38,263 mods in this category, of every kind an
agent can take. Each one carries what it costs per session, what the
scan found, and whether it is the original.
A routing guide for choosing the right coding-agent skill based on the size, stages, and long-term nature of a task. It can also account for skills the user or workspace has already specified.
This rules file teaches Cursor's AI when to call the four cognitive harness tools exposed by the ejentum-mcp MCP server. Use it when you have ejentum-mcp installed in Cursor's MCP settings and want the agent to fire the right harness automatically.
The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This TypeScript SDK implements the full MCP specification, making it easy to.
Forge a coding or agentic prompt optimized for Claude Opus 4.7 or GPT-5.5 (Codex CLI) by interviewing the user with structured multiple-choice questions, then writing a precise XML-tagged prompt and optional SPEC.md handoff. Use whenever the user asks to write a prompt, scope a coding task, design a system prompt for…
Behavioral guidelines to reduce common LLM coding mistakes. Emphasizes thinking before coding, simplicity, surgical changes, and goal-driven execution. Load this when writing or modifying code to avoid overengineering and unnecessary changes.
Use first when a task may benefit from specialized capability in a large local skill library. Search indexed metadata, select an explainable ranked candidate, then fetch only the exact SHA-256 revision returned by search.
Configure the API key (or other credential) for a registered ACPX agentid (claude/codex/cursor/gemini/qwen/copilot/pi/droid/iflow/kilocode/kimi/kiro/opencode/tlamatini) end-to-end across data.keys, config.json (top-level + acpx.agents. .env), regensecrets.py, and verification via acpdoctor.
Self-improving skills toolkit that watches real agent sessions, detects missed triggers, grades execution quality, and improves skill packages through evals, replay, baselines, review, and post-deploy watch. Use when verifying or publishing a skill, improving instructions or routing, checking skill health, grading…
An AI product planning guide for deciding whether to use retrieval-augmented generation (RAG) or an agent. RAG lets an AI search a knowledge collection before answering, while an agent can choose actions and tools.
Audit and improve the Claude Code context layer — CLAUDE.md guidance files, .claude/rules/ path-scoped rules, and companion codemap.md navigation maps — against Claude 5 context-engineering rules (judgement over rules, progressive disclosure, no cross-layer conflicts). Asks whether to optimize the current repository…
AI Intervention Agent: MCP server enabling real-time user intervention in AI-assisted development workflows. Runs locally from the ai-intervention-agent Python package.
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originalMIT
Install, set up, and manage MCP servers using the mcpmu CLI. Use when the user wants to install mcpmu, register it as an MCP server, add/remove/list MCP servers, manage namespaces, set tool permissions, manage server-level denied tools, or expose servers via serve mode.
Implement features, fix bugs, and write tests through Compound Engineering -- a structured pipeline of Research -> Brainstorm -> Plan -> Design -> Implement -> Scrub -> Test -> Review, with gate-checked phase transitions, full artifact chain consumption, mandatory Karpathy guidelines, and a risk-based quality loop.
A rule-management system for resolving conflicts between instructions. It uses priorities, context, user preferences, and recent use to decide which rules should take precedence, while recording those decisions.
Multi-agent semantic reasoning system with persistent memory for code suggestions, architectural guidance, and optimization recommendations.
★not rated 16
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originalApache-2.0
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: