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 morning-intelligencegit 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/morning-intelligence)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/morning-intelligence"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/morning-intelligence/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/morning-intelligence"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/morning-intelligence.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 | $0.00103 | $0.02292 |
| Opus 5.5 | $0.00041 | $0.00917 |
| Sonnet 5.5 | $0.00021 | $0.00458 |
| Haiku 4.5 | $0.00010 | $0.00229 |
Grade A, and why
morning-intelligence 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Morning Intelligence Skill
Write the prompt that writes your briefing. A 15-question interview extracts your exact context — role, topics, sources, exclusions, format, recency — then produces a single master prompt you can paste into a scheduled task or Claude Code Routine and never touch again.
Pro tip: Run this interview with Opus for the best output. Opus asks sharper follow-up questions and writes a tighter master prompt.
Credit: Originally created by Ashwin Francis (Cash&Cache) — adapted and extended for this library.
Required Inputs
No inputs required upfront. The skill runs the interview first.
If the user has already provided context (e.g. pasted a role description or topic list), absorb it and skip those questions in the interview — don't ask for information already given.
How the Interview Works
Run questions one at a time (or in small groups of 2–3 where they're closely related). Don't dump all 15 at once. Wait for each answer before proceeding. Ask natural follow-ups where the answer is vague.
Interview Questions
Block 1 — Who you are and how you read
- What is your role, and what lens do you read news through? (e.g. "Head of Product at a B2B SaaS — I read for competitive moves, AI tooling, and enterprise buying signals.")
- What are the 3–5 topics you always want covered? Be specific — "AI" is too broad; "AI applied to enterprise software" is better.
- What are 2–3 topics you actively want filtered out — things that waste your time every morning?
Block 2 — Sources and signals
- Which publications, newsletters, or outlets do you trust most? (Examples: The Information, TLDR, Benedict Evans, Stratechery, FT, specific subreddits)
- Are there any Twitter/X accounts, Substack writers, or niche sources that are must-reads for you specifically?
- Is there any geography that matters — are you focused on a specific country, region, or market?
Block 3 — Story type and recency
- What mix of story types do you want? Rank or weight these: breaking news / in-depth analysis / opinion / data & research / product launches & announcements.
- How fresh does the content need to be? Only today's news? Last 24 hours? Last 48 hours? Or are you okay with "last few days" if a story is important enough?
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 · 207 lines · 103 tokens per session scan A 40e0060c7f28
morning-intelligence is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 2,292 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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