AI Coding Agents · repowise-dev

claude-code-prompts

A collection of independently authored prompt templates for building AI coding agents, inspired by Claude Code's architecture. Covers system prompts, tool routing, agent delegation, memory management, and multi-agent coordination patterns.

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Source: GitHub — github.com/repowise-dev/claude-code-prompts

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MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	repowise-dev/claude-code-prompts
Owner	repowise-dev
Primary language	Unknown
License	MIT — OSI-approved
Stars	1.1k
Forks	386
Open issues	1
Latest release	Unknown
Last updated	2026-05-11
Source	https://github.com/repowise-dev/claude-code-prompts

What claude-code-prompts is

Open-source prompt engineering library providing layered system prompts, specialized tool handlers (shell, file I/O, web, search), subagent delegation patterns, memory compression strategies, and multi-agent coordinator templates for LLM-based coding assistants.

Quickstart

Get the claude-code-prompts source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/repowise-dev/claude-code-prompts.git2cd claude-code-prompts3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Building custom AI coding agents with Claude API

Organizations implementing their own coding agents can use these prompt templates as battle-tested patterns for safety rules, tool routing, and behavioral constraints without reinventing prompt architecture.

Cursor IDE skill development and extension

The repo includes drop-in Cursor skills for coding standards, verification workflows, and prompt design, enabling rapid integration into existing Cursor workflows with minimal customization.

Multi-agent system design and orchestration

Teams building multi-worker LLM systems can reference the coordinator pattern, memory management templates, and delegation strategies to structure complex agent workflows reliably.

Implementation considerations

Prompts use {{PLACEHOLDER}} notation; your team must replace with actual tool names, risk policies, code style guides, and codebase context before deployment.
Each prompt assumes specific LLM capabilities (Claude API assumed); behavior and safety guarantees may differ with other models—requires re-evaluation and testing.
Memory management prompts (summarization, session notes) require external storage and session lifecycle management; no built-in state persistence provided.
Tool-specific prompts assume integration with shell execution, file I/O, grep, web fetch—you must adapt constraints and safety rules to your actual tooling.
Multi-agent coordinator pattern requires a task dispatch mechanism and subagent lifecycle manager; this repo provides the prompt structure only.

When to avoid it — and what to weigh

Seeking production-hardened, vendor-supported agent frameworks — This is a reference implementation and prompt collection, not a maintained framework. Production use requires integration work, testing, and ongoing maintenance by your team.
Need guaranteed security audits or compliance certification — Prompts are community-authored and not formally security-reviewed. Organizations with strict security/compliance requirements should conduct their own audits before using in regulated environments.
Looking for real-time benchmarks or performance metrics — No performance data, latency benchmarks, or comparative eval results are provided. Quality depends entirely on your LLM choice, codebase context, and implementation details.
Expecting automatic integration with your existing tech stack — These are text prompts requiring manual integration into your agent framework, API client, or IDE. No pre-built SDKs, CI/CD connectors, or turnkey deployments provided.

License & commercial use

MIT License. Permits commercial use, modification, and distribution with attribution. No warranties provided.

MIT is a permissive OSI license allowing commercial use. However, these are independently authored prompt templates, not Anthropic-affiliated or endorsed. Validate that your use case does not violate Anthropic Claude API terms. The repo includes a DISCLAIMER noting it is not affiliated with Anthropic. Requires your own legal review before production deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

Prompts define safety rules (reversibility tiers, destructive action gates, anti-over-engineering constraints) but are not formally audited. Safety depends on LLM instruction-following, tool access controls you implement, and your override/approval workflows. No automated threat modeling or penetration testing data provided. Memory management prompts compress context—validate that sensitive information is not leaked during summarization. Web fetch prompts include citation tracking but do not encrypt or isolate external content.

Alternatives to consider

Anthropic Claude Code (SaaS)

Direct use of Claude Code inside VS Code avoids prompt engineering; managed by Anthropic. Trade-off: less customization, vendor lock-in, closed-source patterns.

OpenAI Assistants API + custom prompts

Alternative LLM provider for coding agents with similar prompt-driven architecture. Requires reimplementing patterns for OpenAI model behavior and constraints.

LangChain / LlamaIndex agent frameworks

Higher-level Python/JS libraries abstracting prompt management, memory, and tool routing. Trade-off: less control over prompt structure, additional dependencies, different mental model.

Software development agency

Build on claude-code-prompts with DEV.co software developers

Clone the repo, review the pattern analyses, customize prompts for your stack, and integrate with your agent framework. Read DISCLAIMER.md before production use.

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Surfaced by semantic similarity across the DEV.co open-source index.

agents pentest-ai-agents Claude-Code-Everything-You-Need-to-Know

Related on DEV.co

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claude-code-prompts FAQ

Is this affiliated with Anthropic?

No. The README and DISCLAIMER state the prompts are independently authored, inspired by observing Claude Code patterns. Not endorsed by Anthropic. Check DISCLAIMER.md for full statement.

Can I use these prompts with non-Claude models (e.g., GPT-4, Llama)?

Technically yes, but with caveats. Prompts assume Claude API capabilities and instruction-following style. Other models may not follow safety rules or tool routing as intended. Requires testing and adaptation.

What's the relationship between claude-code-prompts and RepoWise?

Complementary projects from the same team. claude-code-prompts provides agent behavior templates; RepoWise provides codebase intelligence (indexing, dependency graphs, MCP tools) that agents can query for context.

Do these prompts come with any guarantees or SLA?

No. MIT license includes no warranties. Quality depends on your LLM, implementation, and testing. This is reference material; production use requires your own validation and support.

Work with a software development agency

DEV.co helps companies turn open-source tools like claude-code-prompts into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai coding agents stack.

Start building AI coding agents with production-tested prompt patterns.

Clone the repo, review the pattern analyses, customize prompts for your stack, and integrate with your agent framework. Read DISCLAIMER.md before production use.

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