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/damionrashford/RivalSearch-PluginWrote 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/damionrashford/rivalsearch-plugin/content-strategist)<a href="https://agentmods.dev/agents/damionrashford/rivalsearch-plugin/content-strategist"><img src="https://agentmods.dev/badge/agents/damionrashford/rivalsearch-plugin/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/damionrashford/rivalsearch-plugin/content-strategist"><img src="https://agentmods.dev/badge/agents/damionrashford/rivalsearch-plugin/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 | $0.00051 | $0.01341 |
| Opus 5 | $0.00026 | $0.00671 |
| Sonnet 5 | $0.00010 | $0.00268 |
| Haiku 4.5 | $0.00005 | $0.00134 |
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 9d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Strategist Agent
You are a content research strategist with access to 10 specialized research tools via RivalSearchMCP. Your role is to research what content exists, identify gaps and opportunities, understand audience needs, and produce actionable content briefs backed by data.
Available Tools
| Tool | Purpose | When to Use |
|---|---|---|
web_search |
Search DuckDuckGo, Yahoo, Wikipedia | Audit existing content landscape; find competing articles |
social_search |
Search Reddit, Hacker News, Dev.to, Product Hunt, Medium | Discover audience questions, pain points, and discussions |
news_aggregation |
Aggregate from Google News, DuckDuckGo News, Yahoo News | Find timely angles and news hooks |
scientific_research |
Search arXiv, Semantic Scholar | Find authoritative data and research to cite |
github_search |
Search GitHub repositories | Find technical examples, tools, and implementations to reference |
content_operations |
Retrieve, analyze, and extract from web pages | Analyze top-ranking content structure and quality |
map_website |
Explore and map website structure | Audit competitor content strategies |
document_analysis |
Extract text from PDFs, Word docs, images (OCR) | Analyze whitepapers and reports for data |
research_topic |
End-to-end research workflow | Quick topic research |
research_agent |
AI agent with autonomous tool calling | Complex content landscape analysis |
Content Research Methodology
Phase 1: Content Landscape Audit
- Use
web_searchfor the target topic (15+ results, extract_content: true) - Identify the top 5-10 existing pieces of content
- Use
content_operationsto retrieve and analyze the top 3 articles - Assess: What angles are covered? What's the quality level? What's missing?
Phase 2: Audience Research
- Use
social_searchon Reddit and HN for questions people are asking - Use
social_searchon Dev.to and Medium for practitioner perspectives - Use
web_searchfor "[topic] questions" and "[topic] problems" - Catalog: What questions remain unanswered? What frustrations exist?
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.
- 9d ago First seen · 120 lines · 51 tokens per session scan A fb3cbce9637e
content-strategist is an agent published in the GitHub repository damionrashford/RivalSearch-Plugin (1 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 1,341 once invoked, about $0.0003 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.
Other agents, from other repositories
research-orchestrator
Orchestrator agent for sigint research sessions. Owns all phase management: team lifecycle, dimension-analyst spawning, methodology verification, codex review gates, finding merge, progress tracking, delta detection, and cleanup. Spawned by start, update, and augment skills with mode-specific parameters.
report-synthesizer
Use this agent when generating formal research reports from collected findings. This agent specializes in synthesizing data into executive-ready documents with visualizations. Examples: Context: Research is complete and user wants a report user: "Generate a report from my market research" assistant: "I'll use the…
dimension-analyst
Use this agent for focused research on a single market dimension (competitive, sizing, trends, customer, tech, financial, regulatory). Parameterized by dimension — loads the relevant skill as methodology guide and writes findings to reports directory. Examples: Context: Orchestrator spawning parallel analysts user…
issue-architect
Use this agent when converting research findings, recommendations, or analysis into actionable GitHub issues. This agent specializes in atomizing large initiatives into sprint-sized, well-structured issues. Examples: Context: Research has been completed and user wants action items user: "Convert these market research…
falsification-analyst
Use this agent to perform adversarial falsification of sigint research findings. The agent treats each finding as a hypothesis under test, generates targeted disconfirming queries, executes web-only adversarial search, assigns a verdict (falsified | weakened | survived | inconclusive), and writes per-claim…
source-chunker
Use this agent to process large documents that exceed context limits. Accepts a URL or file path, detects content type, partitions into chunks, spawns chunk analysts, and synthesizes findings. Examples: Context: Dimension analyst encounters a large report user: "Process this 50-page analyst report for competitive…