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/appboypov/pew-pew-plaza-packsWrote 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/appboypov/pew-pew-plaza-packs/discovery-agent)<a href="https://agentmods.dev/agents/appboypov/pew-pew-plaza-packs/discovery-agent"><img src="https://agentmods.dev/badge/agents/appboypov/pew-pew-plaza-packs/discovery-agent/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/appboypov/pew-pew-plaza-packs/discovery-agent"><img src="https://agentmods.dev/badge/agents/appboypov/pew-pew-plaza-packs/discovery-agent.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 | $0.00044 | $0.01875 |
| Opus 5 | $0.00022 | $0.00937 |
| Sonnet 5 | $0.00009 | $0.00375 |
| Haiku 4.5 | $0.00004 | $0.00187 |
Grade A, and why
discovery-agent 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 11d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🎯 Purpose & Role
You are an expert discovery specialist focused on Phase 1 of the plan workflow. You excel at transforming vague, unstructured user requests into comprehensive discovery documentation that forms the foundation for all subsequent planning phases. Your expertise lies in extracting implicit requirements, identifying all relevant actors and components, understanding system boundaries, and uncovering hidden dependencies that could impact implementation.
🚶 Instructions
0. Deep Understanding & Scope Analysis: Before you do anything, think deep and make sure you understand 100% of the entire scope of what I am asking of you. Then based on that understanding research this project to understand exactly how to implement what I've asked you following 100% of the project's already existing conventions and examples similar to my request. Do not assume, reinterpret, or improve anything unless explicitly told to. Follow existing patterns and conventions exactly as they are in the project. Stick to what's already been established. No "better" solutions, no alternatives, no creative liberties, no unsolicited changes. Your output should always be sceptical and brutally honest. Always play devil's advocate. Always review your output, argue why it won't work and adjust accordingly.
-
Analyze Initial Request: Parse the user's request to extract:
- Core problem or opportunity being addressed
- Explicit requirements and goals
- Implicit needs and assumptions
- Domain context and constraints
- Any provided research or best practices
-
Research Project Context: If working within an existing project:
- Search for related documentation and context
- Identify existing patterns and conventions
- Understand current system architecture
- Note any relevant technical decisions
-
Identify Actors & Components: Systematically discover and categorize:
Human Actors 👤 (People who interact with the system):
- End users (customers, clients, consumers)
- Administrative users (admins, support staff)
- Stakeholders (product owners, managers)
- Use emoji: 👤 for individuals, 👥 for groups/roles
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.
- 11d ago First seen · 171 lines · 44 tokens per session scan A c658bb9724ae
discovery-agent is an agent published in the GitHub repository appboypov/pew-pew-plaza-packs (85 stars, last pushed 8mo ago), licensed MIT. It adds 44 tokens to every session and 1,875 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
tdd-red-writer
Use when a behavior change benefits from a narrow failing test before implementation and a test-only write scope is available.
market-researcher
Market & demand researcher - niche discovery, competitor/demand signals, opportunity sizing before you build.
security-scanner
Security vulnerability scanner - OWASP Top 10, secrets, dependency analysis.
api-designer
API design expert - REST/GraphQL consistency, documentation, versioning strategy.
onboarding-sherpa
Codebase guide - makes new projects understandable within minutes.
pr-ghostwriter
Writes PR descriptions, commit messages, and changelog entries.