Ring:Using Lib Commons logo

Ring:Using Lib Commons

Organization
LerianStudio
ring:using-lib-commons

Using lib-commons v5, Lerian's shared Go library (non-observability surface), in two modes. Sweep Mode detects DIY code replaceable by lib-commons at file:line. Reference Mode catalogs lifecycle (Launcher), outbox repo, circuit breakers, tenant management, idempotency, security/TLS, database, and HTTP toolkit. Observability moved to ring:using-lib-observability. Go-only. Skip for non-Go.

Overview

PublisherLerianStudio
Repositoryring
Skill namering:using-lib-commons
Stars
215
Forks
28
Bundled files
2
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.

  • 2 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:Using Lib Commons 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/using-lib-commons .claude/skills/lerianstudio-ring-using-lib-commons
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Using Lib Commons 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:Using Lib Commons 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:Using Lib Commons 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.

ring:using-lib-commons

Scope note (lib-observability v1.0.0): The observability layer — log, metrics, tracing, zap, assert, runtime (panic recovery), redaction, and OTel attribute constants — moved out of lib-commons into github.com/LerianStudio/lib-observability as of v1.0.0. lib-commons v5 keeps deprecated shims for back-compat, but this skill is no longer the canonical reference for those packages. For observability work, dispatch [[using-lib-observability]] (top-level) or its dedicated sub-skills [[using-tracing]] / [[using-runtime]] / [[using-assert]]. This skill now focuses on lib-commons's non-observability surface: lifecycle (commons.Launcher), outbox repository (writer side lives in [[using-lib-streaming]]), circuit breakers, tenant management, idempotency, security/TLS, database connections, messaging (RabbitMQ command queues; events go through [[using-lib-streaming]]), HTTP toolkit.

When to use

Sweep mode:

  • "Sweep the codebase for lib-commons opportunities"
  • "Find where we could use lib-commons instead of DIY"
  • "Audit this service for lib-commons compliance"
  • "Identify lib-commons migration opportunities"

Reference mode:

  • Need to understand what lib-commons provides
  • Looking for the right package/API for a task
  • Setting up a new service that uses lib-commons
  • Need correct constructor/initialization patterns
  • Working with multi-tenancy (tenant-manager subsystem)
  • Working with event-driven tenant discovery

Skip when

  • Working on non-Go services
  • Working on frontend code
  • Target codebase is Ring itself (no lib-commons dependency)

Related

Similar: ring:using-dev-team, ring:planning-backend-refactor Observability layer (moved to lib-observability): [[using-lib-observability]], [[using-tracing]], [[using-runtime]], [[using-assert]] Adjacent libs: [[using-outbox]], [[using-lib-streaming]], [[using-lib-systemplane]]

Mode Selection

Request ShapeMode
"Sweep / audit / find opportunities / migrate to lib-commons"Sweep
"What does lib-commons provide for X?"Reference
"How do I initialize Y from lib-commons?"Reference
"Replace our DIY webhook delivery with lib-commons"Sweep

SWEEP MODE

Orchestrate a 4-phase sweep. Each phase has a hard gate — do not proceed until the current phase produces its artifact.

Phase 1: Version Reconnaissance   → version-report.json
Phase 2: CHANGELOG Delta Analysis → delta-report.json
Phase 3: Multi-Angle DIY Sweep    → 22 × libcommons-sweep-{N}-{angle}.json
Phase 4: Consolidated Report      → libcommons-sweep-report.md + tasks.json

Phase 1: Version Reconnaissance

  1. Read go.mod — extract pinned version of github.com/LerianStudio/lib-commons/vN
  2. WebFetch https://api.github.com/repos/LerianStudio/lib-commons/releases/latest — extract tag_name
  3. Classify drift: up-to-date / minor-drift / moderate-drift / major-upgrade / module-mismatch
  4. If v4.x detected: add major upgrade advisory flag
  5. Emit version-report.json: {pinned_version, latest_version, drift_classification, major_upgrade_required, module_path}

Phase 2: CHANGELOG Delta Analysis

  1. WebFetch https://raw.githubusercontent.com/LerianStudio/lib-commons/main/CHANGELOG.md
  2. Extract entries between pinned_version (exclusive) and latest_version (inclusive)
  3. Classify each: new-package / new-api / breaking-change / security-fix / performance / bugfix
  4. Emit delta-report.json with classified entries

Phase 3: Multi-Angle DIY Sweep

Dispatch all 22 explorer angles in 3 batches (8+8+6). Wait for each batch before next.

BatchAnglesFocus
11–8Infrastructure + HTTP
29–16Ergonomics + security + observability-shim detection
317–22Resilience + multi-tenant + utilities

⛔ STOP-CHECK BEFORE DISPATCH (each batch)

Before emitting any Task call in a batch, count the explorers you intend to launch in this turn.

  • Count MUST equal the batch size declared above (8, 8, or 6).
  • If your dispatch count diverges from the batch size → STOP and reconcile against the batch row.
  • No substitutions, no omissions within a batch.

⛔ MUST NOT trickle-dispatch within a batch

All explorers in a batch leave in the SAME TURN, before reading any explorer output.

Forbidden sequences:

  • Dispatch explorer 1 → read result → dispatch explorer 2
  • Dispatch a subset of the batch → wait → dispatch the rest
  • Dispatch follow-up explorers conditioned on partial output
  • Loop sequentially over the batch's angle list

If you find yourself about to dispatch an explorer in a turn AFTER any explorer in the SAME batch has already returned a result → STOP. You violated parallel dispatch. Report the violation and mark the batch INCOMPLETE rather than completing the trickle. (Sequential batch ordering is intentional; trickle within a batch is not.)

Self-verify after dispatch

After each batch's dispatch turn, verify all batched Task calls were emitted in that single turn. If fewer went out than the batch size, the batch did NOT execute correctly. Mark INCOMPLETE and surface the dispatch failure — do NOT silently continue with a partial batch.

Parallel dispatch — atomic batch (within this batch)

Emit all Task calls for THIS BATCH in a SINGLE TURN, as one atomic batch. (Batches themselves remain sequential — do not dispatch batch N+1 until batch N has fully returned.)

If your runtime exposes a multi_tool_use.parallel wrapper, use it to dispatch the complete batch in one wrapped invocation. This is the canonical fan-out mechanism on OpenAI-style tool envelopes and on certain Anthropic SDK consumers — naming it explicitly activates parallel emission on runtimes where trickle-dispatch is the default behavior.

If your runtime emits parallel tool_use blocks natively (Claude Code with Claude models), multi_tool_use.parallel may not be needed — but naming it is harmless and serves as an enforcement anchor.

The STOP-CHECK, anti-trickle, and self-verify guards above remain binding regardless of which mechanism your runtime uses.

Observability angles (14, 15, 16) — scope shift: these angles still run as part of this sweep, but their detection logic now targets deprecated lib-commons shim imports in addition to raw DIY. The canonical replacement is lib-observability/*, NOT commons/zap, commons/runtime, commons/assert. For a deep, dedicated audit of the observability layer, dispatch [[using-lib-observability]] (top-level) or [[using-tracing]] / [[using-runtime]] / [[using-assert]] instead — those produce richer findings than the breadth-first single-angle sweep here.

Per-explorer dispatch (subagent_type: ring:codebase-explorer):

## Target
<absolute path to target repo root>

## Your Angle
<angle number + name>

## Severity Calibration / DIY Patterns / Replacement / Migration Complexity / Version Context
<verbatim from sub-files/sweep-angles.md for this angle>

## Output
Write findings to: /tmp/libcommons-sweep-{N}-{angle-slug}.json
Schema: { angle_number, angle_name, severity, migration_complexity, findings: [{file, line, diy_pattern, replacement, evidence_snippet, notes}], summary, requires_major_upgrade }
If no findings: write file with empty findings array and summary "No DIY patterns detected for this angle".

Full angle specifications: sub-files/sweep-angles.md

Phase 4: Consolidated Report

Dispatch synthesizer (subagent_type: ring:codebase-explorer):

Read /tmp/version-report.json, /tmp/delta-report.json, /tmp/libcommons-sweep-*.json (22 files).
Emit:
1. /tmp/libcommons-sweep-report.md — aggregate findings by severity
2. /tmp/libcommons-sweep-tasks.json — one task per DIY pattern cluster (same file/package = one task)

MUST NOT invent findings. MUST NOT omit explorer findings. MUST NOT reclassify severity without justification.

Surface report path + task count to user; offer handoff to ring:running-dev-cycle.


REFERENCE MODE

Full API catalog in sub-files/reference.md. Load the relevant sections for your current task.

Quick Navigation

#SectionWhat you'll find
1Package CatalogAll packages by domain
2Common Initialization PatternTypical service bootstrap
3Database Connectionspostgres, mongo, redis, rabbitmq
4HTTP ToolkitMiddleware, rate limiting, pagination, idempotency
5Observability (moved)Logger, tracing, metrics, runtime, assert — now in [[using-lib-observability]]
6Resilience & UtilitiesCircuit breaker, backoff, safe math
7SecurityJWT, encryption, sensitive fields, TLS
8Transaction DomainIntent planning, balance posting, outbox
9Tenant ManagerFull multi-tenancy subsystem
10Webhook DeliverySSRF-safe HMAC-signed delivery
11Dead Letter QueueRedis-backed DLQ with exponential backoff
12Root Package & UtilitiesApp lifecycle, errors, UUID, env vars
13Cross-Cutting PatternsShared patterns across packages
14Which Package Do I Need?Decision tree
15Breaking ChangesMigration notes v4.2.0 → v5.x

Read sub-files/reference.md for full API detail.

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:Using Lib Commons AI skill do?

Using lib-commons v5, Lerian's shared Go library (non-observability surface), in two modes. Sweep Mode detects DIY code replaceable by lib-commons at file:line. Reference Mode catalogs lifecycle (Launcher), outbox repo, circuit breakers, tenant management, idempotency, security/TLS, database, and HTTP toolkit. Observability moved to ring:using-lib-observability. Go-only. Skip for non-Go.

Why use Ring:Using Lib Commons on TypingMind?

Because you install it once and use it with any model. Ring:Using Lib Commons 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:Using Lib Commons in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/dev-team/skills/using-lib-commons. 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:Using Lib Commons?

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:Using Lib Commons?

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

Is the Ring:Using Lib Commons 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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