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/role-redesign-for-ai)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/role-redesign-for-ai"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/role-redesign-for-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/role-redesign-for-ai"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/role-redesign-for-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 | $0.00133 | $0.01379 |
| Opus 5 | $0.00067 | $0.00690 |
| Sonnet 5 | $0.00027 | $0.00276 |
| Haiku 4.5 | $0.00013 | $0.00138 |
Grade A, and why
role-redesign-for-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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role Redesign For AI Skill
When AI absorbs 40% of a role's tasks, orgs default to the worst option: say nothing, and let expectations quietly inflate until the human is doing their old job plus supervising the machine, evaluated by standards from neither. This skill makes the redesign explicit — what the role stops doing, what it now owns, and what "good" means after the shift.
What This Skill Produces
- A task inventory, before/after: what AI took, what it created, what stayed human — with hours
- The redefined core: the role's new centre of gravity, written as a charter
- New expectations & metrics: what performance means now (and which old metrics are dead)
- Level and growth-path implications — including the junior-pipeline problem, faced honestly
Required Inputs
Ask for (if not already provided):
- The role today: title, level, the real task list (or the JD plus what the JD lies about)
- What AI actually absorbed — observed, not vendor-promised: which tasks, how completely, with what verification burden
- The person/team context: one person or a team of eight? tenure mix? current performance framework?
- The org's honest intent: same headcount doing more? fewer people? higher-value work? (The redesign differs; refusing to pick is itself the problem — flag it.)
Redesign Method
- Inventory tasks, not titles. List the role's tasks with weekly hours. Mark each: AI-absorbed (machine does it, human spot-checks) · AI-assisted (human does it faster) · AI-created (new work: prompting, verifying, correcting, supervising agents) · Human-core (judgment, relationships, accountability, taste). The AI-created column is the one orgs forget — verification is work, and it's in this role now.
- Balance the hours honestly. Old role = 40h. Absorbed −12h, assisted −6h, created +8h → 10h of genuine capacity. The redesign decides where those hours go on purpose: deeper human-core work, wider scope, or reduced load. Unallocated capacity becomes silent expectation inflation within a quarter.
- Redefine the core. The role's new centre is what only it can be accountable for. Write the charter in outcomes: what this role owns (decisions, quality bars, relationships), what it supervises (the AI-done work — with the verification standard stated), what it no longer does (named, so nobody performs it out of habit or fear).
- Rewrite the metrics. Kill throughput metrics the machine now drives (tickets closed, words shipped, drafts produced) — a human evaluated on machine output is being evaluated on prompt luck. New metrics live where the human is: judgment quality (error catch rate on AI output, decision outcomes), the human-core outcomes, and supervision health. Pair with
ai-assisted-performance-reviewfor the review conversation itself. - Face the ladder problem. If AI absorbed the tasks juniors learned on, the pipeline to senior judgment is cut. The redesign states how the next cohort develops: deliberate reps on AI-done tasks (inefficient on purpose), verification apprenticeships, or a redesigned junior role — "we'll figure it out" is how professions hollow out.
- Plan the conversation. The redesign lands as a change to someone's identity, not their task list. The rollout: the draft is discussed with the people in the role before it's announced, the "no longer does" list is framed as release not demotion, and comp/level implications are stated in the same meeting they're wondered about.
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 · 72 lines · 133 tokens per session scan A 3a2290feaefc
role-redesign-for-ai is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 4d ago), licensed MIT. It adds 133 tokens to every session and 1,379 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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