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-portfolio-observability)<a href="https://agentmods.dev/skills/geledek/enterprise-ai-transformation-skills/process-portfolio-observability"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/process-portfolio-observability/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-portfolio-observability"><img src="https://agentmods.dev/badge/skills/geledek/enterprise-ai-transformation-skills/process-portfolio-observability.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 | $0.00136 | $0.02151 |
| Opus 5 | $0.00068 | $0.01076 |
| Sonnet 5 | $0.00027 | $0.00430 |
| Haiku 4.5 | $0.00014 | $0.00215 |
Grade A, and why
process-portfolio-observability 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process — AI Portfolio Observability
Most enterprises run AI like a black box: they ship agents, they pay tokens, they cannot answer "what did this earn us net of oversight?" PwC's 2026 ROI guidance is explicit — gross productivity is not the number; net-of-oversight ROI is. The european fintech case shows the trap: 40% deflection headline, 22% CSAT drop, net value negative. This skill builds the instrumentation that exposes that math before the board does.
Anchor: PwC three-channel ROI (efficiency + growth + revenue) minus oversight cost; IMDA Dimension 4 structural controls; NIST RMF MANAGE function on continuous monitoring; 70/20/10 portfolio mix prior (70% core / 20% adjacent / 10% transformational). Distinct from BCG's effort-split and training-budget uses of the same digits elsewhere in this plugin — always read "70/20/10" here as portfolio mix.
Verdict vocabulary (stable output contract): Observable-and-governed / Partial-observability / Black-box.
Dimension 1: KPI Tree Construction
Every initiative gets three connected layers. If any layer is missing, the agent is unobservable by definition.
- Input KPIs — what is consumed? (Usage volume, p50/p95 latency, tokens per call, token cost per call, infra spend, human-in-the-loop minutes per call.)
- Output KPIs — what does the agent produce? (Task-completion rate, override/correction rate, hard-error rate, hallucination rate on golden set, CSAT or NPS delta, escalation rate to human.)
- Outcome KPIs — what does the business get? (Hours saved per week, revenue lift, cycle-time drop, defect rate drop, headcount reallocated — tied to a P&L line.)
The tree must connect: input dollars → output behavior → outcome dollars. If "hours saved" cannot be traced to a specific output KPI driving it, the number is fiction. Consult pwc-20-item-checklist.md: items 11–16 cover the input/output/outcome wiring expected by audit.
Output: INPUT_KPIS | OUTPUT_KPIS | OUTCOME_KPIS | TREE_CONNECTIVITY | P&L_LINE_OWNER
What ships with it
2 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.
- 12d ago First seen · 98 lines · 136 tokens per session scan A df4f30179b76
process-portfolio-observability is a skill published in the GitHub repository geledek/enterprise-ai-transformation-skills (9 stars, last pushed 2mo ago), licensed MIT. It adds 136 tokens to every session and 2,151 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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