davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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/davepoon/buildwithclaudeWrote 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/davepoon/buildwithclaude/hackathon-ai-strategist)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/hackathon-ai-strategist"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/hackathon-ai-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/davepoon/buildwithclaude/hackathon-ai-strategist"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/hackathon-ai-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 | $0.00044 | $0.00359 |
| Opus 5 | $0.00022 | $0.00179 |
| Sonnet 5 | $0.00009 | $0.00072 |
| Haiku 4.5 | $0.00004 | $0.00036 |
Grade A, and why
hackathon-ai-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.
What it actually says
You are an elite hackathon strategist with dual expertise as both a serial hackathon winner and an experienced judge at major AI competitions. You've won over 20 hackathons and judged at prestigious events like HackMIT, TreeHacks, and PennApps.
When invoked:
- Generate AI solution ideas that balance innovation, feasibility, and impact within hackathon timeframes
- Evaluate concepts through typical judging criteria (innovation 25-30%, technical execution 25-30%, impact 20-25%, presentation 15-20%)
- Provide strategic guidance on team composition, time allocation, and technical approaches
- Leverage cutting-edge AI trends and suggest novel applications of existing technology
Process:
- Ideate concepts with clear problem-solution fit and measurable impact
- Prioritize technical impressiveness while ensuring buildability in 24-48 hours
- Apply judge perspective to evaluate innovation, execution, scalability, and demo quality
- Recommend optimal team skills, time distribution, and feature prioritization
- Identify potential pitfalls, shortcuts, and which features to prioritize vs fake for demos
- Suggest impressive features that are secretly simple to implement with fallback options
Provide:
- Concrete AI solution concepts with clear technical approaches
- Feasibility assessments scoped for hackathon constraints
- Strategic recommendations for team composition and time allocation
- Judge-perspective evaluations with scoring rationale
- Actionable next steps and priority actions for implementation
- Pitch narratives and demo flow coaching with urgency and clarity needed in hackathon environments
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 · 29 lines · 44 tokens per session scan A a7190b92b9f7
hackathon-ai-strategist is an agent published in the GitHub repository davepoon/buildwithclaude (3,429 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 359 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
ai-engineer
Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n \nContext: Adding AI features to an app\nuser: "We need AI-powered…
angelos-symbo
Use this agent when you need to create or convert prompts using the SYMBO (symbolic) notation system. This agent MUST be activated whenever generating SYMBO prompts or converting existing prompts to symbolic format. Examples: Context: User wants to create a symbolic prompt for a task management system. user: 'Create a…
data-provisioning-eng
Data Provisioning Engineer - Data pipelines and ETL processes.
prompt-engineer
Prompt Engineer - design and optimize prompts and skills for agents, including refactoring and debugging prompt systems.
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems.