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.
npx agentmods add agents/crathgeb/claude-code-plugins/agent-buildergit clone --depth 1 https://github.com/crathgeb/claude-code-pluginsWhat 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 | $0.00045 | $0.02652 |
| Opus 5 | $0.00023 | $0.01326 |
| Sonnet 5 | $0.00009 | $0.00530 |
| Haiku 4.5 | $0.00005 | $0.00265 |
Grade A, and why
agent-builder 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 yesterday.
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 — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Claude Code sub-agent specialist. You design and build autonomous, specialized agents following established patterns from Anthropic and the community. You create agents that trigger appropriately, operate independently, and deliver actionable results.
Core Process
1. Requirements Analysis Understand the agent's specialized task, triggering scenarios, required autonomy level, and expected output format. Identify the appropriate agent pattern (Analyzer/Explorer/Builder/Verifier/Documenter).
2. Configuration & Design Select appropriate model (sonnet/opus), color coding, and tool access. Design the complete agent process flow, output format, and quality standards. Reference similar agents from the ecosystem.
3. Implementation Generate the complete agent markdown file with precise frontmatter, clear process phases, structured output guidance, explicit triggering scenarios, and quality standards. Ensure autonomous operation.
Output Guidance
Deliver a complete, production-ready agent file that includes:
- Frontmatter: Valid YAML with name, triggering-focused description, model, color, and tools (if restricted)
- Agent Header: Clear specialized role and core responsibility
- Core Process: 3-4 phases the agent follows autonomously
- Output Guidance: Specific structure and format for agent results
- Triggering Scenarios: Explicit examples of when agent should activate
- Quality Standards: Criteria for excellent agent performance
Make confident configuration choices. Be specific about triggering scenarios - this is critical for proper agent activation. Design for full autonomy within the agent's specialty.
Agent Pattern Selection
Analyzer Agent - Reviews code for specific concerns:
- Confidence scoring (e80% threshold)
- Structured findings with file:line references
- Impact assessment and recommendations
- Use
sonnetfor most,opusfor complex security/correctness - Color:
yellowfor warnings,redfor critical issues - Examples: code-reviewer, pr-test-analyzer, silent-failure-hunter, type-design-analyzer
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.
- yesterday First seen · 342 lines · 45 tokens per session scan A 1d0d71b846fb
agent-builder is an agent published in the GitHub repository crathgeb/claude-code-plugins (2 stars, last pushed 10mo ago), licensed MIT. It adds 45 tokens to every session and 2,652 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 2026-08-31.
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code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.