Mx Space Remote Translation Audit logo

Mx Space Remote Translation Audit

Community
Innei
mx-space-remote-translation-audit

Audit remote mx-space translation data through ssh to the swarm host, then docker exec into the Postgres container and run psql inside the container. Use for checking translation_entries coverage, ai_translations gaps, strict computeContentHash mismatches, and runtime freshness semantics in deployments where direct Postgres access is unreliable.

Overview

PublisherInnei
RepositorySKILL
Skill namemx-space-remote-translation-audit
Stars
81
Forks
2
Bundled files
3
Links
Skill sourceRepository
  • 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 Innei on GitHub. Read the source before you install it.

Installation

Install the Mx Space Remote Translation Audit 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/Innei/SKILL.git /tmp/SKILL
mkdir -p .claude/skills
cp -r /tmp/SKILL/skills/infrastructure/mx-space-remote-translation-audit .claude/skills/mx-space-remote-translation-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mx Space Remote Translation Audit 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 Mx Space Remote Translation Audit 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 Mx Space Remote Translation Audit 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.

mx-space Remote Translation Audit

Use this skill when validating remote mx-space translation state in translation_entries or ai_translations — coverage, freshness, hash drift, or route-level behavior.

This deployment migrated MongoDB → PostgreSQL. Older notes referencing mongosh, refType/sourceModified, or other camelCase fields are stale.

For connection mechanics see the sibling skill mx-space-remote-db-access: mx_core database, mx role, mx-space-pg-* container, container env auto-sets credentials.

Critical semantics (must not collapse)

strict hash mismatch  !=  runtime stale
StatusMeaning
missingno translation row exists for the requested language
strict hash mismatchtranslation.hash !== computeContentHash(current source)
runtime validsource_modified_at >= article.modified_at, or created_at >= article.modified_at when source_modified_at is absent, or hash matches
runtime staleruntime freshness check explicitly concludes the translation is stale
unknownsnapshot lacks enough source fields to compute a hash

Always state which conclusion the result represents — strict-hash or runtime — they can disagree, and a runtime-valid translation may legitimately ship with a drifted hash.

Table of Contents

TopicReference
ai_translations and translation_entries column reference (post-migration field names)references/schema.md
Audit workflow: scope → coverage → semantics → user-visible verificationreferences/audit-workflow.md
Reproducing computeContentHash and the list-translation bug patternreferences/hash-semantics.md

Preferred audit script

When the local mx-core repo is available, prefer the built-in script:

bash
cd /Users/innei/git/innei-repo/mx-core
pnpm check:ai-translation-hash --help

The flag set may have changed post-migration (Postgres URI rather than Mongo URI). Read --help first; fall back to manual SQL if the script is absent or pre-migration.

Output sections: missing, runtimeStale, strictHashMismatch, taskPayloads (the regenerate set = missing + runtimeStale).

API verification

When the user reports a page still shows untranslated content, hit the route inside the core container instead of inferring from DB state:

bash
ssh -p "$SSH_PORT" "$SSH_USER@$SSH_HOST" \
  "docker exec '$CORE_CONTAINER' curl -sS 'http://127.0.0.1:2333/api/v2/posts/<category>/<slug>?lang=en'"

If the database has the row but the route still returns source language, the bug is on the read path — see references/hash-semantics.md for the known list-route failure pattern.

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 Mx Space Remote Translation Audit AI skill do?

Audit remote mx-space translation data through ssh to the swarm host, then docker exec into the Postgres container and run psql inside the container. Use for checking translation_entries coverage, ai_translations gaps, strict computeContentHash mismatches, and runtime freshness semantics in deployments where direct Postgres access is unreliable.

Why use Mx Space Remote Translation Audit on TypingMind?

Because you install it once and use it with any model. Mx Space Remote Translation Audit 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 Mx Space Remote Translation Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Innei/SKILL/tree/main/skills/infrastructure/mx-space-remote-translation-audit. 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 Mx Space Remote Translation Audit?

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 Mx Space Remote Translation Audit?

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

Is the Mx Space Remote Translation Audit AI skill free?

It is published on GitHub by Innei. Check the repository for licensing terms. 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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