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.
git 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/rules/mohitagw15856/pm-claude-skills/get-more-from-ai)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/get-more-from-ai"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/get-more-from-ai/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/rules/mohitagw15856/pm-claude-skills/get-more-from-ai"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/get-more-from-ai.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 | $0.00135 | $0.01084 |
| Opus 5 | $0.00068 | $0.00542 |
| Sonnet 5 | $0.00027 | $0.00217 |
| Haiku 4.5 | $0.00014 | $0.00108 |
Grade A, and why
get-more-from-ai 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 8d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Get More From AI
Most people use AI at a fraction of its capability — one-shot questions, accept the first answer, move on — and never learn the handful of techniques that separate a frustrating tool from a force multiplier. The gap isn't a better model; it's how you use the one you have. This reads how you use AI now, picks the two or three techniques that would help you most next, and shows the difference on your own examples — a targeted level-up, not a firehose of a hundred tips.
What This Skill Produces
- An honest read of your current use — how you use AI now and the specific habits capping your results (one-shot asking, no context, accepting first drafts, no examples)
- Your highest-leverage next techniques — the 2–3 that would most improve your results, from: giving rich context, iterating instead of accepting, showing examples, decomposing big asks, assigning a role, and verifying output
- A before/after on your own use — the same task done your current way vs the leveled-up way, so the payoff is concrete
- A simple practice path — how to build the new techniques into habits one at a time, not all at once
- The mindset shift — from "search box" to "collaborator you brief, iterate with, and check" — the frame behind all the techniques
Required Inputs
Ask for these if not provided:
- How you use AI now — a real example of a prompt or task (shows exactly what to level up)
- Where it frustrates you — where results fall short (points at the missing technique)
- What you use it for — your main tasks and domains
- Your level — beginner / regular / trying to go advanced
Framework: Close The Gap One Technique At A Time
- Diagnose the current pattern. Look at how the person actually uses AI — the habit capping their results is usually obvious (one-shot, context-starved, first-draft-accepting) and points straight at the fix.
- Pick the 2–3 that pay off most. Don't dump every technique. From context-giving, iterating, showing examples, task decomposition, role-setting, and verifying — choose the few that fix this person's bottleneck.
- Show the payoff on their example. Take their real task and show it done the leveled-up way beside their current way — the gap is the motivation.
- Reframe the mindset. The through-line: AI is a collaborator you brief richly, push back on, and iterate with — not a vending machine you query once. The techniques follow from the frame.
- Practice one at a time. Build one technique into a habit before adding the next — layering beats a firehose that nothing sticks from.
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.
- 8d ago First seen · 65 lines · 135 tokens per session scan A 579029b1fc03
get-more-from-ai is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 135 tokens to every session and 1,084 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 2026-09-03.
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