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 ai-context-primergit 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/ai-context-primer)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/ai-context-primer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-context-primer/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/ai-context-primer"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-context-primer.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 | $0.00149 | $0.01094 |
| Opus 5.5 | $0.00060 | $0.00438 |
| Sonnet 5.5 | $0.00030 | $0.00219 |
| Haiku 4.5 | $0.00015 | $0.00109 |
Grade A, and why
ai-context-primer 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Context Primer
Generic AI answers are almost always a context problem, not a model problem — you asked for something the AI had no way to tailor, so it gave you the average of everything. The fix is priming: giving it the background, constraints, examples, and format it can't guess before you make the request. This builds that primer for your task, so the first result is close, not a starting point you spend five rounds correcting.
What This Skill Produces
- The context this task actually needs — the who (audience, you), the what (goal, background), the constraints (must/must-not), the examples (what good looks like), and the format (structure, length, tone)
- A reusable primer block — a clean paste-ahead of your request that briefs the AI properly, not a one-off
- The gap it fills — what the AI was missing that made earlier answers generic, made explicit
- What to leave out — the noise that dilutes rather than helps, so the primer stays sharp
- Starved vs briefed, shown — a quick before/after so you feel the difference context makes
- A primer habit — how to make briefing-before-asking your default for tasks that matter
Required Inputs
Ask for these if not provided:
- The task — what you want the AI to do
- The background it can't guess — your situation, audience, goal, prior context
- What good looks like — an example, a reference, or the standard you're holding it to
- Constraints — must-haves, must-avoids, length, tone, format
- What went generic before — if you've tried, what was off (points at the missing context)
Framework: Brief It Like It Knows Nothing About You
- Name what the AI can't know. It has no access to your situation, audience, standards, or prior work — list what it'd need to tailor the answer, because that's exactly what's missing.
- Assemble the five pieces. Who (audience + you), what (goal + background), constraints (must/must-not), examples (what good looks like), format (structure/length/tone) — the reliable spine of good context.
- Show, don't just tell. An example of the output you want, or a reference you like, teaches the AI more than a paragraph of description — include one where the task is fuzzy.
- Cut the noise. More context isn't better — irrelevant detail dilutes the signal. Keep what changes the output, drop what doesn't.
- Make it reusable. Package it as a primer block you can paste ahead of similar requests, not something you rebuild each time.
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 · 72 lines · 149 tokens per session scan A b8fcb325a2ae
ai-context-primer is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 149 tokens to every session and 1,094 once invoked, about $0.0006 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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