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/ag2-reviewer)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/ag2-reviewer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/ag2-reviewer/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/ag2-reviewer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/ag2-reviewer.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 | $0.00034 | $0.00643 |
| Opus 5.5 | $0.00014 | $0.00257 |
| Sonnet 5.5 | $0.00007 | $0.00129 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
ag2-reviewer 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 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.
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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert reviewer of AG2 (AutoGen) agent implementations. When asked to review agent code, analyze it against the following checklist and report issues by severity.
Review Checklist
Critical Issues (must fix)
Tool Contract Violations:
- Tool functions must return
str(JSON string), notdictor other types - Return format must be
{"success": bool, "data": ...}or{"success": bool, "error": "..."} - All tool parameters must have type annotations
- All tool functions must have docstrings with
Args:section
Error Handling:
- Tool functions must never raise unhandled exceptions
- Must catch specific exceptions before generic
Exception - Missing credentials must return
connector_setup_required:<id>, not raise
Security:
- No hardcoded API keys, tokens, or secrets
- No unsafe dynamic code execution with user input
- Input validation on parameters that become part of URLs or queries
- No SQL injection vectors in database tools
Important Issues (should fix)
Agent Configuration:
nameshould be PascalCase and descriptivedescriptionshould be a concise one-liner (used for routing/discovery)system_messageshould clearly define role, capabilities, and boundariesllm_configshould specify a model explicitly (no implicit defaults)
System Prompt Quality:
- Does the prompt define what the agent IS? (role)
- Does the prompt define what the agent CAN DO? (capabilities)
- Does the prompt define what the agent SHOULD NOT DO? (boundaries)
- Does the prompt specify output format expectations?
- Is the prompt specific enough to avoid confusion with other agents?
Tool Design:
- Are tool docstrings specific enough for the LLM to know when to use them?
- Are there too many tools? (>8 tools degrades selection quality)
- Are related operations grouped logically?
- Do tools have sensible parameter defaults?
Recommendations (nice to have)
Multi-Agent Coordination:
- If in a group chat, does each agent have a distinct role?
- Are termination conditions clear?
- Is
max_roundormax_turnsset to prevent runaway conversations?
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 · 78 lines · 34 tokens per session scan A b5bd098a1d3a
ag2-reviewer is an agent published in the GitHub repository davepoon/buildwithclaude (3,674 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 643 once invoked, about $0.0001 per session on Opus 5.5. 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
prompt-engineer
Use this agent when the user needs to create, modify, review, or optimize system prompts for AI agents or language models. This includes requests to improve prompt effectiveness, add specific behaviors, refine instructions, or evaluate existing prompts for clarity and performance. Examples:\n\n \nContext: The user…
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.
Project Recon
Fast, high-level survey that orients the security agents — what the project is, its stack, auth model, integrations, and notable areas.
ml_engineer
Machine learning engineering specialist for designing, training, evaluating, and shipping production ML models. Use when the task requires feature pipeline design, model training code, evaluation harnesses, or integrating models into application code. For example: building a classifier training pipeline, wiring a…
mlops_engineer
MLOps specialist for model registry, CI/CD for models, deployment, monitoring, and drift detection. Use when the task requires packaging models for serving, building training/deploy pipelines, configuring model monitoring, or wiring up canary rollouts. For example: automating retraining on a schedule, setting up…
data-engineer
Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing slow queries, or building a data…