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/year-in-review)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/year-in-review"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/year-in-review/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/year-in-review"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/year-in-review.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 | $0.00106 | $0.00929 |
| Opus 5 | $0.00053 | $0.00464 |
| Sonnet 5 | $0.00021 | $0.00186 |
| Haiku 4.5 | $0.00011 | $0.00093 |
Grade A, and why
year-in-review 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 9d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Year in Review
The problem with New Year's resolutions is they're a wish-list with no diagnosis — so they fail. This runs the review a good operator runs on a project, pointed at your year: what actually happened (not the highlight reel), where your energy went, what you'd repeat and stop, and a single organising theme that makes next year's dozens of small choices easier. One theme beats twelve resolutions.
What This Skill Produces
- The retrospective — the year in the domains that matter to you (work, health, relationships, money, growth), honestly
- The energy audit — what consistently gave energy vs. drained it (the most useful and most skipped part)
- The misses — what didn't happen and the real reason, without self-flagellation
- Next year's theme — one organising word/phrase, plus 3–5 concrete commitments tied to it
Required Inputs
Ask for these if not provided:
- The raw material — highs, lows, big changes, and anything you're proud of or avoiding (a brain-dump is fine)
- The domains you care about — so the review reflects your life, not a generic template
- How last year's goals went — if you set any (be honest about the gap)
- Constraints for next year — anything fixed (a move, a baby, a health issue) that shapes what's realistic
Framework: Diagnose, Then Direct
- Facts before feelings. What actually happened this year, listed plainly, before judging it.
- Energy is the signal. What gave energy and what drained it predicts next year better than any goal — audit it explicitly.
- Misses get a real reason. Not "I was lazy" — the actual constraint (no time, wrong system, wrong goal). Reasons are actionable; shame isn't.
- One theme, not a list. A single organising idea ("build, don't polish"; "less, better") makes small decisions automatic all year.
- Commitments, not resolutions. 3–5 concrete, checkable actions tied to the theme — fewer than you want.
Output Format
[Year] in Review — [name]
One-line summary of the year: …
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.
- 9d ago First seen · 75 lines · 106 tokens per session scan A 7bdca22b8872
year-in-review is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 929 once invoked, about $0.0005 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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