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/rajitsaha/100xprismWrote 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/rajitsaha/100xprism/product-marketing-context)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/product-marketing-context"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/product-marketing-context/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/rajitsaha/100xprism/product-marketing-context"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/product-marketing-context.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 | $0.00029 | $0.01482 |
| Opus 5 | $0.00015 | $0.00741 |
| Sonnet 5 | $0.00006 | $0.00296 |
| Haiku 4.5 | $0.00003 | $0.00148 |
Grade A, and why
product-marketing-context 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 6d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Marketing Context
Create and maintain .agents/product-marketing-context.md — the foundational positioning/messaging document all other marketing skills read, so users don't repeat themselves.
Workflow
Step 1: Check for Existing Context
Check .agents/product-marketing-context.md; also .claude/product-marketing-context.md (older setups) — if found only there, offer to move it to .agents/.
Exists: read it, summarize what's captured, ask which sections to update, gather info only for those.
Doesn't exist — offer two options:
- Auto-draft from codebase (recommended, faster): study the repo — README, landing pages, marketing copy, package.json, etc. — and draft a V1 for the user to review, correct, and fill gaps.
- Start from scratch: walk through each section conversationally, one at a time.
Most users prefer option 1. After presenting the draft, ask: "What needs correcting? What's missing?"
Step 2: Gather Information
Auto-drafting: read the codebase (README, landing pages, marketing copy, about pages, meta descriptions, package.json, existing docs), draft all sections, present, ask what needs correcting or is missing, iterate until satisfied.
From scratch: one section at a time — don't dump all questions at once. Per section: briefly explain what you're capturing, ask, confirm accuracy, move on.
Push for verbatim customer language — exact phrases reflect how customers actually think and speak, making copy more resonant.
Sections to Capture
1. Product Overview
One-liner; what it does (2-3 sentences); category (the "shelf"—how customers search for you); type (SaaS, marketplace, e-commerce, service, etc.); business model and pricing.
2. Target Audience
Target company type (industry, size, stage); decision-makers (roles, departments); primary use case (main problem solved); jobs to be done (2-3 things customers "hire" you for); specific use cases/scenarios.
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.
- 6d ago First seen · 192 lines · 1,482 tokens per session scan A f1e1148cc9d8
product-marketing-context is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 9d ago), licensed MIT. It adds 29 tokens to every session and 1,482 once invoked, about $0.0001 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.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.