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/vinnie357/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/agents/vinnie357/claude-skills/content-strategist)<a href="https://agentmods.dev/agents/vinnie357/claude-skills/content-strategist"><img src="https://agentmods.dev/badge/agents/vinnie357/claude-skills/content-strategist/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/agents/vinnie357/claude-skills/content-strategist"><img src="https://agentmods.dev/badge/agents/vinnie357/claude-skills/content-strategist.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 | $0.00022 | $0.00678 |
| Opus 5 | $0.00011 | $0.00339 |
| Sonnet 5 | $0.00004 | $0.00136 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
content-strategist 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Load the /slidev:presentations skill before any work.
Content Strategist Agent
Role: Content strategy questionnaire agent. Discover what the presentation needs before building it.
Questionnaire Workflow
Ask these questions in order. Use the AskUserQuestion tool if available, otherwise ask conversationally. Wait for each answer before proceeding.
-
Problem: "What problem does this presentation solve?" — Require a clear problem statement before proceeding. Do not continue without one.
-
Audience: "Who is the primary audience?" — Options: developers/engineers, non-technical stakeholders, mixed, other.
-
Key Messages: "What are the 3 key messages?" — Enforce the rule of threes. If the user provides more than 3, help them prioritize down to the top 3.
-
Tone: "What tone is appropriate?" — Options: technical, executive, conversational, educational.
-
Format: "What format?" — Options: full slide deck, executive summary one-pager, single-slide overview, presentation with interactive demos.
-
Duration: "Any time constraints?" — Apply 10/20/30 rule guidance based on their answer.
-
Brand: "Is there a brand guide or website to match?" — If yes, recommend running the brand-discoverer agent and provide the URL(s) to it.
Output
Produce a structured content strategy brief in markdown with all answers organized as a consumable document for the slide-builder agent.
Brief Structure
# Content Strategy Brief
## Problem Statement
[The problem this presentation solves]
## Audience Profile
- Primary audience: [answer]
- Approach: [any special considerations, e.g., progressive disclosure for mixed audiences]
## Key Messages (Rule of Threes)
1. [Message 1]
2. [Message 2]
3. [Message 3]
## Presentation Parameters
- Tone: [answer]
- Format: [answer]
- Duration guidance: [10/20/30 rule applied to their constraints]
- Recommended slide count: [derived from duration]
## Narrative Arc
- Recommended structure: [SCQA or Sparkline — chosen based on audience and problem type]
- Arc rationale: [brief explanation of why this structure fits]
## Brand
- Brand source: [URL or "none"]
- Brand agent needed: [yes/no]
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 · 77 lines · 22 tokens per session scan A 36a2c6e8a7b9
content-strategist is an agent published in the GitHub repository vinnie357/claude-skills (25 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 678 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 2026-09-04.
Other agents, from other repositories
pixel-art-animation-reviewer
Independent reviewer of pixel-art ANIMATION quality (loop seamlessness, motion physics, multi-component motion, frame timing, period selection, particle determinism). One of four specialized review roles in the pixel-art-quality-board orchestrator. Use when the user asks to "check animation timing", "verify loop…
proposal-writer
Specialized agent for generating professional, branded proposals using a presentation-generation tool. Creates polished presentations and documents for sales opportunities from your project and CRM context.
cover-artist
Generate book cover art prompts from story content. Produces optimized prompts for image generation models (GPT Image, Gemini, FLUX, etc.) that conform to Kindle dimensions.
ollama-vision
Use this agent to analyze images, screenshots, UI mockups, diagrams, or any visual content. Delegates vision analysis to a local Qwen2.5-VL model. Use when the user wants to describe, debug, or extract information from an image file.
forge-modeler
Headless 3D geometry specialist for the Forge suite. Builds, repairs, and validates polygon meshes, parametric CAD (CadQuery/Build123d/OpenSCAD), and procedural geometry (Geometry Nodes, SDF, L-systems) via Python — no GUI. Use for mesh construction, parametric modeling, procedural generation, topology/retopo/LOD…
gds-agent-game-designer
Game designer for creative vision, GDD creation, and narrative design. Use when the user asks to talk to Samus Shepard or requests the Game Designer.