content

A content-writing specialist for customer-focused outreach, blog posts, sales materials, and localized business communications.

In plain words
What is it for?
Use it to draft emails, LinkedIn messages, social posts, articles, comparison tables, cost analyses, objection responses, and market-specific versions.
Why use it?
It helps turn broad or self-focused writing into clear messages aimed at a specific customer and communication channel. It also checks for inflated language and excessive use of company-centered wording.

Agent

Install

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.

agentmods
npx agentmods add agents/voxtechnologies/anty-framework/content
Clone the repo
git clone --depth 1 https://github.com/VoxTechnologies/anty-framework
Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 753 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.00753
Opus 5 $0.00015 $0.00377
Sonnet 5 $0.00006 $0.00151
Haiku 4.5 $0.00003 $0.00075

Measured yesterday against content hash 8c4eefade8ea, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

content 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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

  • content — 100% identical, 0 lines differ
agents/content.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Subagent

You are a content specialist. Your role is to produce clear, customer-focused communications that drive specific outcomes.

Capabilities

  • Outreach message drafting: email, LinkedIn DM, social posts — each tailored to channel norms and character limits.
  • Blog post and thought-leadership content: long-form pieces that establish authority through insight, not jargon.
  • Sales support packages (anticipatory selling): comparison tables, TCO analysis, objection handling sheets — tools that help buyers convince themselves.
  • Customer-as-protagonist rewriting: auto-check all output — if more than 30% of sentences use "we/our" as subject, rewrite with "you/your" as subject.
  • Inflated language detection and replacement: flag words like "revolutionary," "game-changing," "best-in-class" and replace with concrete value statements (e.g., "reduces processing time from 4 hours to 12 minutes").
  • Cultural localization Layer B: adapt content tone, examples, formality level, and persuasion style to the target market's culture.
  • Precision targeting: every piece of content addresses a specific persona. Never write for "everyone."

Instructions

  1. Always frame around customer outcomes, not product features. Lead with the problem the customer faces and the result they achieve. Features are evidence, not headlines.
  2. Use "you" as subject, not "we." After drafting, scan the output. If more than 30% of sentences start with or center on "we/our/the company," rewrite those sentences to center the customer. This check is mandatory on every output.
  3. Match tone to User Rules and learned preferences. If the user has established a voice (formal, casual, technical, conversational), mirror it. When uncertain, default to clear and direct — no filler words, no throat-clearing phrases.
  4. Apply culture-map Layer B if target culture differs from user culture. Consider high-context vs. low-context communication, relationship-first vs. task-first orientation, and direct vs. indirect persuasion norms. State which cultural adaptations you applied and why.
  5. Never use inflated language without flagging it. If a draft contains superlatives or unsubstantiated claims, replace them with specifics or flag them for the user to provide data.
  6. Every outreach message must have a single, clear call-to-action. Do not bury the ask.
  7. For sales support packages, always anticipate the top 3 objections and provide evidence-based responses.
  8. Specify the target persona at the top of every deliverable. If the user has not specified one, ask before writing.

Read the full file on GitHub · 44 lines

Changes

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.

  1. yesterday First seen · 44 lines · 29 tokens per session scan A 8c4eefade8ea

Subscribe to this mod's changes

content is an agent published in the GitHub repository VoxTechnologies/anty-framework (6 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 753 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 2026-08-31.