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Ring:Planning Codebase Simplification

Organization
LerianStudio
ring:planning-codebase-simplification

Planning a whole-codebase simplification: audits a Go/TS codebase for needless abstraction and emits a KILL/REVIEW/KEEP plan plus a ring:running-dev-cycle task array. Plans only — no edits. Detects single-impl interfaces, pass-through shims, translation-free adapters, and dead-code cascade chains under an inverted burden of proof. Use for pre-public or post-pivot cleanup. Skip for current diff review (use ring:reviewing-code).

Overview

PublisherLerianStudio
Repositoryring
Skill namering:planning-codebase-simplification
Stars
215
Forks
28
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Ring:Planning Codebase Simplification 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/planning-codebase-simplification .claude/skills/lerianstudio-ring-planning-codebase-simplification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Planning Codebase Simplification 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:Planning Codebase Simplification 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:Planning Codebase Simplification 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.

Dev Simplify — Whole-Codebase Structural Sweep

When to use

  • User asks to simplify, flatten, or audit architecture of a whole codebase
  • User mentions "too much indirection", "kill shims", "unnecessary abstractions"
  • Pre-public application where break-compatibility refactor is cheap
  • Post-pivot cleanup: speculative scaffolding accumulated during exploration

Skip when

  • Diff review on a feature branch → use ring:reviewing-code
  • Standards-conformance refactor → use ring:planning-backend-refactor
  • Dead code from a specific change → use ring:dead-code-reviewer in ring:reviewing-code
  • Application already has external clients depending on internals

Related

Complementary: ring:reviewing-code, ring:codebase-explorer Similar: ring:planning-backend-refactor, ring:auditing-production-readiness

Core principle: DELETE is the default verdict. An abstraction survives only with concrete evidence of the swap it enables.

Hard Constraint

Default: public APIs MUST NOT break (HTTP routes, SDK surface, webhooks, event contracts).

Supply hard_constraint input to override. Must be declared — never auto-inferred.

Dispatch Protocol

⛔ STOP-CHECK BEFORE DISPATCH

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

  • Count MUST equal 6 (or 5 if branch has no commits ahead of main — Task 5 skipped).
  • If your dispatch count diverges → STOP and reconcile against the task table below.
  • No substitutions, no omissions.

⛔ MUST NOT trickle-dispatch

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

Forbidden sequences:

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

If you find yourself about to dispatch an explorer in a turn AFTER any explorer has already returned a result → STOP. You violated parallel dispatch. Report the violation and mark the phase INCOMPLETE rather than completing the trickle.

Self-verify after dispatch

After the dispatch turn, verify all scoped Task calls (6, or 5 if Task 5 skipped) were emitted in that single turn. If fewer went out than scoped, the phase did NOT execute correctly. Mark INCOMPLETE and surface the dispatch failure — do NOT silently continue with a partial pool.

Parallel dispatch — atomic batch

Emit all scoped Task calls (the count established in the STOP-CHECK above — 6 or 5) in a SINGLE TURN, as one atomic batch.

If your runtime exposes a multi_tool_use.parallel wrapper, use it to dispatch the complete pool 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.

Dispatch 6 explorer agents in parallel (5 if branch has no commits ahead of main — skip Task 5):

TaskAgentFocus
1aring:codebase-explorerSingle-impl interfaces, ports, repositories
1bring:codebase-explorerSpeculative factories, builders, strategies, facades
2ring:codebase-explorerTranslation-free adapters, pass-through shims, internal DTOs
3ring:codebase-explorerArchitecture topology mapping, indirection depth
4ring:codebase-explorerCascade chains (Three Rings applied to codebase)
5ring:codebase-explorerBranch AI slop (diff vs main) — skip if no commits ahead

Explorer dispatch contract:

## Target: <absolute path to repo root>
## Your Focus: <smell category from task table above>
## Hard Constraint: {hard_constraint}
## Output: Write to /tmp/simplify-{task}-findings.json
Schema: { task, findings: [{name, file_line, smell, rebuttal_if_any, blast_radius, public_api_impact, action}], cascade_chains: [...] }

Abstraction Smell Rubric

SmellSignalDefault Action
Single-implementation interfaceOne concrete impl; test doubles identical to prodDELETE
Translation-free adapterA→B is rename-only, 1:1 field mappingDELETE
Pass-through shimWraps call site-for-site, no cross-cutting concernDELETE
Speculative factory/builderAlways constructs the same concrete typeDELETE
One-strategy strategyDispatch over enum with one caseCOLLAPSE
One-consumer facadeSingle call site, "for future reuse"COLLAPSE INTO CALLER
Config seam over constantIndirection for a value that never variesDELETE
Internal DTO ↔ entity with 1:1 fieldsTranslation across identical shapesDELETE
Hexagonal port with one adapterNo swap pressureCOLLAPSE
Narrating comment (branch diff)Comment restates what code literally doesDELETE
Defensive check in trusted path (branch diff)Guard where caller already validatedDELETE

Accepted evidence to KEEP (must name concretely, not hypothetically):

  • Second implementation exists today in this repo
  • Swappability exercised in tests with divergent behavior
  • Cross-process or cross-language boundary
  • Regulatory or contractual requirement

Phase 4: Consolidated Report

Dispatch synthesizer to read all explorer files and emit:

  1. docs/ring:planning-codebase-simplification/simplify-report-{timestamp}.md — KILL / REVIEW / KEEP tables
  2. docs/ring:planning-codebase-simplification/simplify-tasks-{timestamp}.json — ring:running-dev-cycle task array

MUST emit both artifacts. MUST include cascade chains decomposed into per-ring tasks with depends_on wiring.

Output Format

markdown
## Simplify Summary
- Scope / Hard Constraint / Generated
- Kill list: N items | Review list: N items | Keep list: N items

## Hard Constraint
- Declared constraint + load-bearing surface location

## Kill List
| Name | file:line | Smell | Blast radius | Action | Acceptance Criteria |

## Review List
| Name | file:line | Smell | Why uncertain | Recommended next step |

## Keep List
| Name | file:line | Smell resembled | Evidence |

## Cascade Chains
| Chain ID | Leaf | Ring depth | Terminal type | Collapse blast radius |

## Cascade Execution Plan
Per-chain DAG: ring-1 (leaf) → ring-N, each with depends_on wiring

## Remaining Risks
| Risk ID | Related findings | Risk type | Mitigation |

Task JSON Schema

json
{
  "tasks": [{
    "id": "simplify-001",
    "title": "",
    "severity": "KILL | REVIEW",
    "smell_category": "unexercised-seam | speculative-construction | translation-layer | topology | cascade | branch-slop",
    "files_affected": [],
    "blast_radius": {"files": 0, "lines": 0},
    "acceptance_criteria": [],
    "estimated_complexity": "trivial | moderate | complex",
    "depends_on": [],
    "rebuttal_if_kept": null
  }]
}

Cascade chains → N tasks with depends_on wiring (leaf = ring-1, depends_on: []).

Frequently asked questions

What does the Ring:Planning Codebase Simplification AI skill do?

Planning a whole-codebase simplification: audits a Go/TS codebase for needless abstraction and emits a KILL/REVIEW/KEEP plan plus a ring:running-dev-cycle task array. Plans only — no edits. Detects single-impl interfaces, pass-through shims, translation-free adapters, and dead-code cascade chains under an inverted burden of proof. Use for pre-public or post-pivot cleanup. Skip for current diff review (use ring:reviewing-code).

Why use Ring:Planning Codebase Simplification on TypingMind?

Because you install it once and use it with any model. Ring:Planning Codebase Simplification 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:Planning Codebase Simplification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/dev-team/skills/planning-codebase-simplification. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ring:Planning Codebase Simplification?

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:Planning Codebase Simplification?

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

Is the Ring:Planning Codebase Simplification 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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