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 VandanaAjayDubey111/great-pm --skill ai-unit-economicsgit clone --depth 1 https://github.com/VandanaAjayDubey111/great-pmWrote 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/vandanaajaydubey111/great-pm/ai-unit-economics)<a href="https://agentmods.dev/skills/vandanaajaydubey111/great-pm/ai-unit-economics"><img src="https://agentmods.dev/badge/skills/vandanaajaydubey111/great-pm/ai-unit-economics/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/vandanaajaydubey111/great-pm/ai-unit-economics"><img src="https://agentmods.dev/badge/skills/vandanaajaydubey111/great-pm/ai-unit-economics.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 | $0.00094 | $0.03737 |
| Opus 5.5 | $0.00038 | $0.01495 |
| Sonnet 5.5 | $0.00019 | $0.00747 |
| Haiku 4.5 | $0.00009 | $0.00374 |
Grade A, and why
ai-unit-economics 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 25d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex host binding
- Treat references to Claude slash workflows as the equivalently named Codex skill.
- Before delegating to any specialist, read the
great-pm-runtimeskill and the selected packaged role file. - Treat "invoke", "assign", "delegate", "spawn", and source Agent-tool instructions as a required Codex
spawn_agentcall with that role and a bounded assignment. - For the spawn tool's
task_nameoragent_nameidentifier, replace canonical role-name hyphens with underscores (query-refiner-pm→query_refiner_pm). Keep canonical names unchanged in prompts, filenames, verdict logs, and user-facing output; this is an internal identifier mapping, not a role rename. - Store every returned agent identifier. Never call a wait tool until a spawn has returned an identifier, and wait only on identifiers returned by successful spawns.
- If
spawn_agentis unavailable or a spawn fails, report BLOCKED; do not impersonate the specialist or wait on an empty agent set. - Resolve bundled paths from the installed GreatPM plugin root.
- Ignore Claude-only model aliases, colors, turn limits, and tool allowlists.
- Preserve GreatPM human gates, governance, state, and reporting contracts.
AI unit economics — every feature use costs money
Classic SaaS has near-zero marginal cost: once the code is written, the millionth user costs almost nothing to serve. AI products break this assumption. Every query, every feature use, every agent loop spends real tokens that scale linearly (or worse) with usage. Margin is no longer managed per-account at signup — it is managed per-query, every single time the feature fires.
If a PM cannot state the fully-loaded cost of one AI action and the revenue that covers it, the product does not have a business model — it has a demo with a credit card attached to an LLM API.
This skill is the AI extension of cost-model (general initiative cost)
and pricing-models (how you charge). It models the thing those two do
not: per-query variable cost and its margin consequences.
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.
- 25d ago First seen · 288 lines · 94 tokens per session scan A c4f26b7d5dd1
ai-unit-economics is a skill published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 26d ago), licensed MIT. It adds 94 tokens to every session and 3,737 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-21.
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