MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
MITAuto-check: notes
Install Neural Train
$ npx skills add ruvnet/ruflo --skill neural-train -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ruvnet/ruflo neural-train --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ruflo-intelligence/skills/neural-train .claude/skills/neural-train && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "neural-train" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-train into .claude/skills/neural-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neural-train", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-trainType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ruvnet/ruflo --skill neural-train -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ruvnet/ruflo neural-train --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ruflo-intelligence/skills/neural-train .agents/skills/neural-train && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "neural-train" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-train into .agents/skills/neural-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neural-train", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill neural-train -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ruvnet/ruflo neural-train --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ruflo-intelligence/skills/neural-train .cursor/skills/neural-train && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "neural-train" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-train into .cursor/skills/neural-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neural-train", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ruvnet/ruflo.git --path plugins/ruflo-intelligence/skills/neural-train--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ruvnet/ruflo --skill neural-train -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ruvnet/ruflo neural-train --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ruflo-intelligence/skills/neural-train .gemini/skills/neural-train && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "neural-train" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-train into .gemini/skills/neural-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neural-train", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ruvnet/ruflo neural-trainInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ruvnet/ruflo --skill neural-train -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ruflo-intelligence/skills/neural-train .github/skills/neural-train && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "neural-train" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-train into .github/skills/neural-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neural-train", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill neural-train -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ruvnet/ruflo neural-train --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ruflo-intelligence/skills/neural-train .opencode/skills/neural-train && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "neural-train" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-intelligence/skills/neural-train into .opencode/skills/neural-train/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neural-train", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
neural-train- GitHub stars
- 74k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 218 words
- Files
- 1
- Skills in repo
- 264
- Repo updated
- First seen
- Licence
- MIT
At a glance
Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
- Works in 8 steps: Check current neural status —… → Start a trajectory —… → Record steps — for each significant… → …
- SKILL.md covers When to use, Standard flow (DISTILL), SONA adaptation… and MicroLoRA adaptation…, plus 4 more sections
- Calls npx
What it does
Neural Train is an agent skill from ruvnet/ruflo. Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Model Context Protocol. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.
Example prompts
- “/neural-train”
Requirements
- Node.js
- Pre-approved tools (allowed-tools): mcp__plugin_ruflo-core_ruflo__neural_train, mcp__plugin_ruflo-core_ruflo__neural_status, mcp__plugin_ruflo-core_ruflo__neural_patterns, mcp__plugin_ruflo-core_ruflo__neural_predict, mcp__plugin_ruflo-core_ruflo__neural_optimize, mcp__plugin_ruflo-core_ruflo__neural_compress, mcp__plugin_ruflo-core_ruflo__hooks_pretrain, mcp__plugin_ruflo-core_ruflo__hooks_build-agents, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_learn, mcp__plugin_ruflo-core_ruflo__hooks_intelligence-reset, mcp__plugin_ruflo-core_ruflo__ruvllm_sona_create, mcp__plugin_ruflo-core_ruflo__ruvllm_sona_adapt, mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_create, mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt, mcp__plugin_ruflo-core_ruflo__agentdb_consolidate, Bash
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Check current neural status — mcpplugin_ruflo-core_rufloneural_status.
- Start a trajectory — mcpplugin_ruflo-core_ruflohooks_intelligence_trajectory-start with the task context.
- Record steps — for each significant action, mcpplugin_ruflo-core_ruflohooks_intelligence_trajectory-step.
- End trajectory — mcpplugin_ruflo-core_ruflohooks_intelligence_trajectory-end with verdict: pass|fail|partial.
- Learn from the trajectory — mcpplugin_ruflo-core_ruflohooks_intelligence_learn.
- Train patterns — mcpplugin_ruflo-core_rufloneural_train with --pattern-type coordination --epochs 10.
- Store patterns — mcpplugin_ruflo-core_ruflohooks_intelligence_pattern-store.
- Verify — mcpplugin_ruflo-core_rufloneural_patterns to confirm.
What it can do on your machine
Read from SKILL.md and the folder at commit 6051f67. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
mcp__plugin_ruflo-core_ruflo__neural_trainmcp__plugin_ruflo-core_ruflo__neural_statusmcp__plugin_ruflo-core_ruflo__neural_patternsmcp__plugin_ruflo-core_ruflo__neural_predictmcp__plugin_ruflo-core_ruflo__neural_optimizemcp__plugin_ruflo-core_ruflo__neural_compressmcp__plugin_ruflo-core_ruflo__hooks_pretrainmcp__plugin_ruflo-core_ruflo__hooks_build-agentsmcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-startmcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step
…and 10 more on the same allowed-tools line.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
npx
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Neural Train loads about 1.2k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 218 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: mcp__plugin_ruflo-core_ruflo__neural_train, mcp__plugin_ruflo-core_ruflo__neural_status, mcp__plugin
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from ruvnet/ruflo at commit 6051f67, republished under its MIT licence (© ruvnet). 218 words, ~1,168 tokens.
Download SKILL.mdSave it as
.claude/skills/neural-train/SKILL.md (or your agent's skills folder).- name
- neural-train
- description
- Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
- allowed-tools
- mcp__plugin_ruflo-core_ruflo__neural_train, mcp__plugin_ruflo-core_ruflo__neural_status, mcp__plugin_ruflo-core_ruflo__neural_patterns, mcp__plugin_ruflo-core_ruflo__neural_predict, mcp__plugin_ruflo-core_ruflo__neural_optimize, mcp__plugin_ruflo-core_ruflo__neural_compress, mcp__plugin_ruflo-core_ruflo__hooks_pretrain, mcp__plugin_ruflo-core_ruflo__hooks_build-agents, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_learn, mcp__plugin_ruflo-core_ruflo__hooks_intelligence-reset, mcp__plugin_ruflo-core_ruflo__ruvllm_sona_create, mcp__plugin_ruflo-core_ruflo__ruvllm_sona_adapt, mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_create, mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt, mcp__plugin_ruflo-core_ruflo__agentdb_consolidate, Bash
- argument-hint
- [--pattern-type coordination|edit|task] [--epochs N] [--microlora]
Neural Training
Train and consolidate neural patterns. Implements the DISTILL and CONSOLIDATE phases of the 4-step intelligence pipeline.
When to use
- After completing a successful task — capture what worked.
- After accumulating ≥10 task completions — run consolidation to fold patterns into long-term storage.
- When training a new domain — create a MicroLoRA adapter for it.
Standard flow (DISTILL)
- Check current neural status —
mcp__plugin_ruflo-core_ruflo__neural_status. - Start a trajectory —
mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-startwith the task context. - Record steps — for each significant action,
mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step. - End trajectory —
mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-endwithverdict: pass|fail|partial. - Learn from the trajectory —
mcp__plugin_ruflo-core_ruflo__hooks_intelligence_learn. - Train patterns —
mcp__plugin_ruflo-core_ruflo__neural_trainwith--pattern-type coordination --epochs 10. - Store patterns —
mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store. - Verify —
mcp__plugin_ruflo-core_ruflo__neural_patternsto confirm.
SONA adaptation (single-domain, <0.05ms)
For real-time micro-adaptation:
mcp tool call ruvllm_sona_create --json -- '{"domain": "coding"}'
mcp tool call ruvllm_sona_adapt --json -- '{"feedback": {"score": 0.9, "trajectory": "..."}}'MicroLoRA adaptation (multi-domain)
When you have ≥3 distinct domains, create a MicroLoRA adapter per domain rather than overloading SONA:
# Create the adapter
mcp tool call ruvllm_microlora_create --json -- '{"domain": "frontend"}'
# Adapt with feedback
mcp tool call ruvllm_microlora_adapt --json -- '{"adapter": "frontend", "feedback": {...}}'
# CONSOLIDATE phase: apply EWC++ on weight deltas to prevent catastrophic forgetting
mcp tool call ruvllm_microlora_adapt --json -- '{"adapter": "frontend", "consolidate": true}'The --consolidate flag is the EWC++ trigger. Without it, fresh training overwrites older domains.
CONSOLIDATE phase (separate from training)
After every ~10 trajectory completions, run a full consolidation pass:
mcp tool call agentdb_consolidate --json
mcp tool call neural_compress --json # storage efficiencyThis folds patterns into long-term storage under EWC++ semantics.
Bootstrapping from scratch
If the system has no learned patterns yet:
mcp tool call hooks_pretrain --json -- '{"modelType": "moe", "epochs": 10}'
mcp tool call hooks_build-agents --json -- '{"agentTypes": "coder,tester"}'hooks_pretrain writes to the patterns (plural) namespace — distinct from the pattern (singular) ReasoningBank target. See ruflo-agentdb ADR-0001 for the namespace convention.
Reset (testing only)
To wipe intelligence state (e.g., for benchmarking):
mcp tool call hooks_intelligence-reset --jsonCLI alternatives
npx @claude-flow/cli@latest neural train --pattern-type coordination --epochs 10
npx @claude-flow/cli@latest neural patterns --list
npx @claude-flow/cli@latest neural status
npx @claude-flow/cli@latest neural compress
npx @claude-flow/cli@latest hooks pretrain --model-type moe --epochs 10
npx @claude-flow/cli@latest hooks build-agents --agent-types coder,tester© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in plugins/ruflo-intelligence/skills/neural-train of ruvnet/ruflo.
Open the folder on GitHubat commit 6051f67
Compare with similar skills
Neural Train next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Neural Train this skillruvnet/ruflo | 74k | — | ~1.2k | Automated safety check: Notes | MIT | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Figma use_figma Plugin API Ruleswarpdotdev/warp | 65k | 4 repos | ~4.4k | Automated safety check: Pass | AGPL-3.0 | |
| Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills | 8.4k | 6 repos | ~3.2k | Automated safety check: Notes | Apache-2.0 |
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Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
Figma use_figma Plugin API Rules
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MCP Development
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More from ruvnet/ruflo
All 264 skills in this repoAgentDB Pattern Memory
ruvnet/ruflo
Stores, searches, and retrieves successful patterns with HNSW-indexed semantic search so agents can reuse past solutions instead of relearning them.
Security Audit Scanner
ruvnet/ruflo
Runs claude-flow CLI security scans for input validation, path traversal, SQL injection, XSS, hardcoded secrets and known CVEs, and writes an audit report.
SPARC Development Methodology
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Applies the SPARC method (specification, pseudocode, architecture, refinement, completion) with 17 specialized modes and multi-agent orchestration, from research to deployment.
Swarm Orchestration
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Coordinates a hierarchical swarm of specialized agents through the claude-flow CLI for work that spans several files or modules at once.
Ruflo Multi-Agent Orchestration
ruvnet/ruflo
Sets up and drives Ruflo, an npm-installed orchestration layer for multi-agent swarms, persistent memory, routing, hooks and its MCP tool catalog.
Agent Coordination
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Reference for spawning, listing, monitoring and stopping agents with claude-flow commands, with agent type families, routing codes and coordination tips.
Related
Works with
Questions about Neural Train
What does Neural Train do?
Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline. Neural Train is an agent skill from ruvnet/ruflo.
How do I install Neural Train in Claude Code?
Run `npx skills add ruvnet/ruflo --skill neural-train -a claude-code`. Or copy the skill folder (plugins/ruflo-intelligence/skills/neural-train in ruvnet/ruflo) into .claude/skills/neural-train in your project. Claude Code loads it when a task matches its description.
How do I install Neural Train in Codex?
Run `npx skills add ruvnet/ruflo --skill neural-train -a codex`. Or copy the skill folder (plugins/ruflo-intelligence/skills/neural-train in ruvnet/ruflo) into .agents/skills/neural-train in your project. Codex loads it when a task matches its description.
Can I use Neural Train in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill neural-train -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/neural-train, .gemini/skills/neural-train, .github/skills/neural-train and .opencode/skills/neural-train in your project.
What does Neural Train need to run?
Going by SKILL.md and its folder, Neural Train needs the command-line tools its instructions call (npx). Our summary lists: Node.js. Its frontmatter pre-approves these tools: mcp__plugin_ruflo-core_ruflo__neural_train, mcp__plugin_ruflo-core_ruflo__neural_status, mcp__plugin_ruflo-core_ruflo__neural_patterns, mcp__plugin_ruflo-core_ruflo__neural_predict, mcp__plugin_ruflo-core_ruflo__neural_optimize, mcp__plugin_ruflo-core_ruflo__neural_compress, mcp__plugin_ruflo-core_ruflo__hooks_pretrain, mcp__plugin_ruflo-core_ruflo__hooks_build-agents, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store, mcp__plugin_ruflo-core_ruflo__hooks_intelligence_learn, mcp__plugin_ruflo-core_ruflo__hooks_intelligence-reset, mcp__plugin_ruflo-core_ruflo__ruvllm_sona_create, mcp__plugin_ruflo-core_ruflo__ruvllm_sona_adapt, mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_create, mcp__plugin_ruflo-core_ruflo__ruvllm_microlora_adapt, mcp__plugin_ruflo-core_ruflo__agentdb_consolidate, Bash.
Does Neural Train access the network?
SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Neural Train safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Neural Train use?
Neural Train is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Neural Train use?
About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Neural Train?
Skills that share tags, products or a category with Neural Train: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Neural Train?
ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,089 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 8, 2026.
Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.