 95-5-genai-divide.md):
- Narrow scope + simple execution = fast wins (target this quadrant)
- Narrow scope + complex execution = early pilots (proceed with caution)
- Broad scope = partial pilots or failure (justify explicitly if broad scope is proposed)
Classify the proposed pilot:
- What is the scope? (One function, one workflow, one user group)
- What is the execution complexity? (Simple / Complex)
- Is this upper-left on the grid?
PROBLEM STATEMENT: State the friction this pilot addresses in one sentence. This should be friction-first ("users struggle with X because of Y"), not tech-first ("we want to test AI for Z").
Output: SCOPE | COMPLEXITY | GRID POSITION | FRICTION STATEMENT
Role 2: Pre-Deployment Metrics
Name the metrics BEFORE the pilot launches. If you can't name them now, the pilot isn't ready.
THE MEASUREMENT-GAP WARNING (consult european-fintech-case.md):
A leading European fintech replaced 700 agents with one AI system. Tracked volume, response time, cost. Did NOT track resolution quality, repeat-contact rate, CSAT. CSAT dropped 22%. Hiring resumed within months.
Name three metrics that prove this pilot is working:
- [Metric 1 — what it measures, how it will be tracked]
- [Metric 2 — what it measures, how it will be tracked]
- [Metric 3 — what it measures, how it will be tracked]
What ships with it
1 file 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.
- 12d ago First seen · 168 lines · 0 tokens per session scan A f5792341926b
process-pilot-design is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 1,678 once invoked, about $0.0007 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.
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