PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill agent-era-pricinggit clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/mohitagw15856/pm-claude-skills/agent-era-pricing)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-era-pricing"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-era-pricing/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/mohitagw15856/pm-claude-skills/agent-era-pricing"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-era-pricing.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 | $0.00108 | $0.01291 |
| Opus 5.5 | $0.00043 | $0.00516 |
| Sonnet 5.5 | $0.00022 | $0.00258 |
| Haiku 4.5 | $0.00011 | $0.00129 |
Grade A, and why
agent-era-pricing 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 2d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Era Pricing Skill
Seat pricing quietly assumed the user was a human who logs in. Agents break the assumption from both sides: your customers need fewer seats (one operator, ten agents), and your product gets more usage than ever. This skill redesigns the model around a value metric that survives non-human users — without torching existing revenue on the way.
What This Skill Produces
- A value-metric decision: what you charge for when seats stop proxying value
- Agent-tier design: how agent/API usage is packaged, fenced, and priced
- Cannibalisation math: what happens to current revenue under the new model, computed on real cohorts
- A phased migration plan for existing customers, with the grandfathering decision made explicitly
Required Inputs
Ask for (if not already provided):
- Current model: plans, price points, seat definitions, current API/automation pricing if any
- The evidence of pressure: seat contraction, API traffic growth, customer asks, competitor moves
- Unit economics: cost to serve a seat vs an API call/agent action (rough is fine, labelled)
- 3-5 representative customer profiles with seat counts and usage (the cannibalisation test set)
Method
- Find the value metric that survives agents. Test candidates against three questions: does it scale with the value the customer receives (not your costs)? · is it counted identically whether a human or agent drives it? · can the customer predict their bill? Strong candidates are usually outcomes or work-objects (invoices processed, tickets resolved, campaigns run, records enriched) — not raw API calls (unpredictable, punishes retries) and not seats (dying assumption).
- Price the human and the agent differently, deliberately. The durable pattern is a hybrid: a platform/human layer (flat or few-seats — access, admin, support) plus a work layer priced on the value metric, agnostic to who did the work. Decide where agents authenticate: agent traffic on a user's token counted as that user's work, not as a "seat".
- Design the fences. What separates tiers now that seats don't: volume bands on the value metric, rate/concurrency limits, SSO/audit/compliance (still human-org fences), model/automation quality tiers. Every fence must be measurable and hard to game — name the gaming vector for each and why it's acceptable.
- Run the cannibalisation math on real cohorts. For each customer profile: current annual price vs new-model price at current usage, at 2× automation, at 5×. Sum to a revenue bridge. If the new model loses money on your best cohort, the metric or the bands are wrong — fix the model, don't hide the row.
- Phase the migration. New customers first (cleanest signal) → opt-in for existing (with a calculator showing their number) → forced migration only with long notice and a cap ("no more than X% increase in year one"). Grandfathering is a decision with a cost, not a default: state what perpetual legacy plans cost in five years.
- Set the tripwires. Which metrics reprice this model: value-metric inflation/deflation, gaming detected, agent share of traffic crossing thresholds. Pricing in the agent era is a program, not a project.
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.
- 2d ago First seen · 77 lines · 108 tokens per session scan A 3e43cd5c696a
agent-era-pricing is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 108 tokens to every session and 1,291 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 2026-10-12.
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