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 glassity/agent-skills --skill glassity-five-stepsgit clone --depth 1 https://github.com/glassity/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/glassity/agent-skills/glassity-five-steps)<a href="https://agentmods.dev/skills/glassity/agent-skills/glassity-five-steps"><img src="https://agentmods.dev/badge/skills/glassity/agent-skills/glassity-five-steps/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/glassity/agent-skills/glassity-five-steps"><img src="https://agentmods.dev/badge/skills/glassity/agent-skills/glassity-five-steps.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 | $0.00115 | $0.02580 |
| Opus 5 | $0.00057 | $0.01290 |
| Sonnet 5 | $0.00023 | $0.00516 |
| Haiku 4.5 | $0.00012 | $0.00258 |
Grade A, and why
glassity-five-steps 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 10d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The five-step FinOps loop — shared definitions
This skill is the single set of definitions the five /finops-* commands
import: vocabulary, gates, formulas, and the automation dial. It
is declarative — it has no workflow of its own. Each command supplies the
workflow and reads its definitions here.
The long-form methodology, including the failure modes each step is designed to
avoid, is in references/five-steps.md. When that file and this skill
disagree, that file wins and this one gets corrected.
1. Vocabulary
The five steps
| # | Step | Command | One-line job |
|---|---|---|---|
| 1 | Discovery | /finops-discover |
Collect and classify optimization ideas |
| 2 | Opportunity Assessment | /finops-assess |
Score value against effort and risk; compute NOV |
| 3 | Stakeholder & Planning Alignment | /finops-align |
One accountable owner and an actionable plan |
| 4 | Execution | /finops-execute |
Turn the plan into a verified change |
| 5 | Feedback Loop & Celebration | /finops-feedback |
Measure realized against projected; close the loop |
The lifecycle is a closed loop: the output of step N is part of the input to step N+1, and the output of step 5 feeds back into step 1.
The four idea types
Every discovered idea is exactly one type, and each type is handled differently downstream:
- Rate optimization — change the unit price (Reserved Instances, Savings Plans, enterprise discounts). Low effort, no architectural risk.
- Usage optimization — eliminate waste (idle, over-provisioned, or orphaned resources; gp2 to gp3). Medium effort.
- Architectural optimization — redesign for inherent efficiency (Graviton, serverless, tiered storage). Highest effort and risk.
- Governance optimization — preventive controls (tagging, budgets, pre-deployment cost checks). Shift-left work lives here.
Lifecycle stages
The server exposes a pipeline stage on each opportunity:
discovery → assessment → alignment → execution → closure → completed
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.
- 10d ago First seen · 226 lines · 115 tokens per session scan A 14df9d9d9988
glassity-five-steps is a skill published in the GitHub repository glassity/agent-skills (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 115 tokens to every session and 2,580 once invoked, about $0.0006 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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