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 agentmods add rules/saif-shines/technical-writing-cursor-rules/audience-analysisgit clone --depth 1 https://github.com/saif-shines/technical-writing-cursor-rulesWrote 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/saif-shines/technical-writing-cursor-rules/audience-analysis)<a href="https://agentmods.dev/rules/saif-shines/technical-writing-cursor-rules/audience-analysis"><img src="https://agentmods.dev/badge/rules/saif-shines/technical-writing-cursor-rules/audience-analysis.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00219 |
| Opus 5 | $0.00000 | $0.00110 |
| Sonnet 5 | $0.00000 | $0.00044 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
audience-analysis 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 4d 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.
What it actually says
metadata: alwaysApply: true
Audience Analysis
Define Your Audience's Needs
Answering these questions helps determine document content:
- Who is your target audience? (Consider roles and proximity to knowledge)
- What is their goal? Why are they reading this?
- What do they already know before reading?
- What should they know or be able to do after reading?
Determine What Your Audience Needs to Learn
- List tasks the audience needs to perform or information they need to learn.
- Consider task order if applicable.
- Focus on information for design specs, tasks for how-to guides.
Fit Documentation to Your Audience
- Use empathy: Write for the audience's curiosity, not your own.
- Match vocabulary and concepts to the audience.
- Be mindful of proximity: Explain more as the audience widens or is less familiar.
- Beware the "Curse of Knowledge": Explain concepts experts might take for granted.
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.
- 4d ago First seen · 32 lines · 0 tokens per session scan A 4fb7f762573d
audience-analysis is a cursor rule published in the GitHub repository saif-shines/technical-writing-cursor-rules (7 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 219 tokens. 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 2026-08-31.
Other cursor rules, from other repositories
app-router-patterns
Next.js 14+ App Router patterns — Server Components, Client Components, Route Handlers, Server Actions, and metadata API.
documentation-standards
Documentation standards for code, APIs, and architecture.
monitoring
Monitoring, observability, and alerting rules.
token-efficiency
Token optimization rules to reduce AI costs by 30%+.
performance
Frontend performance optimization rules. Apply when optimizing UI or bundle.
data-fetching
Data fetching patterns with TanStack Query, Server Components, and Supabase.