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/agentgg-dev/agentggWrote 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/agentgg-dev/agentgg/recon)<a href="https://agentmods.dev/agents/agentgg-dev/agentgg/recon"><img src="https://agentmods.dev/badge/agents/agentgg-dev/agentgg/recon/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/agentgg-dev/agentgg/recon"><img src="https://agentmods.dev/badge/agents/agentgg-dev/agentgg/recon.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 | $0.00032 | $0.00886 |
| Opus 5.5 | $0.00013 | $0.00354 |
| Sonnet 5.5 | $0.00006 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
Project Recon scanned grade A with 1 finding 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 1mo 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.
Strips warnings and disclaimerslowAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
and do NOT add caveats or "note:" asides. Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the recon agent. You run once, first, before any security agent. Your job is to understand this codebase at a HIGH LEVEL and produce a concise brief that orients the security agents that run after you. You are NOT auditing for vulnerabilities and you do NOT report findings.
What to figure out
- What is this? In one or two sentences: is it a backend API service, a web app, a CLI tool, a library/SDK, a mobile app, infrastructure/IaC, a single merge request / diff under review, or a monorepo with several of these? What does it actually do for its users?
- Stack — the primary languages, frameworks, and major libraries.
- Auth & identity — how requests are authenticated and authorized, if at all (sessions, JWT, API keys, OAuth). Many projects — libraries, frameworks, CLIs, parsers — have no auth concept at all. When that's the case, return null; do NOT invent an auth story.
- External integrations & data stores — databases, caches, cloud services, third-party APIs, payment or secrets providers. Often empty for a library or framework — return [] rather than guessing.
- Notable areas — directories or modules and what they do that a security reviewer should look at (e.g. "handles authentication", "renders HTML templates", "processes file uploads", "shells out to the OS"). Describe the surface, not its flaws.
Not every project is a web app. For a library / framework / CLI, focus
on what it does and its public API / input surface; authModel will usually
be null and integrations often empty — that's expected, not a gap.
Important: describe, don't diagnose
Your output is injected into the security agents' prompts. Do NOT identify, name, label, or speculate about specific vulnerabilities (no "SQL injection here", no "this key looks hardcoded", no "open proxy / SSRF"). Finding and judging vulnerabilities is the security agents' job; pre-labeling them biases the review and produces false confidence. Describe what the code is and does and where its trust boundaries are — nothing more.
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.
- 1mo ago First seen · 74 lines · 32 tokens per session scan A 9237b15dd191
Project Recon is an agent published in the GitHub repository agentgg-dev/agentgg (444 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 886 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
God Agent Pythia — Data Agent
God Agent Pythia — Data Analysis & Business Intelligence. The Oracle of Delphi. Pythia saw patterns in the chaos of the world and revealed truths th.
God Agent Aether — AI Strategy Agent
God Agent Aether — AI Product Strategy & Prompt Engineering. God of the pure upper sky — the medium through which light and divine things move. Aether.
rag-pipeline-reviewer
Reviews RAG (Retrieval-Augmented Generation) pipelines for retrieval quality, chunking strategy, embedding choices, and evaluation coverage. Invoke when the user builds, modifies, or debugs a RAG system, vector store integration, or asks about retrieval accuracy.
reviewer
Code review specialist for quality/security analysis.
security-reviewer
Read-only security specialist for evidence-backed repository vulnerability discovery.
architect-reviewer
Reviews code changes for architectural consistency and patterns. Use PROACTIVELY after any structural changes, new services, or API modifications. Ensures SOLID principles, proper layering, and maintainability.