Ring:Mapping Service Resources logo

Ring:Mapping Service Resources

Organization
LerianStudio
ring:mapping-service-resources

Mapping a Go service's Service -> Module -> Resource hierarchy for dispatch-layer registration: detects modules and per-module PostgreSQL/MongoDB/RabbitMQ resources, database names, and shared databases, generates MongoDB index migration pairs (.up.json/.down.json), detects existing Postgres migrations, emits an HTML report, and offers opt-in S3 upload. Use before ring:adding-multi-tenancy on a new service. Skip for non-Go projects.

Overview

PublisherLerianStudio
Repositoryring
Skill namering:mapping-service-resources
Stars
215
Forks
28
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

    Published by LerianStudio on GitHub. Read the source before you install it.

Installation

Install the Ring:Mapping Service Resources 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/LerianStudio/ring.git /tmp/ring
mkdir -p .claude/skills
cp -r /tmp/ring/dev-team/skills/mapping-service-resources .claude/skills/lerianstudio-ring-mapping-service-resources
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Mapping Service Resources 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 Ring:Mapping Service Resources 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 Ring:Mapping Service Resources 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.

Service Discovery

When to use

  • User wants to know what to provision in dispatch layer for a service
  • User asks "what services/modules/resources does this project have?"
  • Before running ring:adding-multi-tenancy on a new service
  • User asks about MongoDB indexes in a project

Skip when

  • Not a Go project
  • Task does not involve service discovery, dispatch layer, or resource mapping
  • Project has no external dependencies

Related

Complementary: ring:adding-multi-tenancy, ring:implementing-tasks

Prerequisites

  • Go project with go.mod in the current working directory

Scans Go project to produce dispatch layer registration data. Orchestrator executes all detection phases directly.

Phase 1: Service Detection

bash
# Service name
grep "ApplicationName\|ServiceName" internal/bootstrap/config.go 2>/dev/null | head -5
cat .env.example 2>/dev/null | grep -i "APPLICATION_NAME\|SERVICE_NAME" | head -3

# Service type
test -f go.mod && cat go.mod | head -3  # module path hints service purpose
ls internal/adapters/ 2>/dev/null       # adapters reveal type

# Unified service check
ls components/ 2>/dev/null              # multiple components = unified service

Output:

service_name: "my-service"
is_unified: true | false
components: [{name, path, applicationName}]  # if unified

Phase 2: Module Detection

bash
# Strategy A: Explicit WithModule calls (preferred)
grep -rn "WithModule(" internal/ components/ 2>/dev/null
# Extract string arg: WithModule("onboarding") → module "onboarding"

# Strategy B: Component-based (if no WithModule found)
ls components/  # each component = one module
# module_name = component's ApplicationName

# Strategy C: Single-component fallback
# module_name = service ApplicationName

Merge: Strategy A → B fills gaps → C fallback.

Phase 3: Resource Detection per Module

For each module, scan {component_path}/internal/adapters/:

bash
# PostgreSQL: subdirectory existence
ls {base_path}postgres/ 2>/dev/null

# MongoDB
ls {base_path}mongodb/ 2>/dev/null || ls {base_path}mongo/ 2>/dev/null

# RabbitMQ
ls {base_path}rabbitmq/ 2>/dev/null
grep -l "producer\|Producer" {base_path}rabbitmq/ 2>/dev/null
grep -l "consumer\|Consumer" {base_path}rabbitmq/ 2>/dev/null

# Redis (informational only — NOT a dispatch layer resource)
ls {base_path}redis/ 2>/dev/null

Phase 3.5: Database Name Detection per Module

bash
# From bootstrap config
grep -E 'env:"POSTGRES_NAME|env:"DB_.*_NAME|env:"MONGO_NAME|env:"MONGO_.*_NAME' \
  {component_path}/internal/bootstrap/config.go

# From .env.example (actual values)
grep -E "POSTGRES_NAME=|DB_.*_NAME=|MONGO_NAME=|MONGO_.*_NAME=" {component_path}/.env.example

# External datasources
grep -E "DATASOURCE_.*_DATABASE=" {component_path}/.env.example

Cross-reference across modules: same database name in 2+ modules = shared (provision once).

Phase 4: MongoDB Index Detection & Migration File Generation

Only execute if MongoDB was detected in any module during Phase 3.

Execute the procedure in references/mongodb-index-detection.md — Steps 1, 2, 3, 4 only. (Detection and local generation only; S3 upload is handled in Phase 6.)

  1. Step 1 — Detect in-code index definitions (EnsureIndexes, IndexModel, CreateIndex) per module.
  2. Step 2 — Detect existing local migration files in {component_path}/scripts/mongodb/*.up.json + *.down.json (fallback legacy *.js). LOCAL ONLY — no S3 lookup.
  3. Step 3 — Cross-reference code vs. local migration files, classify each as covered / missing_migration / migration_only.
  4. Step 4 — Generate one .up.json + .down.json file pair per missing index (atomic per index, NOT grouped by collection):
    • Path: {component_path}/scripts/mongodb/{NNNNNN}_{index_name}.up.json / .down.json — per-module directory preserves ownership for Phase 6 upload (single-component services resolve {component_path} to repo root)
    • Naming: idx_{collection}_{fields} (or uniq_* for uniqueness business rules)
    • HARD GATE: every .up.json MUST have explicit "options.name" matching the file name
    • Track per module: populate module.generated_migration_files = [{up_file, down_file, index_name}, ...] so Phase 6 knows exactly which files belong to which module

Format reminder: the dispatch layer reads .up.json / .down.json from S3 and applies indexes on tenant provisioning. The service does NOT execute these files. Legacy .js scripts are NOT uploaded — only JSON migrations.

Phase 4.5: PostgreSQL Migration Detection

Only execute if PostgreSQL was detected in any module during Phase 3.

Detection only — Postgres migrations are written by developers; this skill does NOT generate .sql files.

bash
# Common golang-migrate locations (per module path resolved in Phase 3)
ls {component_path}/scripts/postgres/*.up.sql 2>/dev/null
ls {component_path}/scripts/postgresql/*.up.sql 2>/dev/null
ls {component_path}/db/migrations/*.up.sql 2>/dev/null
ls {component_path}/migrations/*.up.sql 2>/dev/null

For each .up.sql file found:

  • Verify the matching .down.sql exists (golang-migrate convention).
  • Map it to its module (by directory path).
  • Track for the Phase 6 upload.

Store: module.postgres_migrations = [{up_file, down_file, sequence, description}]

If a .up.sql exists without .down.sql → flag in Phase 5 HTML report (golang-migrate requires pairs).

Phase 5: Generate HTML Report

Dispatch ring:visualizing:

Generate HTML report showing:
- Service: {service_name} | Unified: {is_unified}
- For each module:
  - Resources table: type, repositories/collections, has_producer, has_consumer, queues
  - Database names: postgres_db, mongo_db (with env var names)
  - Redis: detected / not detected (note: key prefixing only)
- Shared databases: list with modules that share them
- Dispatch layer registration template (JSON)
- MongoDB index coverage table (collection / keys / code / migration / index name) per `references/mongodb-index-detection.md` "Report Section: MongoDB Index Coverage"
- PostgreSQL migration table per module: file count, missing-pair warnings (`.up.sql` without `.down.sql`)
- Upload-ready summary: total Mongo pairs and Postgres pairs to be offered for upload in Phase 6

Style: clean, data-dense table layout

Phase 6: Optional S3 Upload

Execute after Phase 5. Always opt-in — never upload without explicit user confirmation.

text
1. Print summary and ask:
   "Ready to upload to S3:
    - MongoDB: {N} index migration pairs (.up.json/.down.json) across {M} modules
    - PostgreSQL: {P} migration pairs (.up.sql/.down.sql) across {Q} modules
    Upload to S3? (y/n)"

2. If user declines → done. Files remain local. Report status: "Upload skipped by user."

3. If user accepts:
   a. Verify AWS CLI: `aws --version`. If absent → abort, report "AWS CLI not installed."
   b. Ask: "Which S3 bucket? (e.g., lerian-development-migrations)"
   c. Ask: "Which environment? (staging / production)"
   d. Verify access: `aws s3 ls s3://{bucket}/{env}/ 2>&1`. If access denied or 404 → abort, report error.

4. Upload Mongo files (per module, best-effort — continue on individual failures):
   for each module with module.generated_migration_files populated in Phase 4:
     for each {up_file, down_file} in module.generated_migration_files:
       aws s3 cp {up_file}   s3://{bucket}/{env}/{service}/{module}/mongodb/$(basename {up_file})   --content-type "application/json"
       aws s3 cp {down_file} s3://{bucket}/{env}/{service}/{module}/mongodb/$(basename {down_file}) --content-type "application/json"

5. Upload Postgres files (per module, best-effort):
   for each module with module.postgres_migrations populated in Phase 4.5:
     for each {up_file, down_file} in module.postgres_migrations:
       aws s3 cp {up_file}   s3://{bucket}/{env}/{service}/{module}/postgresql/$(basename {up_file})   --content-type "application/sql"
       aws s3 cp {down_file} s3://{bucket}/{env}/{service}/{module}/postgresql/$(basename {down_file}) --content-type "application/sql"

6. Verify per module:
   aws s3 ls s3://{bucket}/{env}/{service}/{module}/mongodb/
   aws s3 ls s3://{bucket}/{env}/{service}/{module}/postgresql/

7. Report uploaded files (full s3:// paths) and any errors. Do NOT abort the whole run if a single file fails — list failures at the end.

Path convention (matches actual bucket layout): s3://{bucket}/{env}/{service}/{module}/{mongodb|postgresql}/{filename}

Output: Dispatch Layer Registration Template

json
{
  "service": "{service_name}",
  "modules": [
    {
      "name": "{module_name}",
      "resources": [
        {
          "type": "postgresql",
          "database": "{db_name}",
          "env_var": "POSTGRES_NAME"
        },
        {
          "type": "mongodb",
          "database": "{db_name}",
          "env_var": "MONGO_NAME"
        },
        {
          "type": "rabbitmq",
          "has_producer": true,
          "has_consumer": true,
          "queues": ["{queue_name}"]
        }
      ]
    }
  ],
  "shared_databases": []
}

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 Ring:Mapping Service Resources AI skill do?

Mapping a Go service's Service -> Module -> Resource hierarchy for dispatch-layer registration: detects modules and per-module PostgreSQL/MongoDB/RabbitMQ resources, database names, and shared databases, generates MongoDB index migration pairs (.up.json/.down.json), detects existing Postgres migrations, emits an HTML report, and offers opt-in S3 upload. Use before ring:adding-multi-tenancy on a new service. Skip for non-Go projects.

Why use Ring:Mapping Service Resources on TypingMind?

Because you install it once and use it with any model. Ring:Mapping Service Resources 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 Ring:Mapping Service Resources in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/dev-team/skills/mapping-service-resources. 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 Ring:Mapping Service Resources?

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 Ring:Mapping Service Resources?

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

Is the Ring:Mapping Service Resources AI skill free?

Yes. It is published on GitHub by LerianStudio under the Apache-2.0 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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