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/hoangsonww/AI-News-BriefingWrote 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/plugins/hoangsonww/ai-news-briefing/competitor-intel)<a href="https://agentmods.dev/plugins/hoangsonww/ai-news-briefing/competitor-intel"><img src="https://agentmods.dev/badge/plugins/hoangsonww/ai-news-briefing/competitor-intel.svg" alt="Measured on agentmods" height="20"></a>Grade A, and why
competitor-intel 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 7d 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
{
"name": "competitor-intel",
"description": "Given a company/product, finds its top competitors, compares their latest feature releases, pricing changes, and customer sentiment.",
"version": "1.0.0",
"author": {
"name": "Research Ops",
"url": "https://github.com/hoangsonww"
},
"homepage": "https://github.com/hoangsonww/AI-News-Briefing",
"repository": {
"type": "git",
"url": "https://github.com/hoangsonww/AI-News-Briefing.git"
},
"license": "MIT",
"keywords": ["competitors", "intelligence", "market-research", "business"],
"categories": ["research", "business", "market-analysis"]
}What it installs
The manifest is a name and a version. 1 skill travel with it, and installing the plugin installs all of them — 29 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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.
- 7d ago First seen · 17 lines scan A 305e28a51678
competitor-intel is a plugin published in the GitHub repository hoangsonww/AI-News-Briefing (41 stars, last pushed 6d ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-08-30.
Other plugins, from other repositories
magician
Full-stack SDLC plugin with dynamic project inspection, a local code knowledge-graph + cache, cross-session reference memory, self-learning, parallel agent orchestration, and feature comprehend→port/integrate (/transmute).
forge
Autonomous spec-driven development loop for Claude Code with behavioral guardrails, knowledge graph integration, and design system support.
seeks
Self-running, verifier-gated loops with durable .seeks/ state and native worktrees.
clone-team
Clone any website with a team of AI agents — pixel-perfect UI clone plus reverse-engineered architecture docs. Built on Claude Code's dynamic Workflow engine with an unskippable test gate; fully pausable and resumable.
loop-engineering
Design production-grade, memory-backed agent loops. Classify deterministic vs non-deterministic, scan your harness (skills/agents/MCP/hooks), derive durable state from the goal, separate done-gates from guardrails, and emit a /loop-ready prompt behind a human approval gate.
g-forge
Educated, enforced project management for AI development - a PM layer, parallel implementation waves, and a git-hook commit gate that can't be skipped. Make any model ship like a senior team.