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Tidewave Integration

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oliver-kriska
tidewave-integration

Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill nametidewave-integration
Stars
555
Forks
40
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Tidewave Integration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/elixir-phoenix/skills/tidewave-integration .claude/skills/tidewave-integration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tidewave Integration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tidewave Integration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tidewave Integration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Tidewave MCP Integration

Runtime intelligence for Phoenix apps via MCP. Prefer Tidewave tools over Bash when available.

Iron Laws — Never Violate These

  1. DEV ONLY — Never use Tidewave tools in production contexts. Avoid on shared dev servers with production data copies
  2. PREFER TIDEWAVE OVER BASHmcp__tidewave__get_docs > web_fetch, execute_sql_query > psql
  3. CHECK AVAILABILITY FIRST — Call Tidewave only when matching mcp__tidewave__* tools are present
  4. SQL IS READ-HEAVY — Use execute_sql_query for SELECT, be careful with mutations
  5. EXACT VERSIONSget_docs returns docs for YOUR mix.lock versions, not latest

Quick Reference

TaskTidewave ToolFallback
Get docsmcp__tidewave__get_docs Module.func/3web_fetch hexdocs.pm/...
Run codemcp__tidewave__project_evalmix run -e "code"
SQL querymcp__tidewave__execute_sql_querypsql $DATABASE_URL
Find sourcemcp__tidewave__get_source_locationgrep -rn "defmodule"
Inspect DOMmcp__Tidewave-Web__browser_evalManual browser inspection
List schemasmcp__tidewave__get_ecto_schemasRead lib/*/schemas/
Read logsmcp__tidewave__get_logs level: :errortail -f log/dev.log

Detection

bash
# Check endpoint
curl -s http://localhost:4000/tidewave/mcp \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"ping"}'

Or use /mcp in Claude Code to see connected servers.

Essential Patterns

Test Function Immediately

elixir
# mcp__tidewave__project_eval
MyApp.Accounts.create_user(%{email: "test@example.com"})

Verify Migration

sql
-- mcp__tidewave__execute_sql_query
SELECT column_name, data_type FROM information_schema.columns
WHERE table_name = 'users';

Debug LiveView (with PID from browser)

elixir
# mcp__tidewave__project_eval
pid = pid("0.1234.0")
:sys.get_state(pid) |> Map.get(:socket) |> Map.get(:assigns) |> Map.keys()

Setup Requirements

elixir
# mix.exs
{:tidewave, "~> 0.6", only: :dev}

# endpoint.ex (in dev block)
plug Tidewave

# config/dev.exs (for LiveView source mapping)
config :phoenix_live_view,
  debug_heex_annotations: true,
  debug_attributes: true

The dependency and endpoint plug expose Tidewave's streamable HTTP server; they do not register it with an MCP client. Configure the current runtime separately with http://localhost:<port>/tidewave/mcp, then verify that Tidewave tools are available before relying on this skill.

Reliability Guards

Worktree/port check (FIRST, in multi-worktree setups): multiple worktrees = multiple dev servers on different ports. Before trusting any Tidewave result, confirm the endpoint belongs to THIS checkout: grep config/dev.exs for the configured port, and verify with project_eval File.cwd!() — if it returns a different worktree path, you're debugging the wrong server.

Schema introspection BEFORE SQL: never guess column names. Run get_ecto_schemas (or query information_schema.columns) before writing SQL against a table you haven't already introspected this session. A guessed-column error costs more than the introspection.

Output-size guard: runtime output is unbounded. Always cap it — LIMIT 20 in SQL, Enum.take(20) in evals, inspect(x, limit: 50, printable_limit: 500) for large structs. Re-query narrower rather than dumping wide.

browser_eval fallback: if mcp__Tidewave-Web__browser_eval is absent or errors, don't stall — inspect the same state server-side: LiveView assigns via :sys.get_state(pid) in project_eval, rendered HTML via Phoenix.LiveViewTest, or read the template source directly.

QA walkthrough pattern: after a feature completes, run a short checklist through project_eval/browser_eval: create the record, fetch it back, exercise the main event, check get_logs level: :error is clean. Report each step's pass/fail — not just "smoke test passed".

Proactive Runtime Checks

Don't just use Tidewave reactively. Query runtime state at workflow checkpoints automatically:

  • After code edits: get_logs level: :error (catch runtime crashes)
  • After features complete: project_eval smoke test (behavioral check)
  • Before planning: get_ecto_schemas + routes eval (concrete context)
  • When investigating: Auto-capture errors before asking user
  • LiveView UI bugs: browser_eval to inspect DOM state before editing components

See ${CLAUDE_SKILL_DIR}/references/proactive-patterns.md for full integration points.

References

For detailed patterns, see:

  • ${CLAUDE_SKILL_DIR}/references/proactive-patterns.md - Push-like runtime patterns at workflow checkpoints
  • ${CLAUDE_SKILL_DIR}/references/tool-examples.md - Complete tool usage examples
  • ${CLAUDE_SKILL_DIR}/references/validation-checklist.md - Runtime validation patterns

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tidewave Integration AI skill do?

Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.

Why use Tidewave Integration on TypingMind?

Because you install it once and use it with any model. Tidewave Integration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tidewave Integration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/elixir-phoenix/skills/tidewave-integration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tidewave Integration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tidewave Integration?

As many as you like. As long as a model supports skills, you can use Tidewave Integration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tidewave Integration AI skill free?

Yes. It is published on GitHub by oliver-kriska under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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