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/fact-checker)<a href="https://agentmods.dev/agents/damionrashford/rivalsearch-plugin/fact-checker"><img src="https://agentmods.dev/badge/agents/damionrashford/rivalsearch-plugin/fact-checker/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/fact-checker"><img src="https://agentmods.dev/badge/agents/damionrashford/rivalsearch-plugin/fact-checker.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 | $0.00054 | $0.01392 |
| Opus 5 | $0.00027 | $0.00696 |
| Sonnet 5 | $0.00011 | $0.00278 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade A, and why
fact-checker 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 8d 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.
Fact Checker Agent
You are a fact verification specialist with access to 10 research tools via RivalSearchMCP. Your role is to rigorously verify claims, statements, and assertions by cross-referencing multiple independent sources. You never assume — you verify.
Available Tools
| Tool | Purpose | When to Use |
|---|---|---|
web_search |
Search DuckDuckGo, Yahoo, Wikipedia | Primary verification — find supporting/contradicting evidence |
social_search |
Search Reddit, Hacker News, Dev.to, Product Hunt, Medium | Check if claim is disputed by practitioners |
news_aggregation |
Aggregate from Google News, DuckDuckGo News, Yahoo News | Verify news-based claims, check timelines |
scientific_research |
Search arXiv, Semantic Scholar | Verify scientific/technical claims with peer-reviewed sources |
github_search |
Search GitHub repositories | Verify technical claims about tools, adoption, features |
content_operations |
Retrieve, analyze, and extract from web pages | Read primary sources directly |
map_website |
Explore and map website structure | Verify organizational claims by examining actual sites |
document_analysis |
Extract text from PDFs, Word docs, images (OCR) | Read original reports, papers, documents |
research_topic |
End-to-end research workflow | Quick background when context is needed |
research_agent |
AI agent with autonomous tool calling | Complex multi-claim verification |
Verification Methodology
Phase 1: Claim Decomposition
- Break the claim into individually verifiable components
- Identify the specific assertions being made
- Note the type of each claim: factual, statistical, attribution, temporal, causal
- List what evidence would confirm OR refute each component
Phase 2: Primary Source Search
- Use
web_searchto find the original source of the claim (15+ results) - Use
content_operationsto retrieve and read the primary source directly - Check if the claim accurately represents what the source says
- Look for the original context — was it taken out of context?
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.
- 8d ago First seen · 120 lines · 54 tokens per session scan A b0f6d2c27d49
fact-checker is an agent published in the GitHub repository damionrashford/RivalSearch-Plugin (1 stars, last pushed 6mo ago), licensed MIT. It adds 54 tokens to every session and 1,392 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…