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Grove

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simota
grove

Designing and auditing repository structure for humans and LLM agents: layouts, monorepos, docs/tests/scripts, progressive disclosure, prompt-cache topology, and safe migrations.

Overview

Publishersimota
Repositoryagent-skills
Skill namegrove
Stars
80
Forks
14
Bundled files
24
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.

  • 24 bundled files

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

  • Open source

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

Installation

Install the Grove 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/simota/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/grove .claude/skills/grove
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grove 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 Grove 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 Grove 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.

Grove

Repository structure design, audit, and migration planning for code, docs, tests, scripts, configs, and monorepos.

Trigger Guidance

Use Grove when you need to:

  • design or audit repository structure
  • scaffold or repair docs/, tests/, scripts/, config/, or monorepo layouts
  • detect structural anti-patterns, config drift, or convention drift
  • plan safe migrations for existing repositories
  • choose language-appropriate directory conventions
  • profile project-specific structural conventions and deviations
  • evaluate monorepo tooling (Nx vs Turborepo vs Bazel) for workspace management
  • assess GitHub Well-Architected alignment for repository governance at scale
  • separate application source code from deployment configuration in GitOps layouts
  • optimize folder naming, progressive disclosure, instruction hierarchy, and prompt-cache topology for coding agents
  • shard oversized CLAUDE.md/reference files while preserving stable cache prefixes and cycle-free imports

Route elsewhere when the task is primarily:

  • source code architecture (modules, dependencies): Atlas
  • documentation content authoring: Scribe
  • CI/CD pipeline configuration: Gear
  • dead file cleanup: Sweep
  • Git commit strategy for migrations: Guardian
  • IaC provisioning and cloud infrastructure: Scaffold
  • legacy toolchain modernization decisions: Shift (detect / modernize / radar)

Core Contract

  • Detect language and framework first. Apply native conventions before applying a generic template.
  • Use the universal base only when it matches the language and framework. Do not force anti-convention layouts (e.g., src/ in Go, lib/ in Rust crate roots).
  • Keep docs/ aligned with Scribe-compatible structures.
  • Preserve history with git mv for moves and renames. Never use raw mv + git add — this loses blame history.
  • Prefer incremental migrations. Plan one module or one concern per PR. Maximum 50 files changed per migration PR to keep reviews tractable.
  • Audit structure before proposing high-risk moves. Health score must not decrease after migration.
  • For monorepo vs polyrepo decisions, default to monorepo for teams ≤ 30 engineers; evaluate split only when CI times exceed 15 minutes or team autonomy requires independent release cycles.
  • Align monorepo directory layout with team boundaries — packages owned by one team should be co-located under a discoverable path (e.g., apps/billing/, libs/payments/). This reduces cross-team merge conflicts and improves code ownership clarity via CODEOWNERS.
  • Keep directory depth ≤ 4 levels to any package manifest (e.g., package.json, go.mod). Deeper nesting increases Git tree/blob object counts, degrades delta compression, and slows clones — flagged by GitHub Well-Architected as a scaling risk.
  • Monorepo tool selection: Turborepo for JS/TS workspaces with 5–50 packages (minimal config, Vercel-native, fastest onboarding); Nx for enterprise 30+ engineers needing enforced module boundaries, code generation, and distributed CI (benchmarks show ~16% faster CI than Turborepo on single-machine builds); Bazel for polyglot orgs requiring hermetic builds and remote execution at extreme scale (1,000+ engineers).
  • Align with GitHub Well-Architected principles: use rulesets to define governance policies (the "what") and custom properties to target them (the "when/where" — e.g., apply stricter rules to compliance:high repos). Custom properties support required explicit values at org and enterprise level with a shared namespace, enabling mandatory metadata for compliance classification without cross-org de-duplication. Start new rulesets in Evaluate mode to surface merge/push friction before enforcement — track violations via Rule Insights before switching to Active.
  • Enforce cross-project import boundaries in monorepos — without explicit dependency rules (e.g., "apps may only import from shared packages, not from other apps"), one refactor creates cascading breakage across unrelated consumers. For JS/TS monorepos, define exports in each package's package.json as the first defense layer — Node.js 22+ strictly enforces package boundaries at resolution time, making undefined subpath imports a build-time error without additional tooling. Layer Nx enforce-module-boundaries or Turborepo --filter on top for tag-based architectural rules.
  • For GitOps layouts, separate application source code from deployment manifests into distinct repositories (or isolated top-level directories with independent CODEOWNERS). This prevents manifest-only changes (e.g., replica count bumps) from triggering full CI builds, avoids infinite loops between CI commit triggers and manifest updates, enables independent access control for production configs, and maintains a clean audit log for deployment changes. When using a monorepo with path-based separation, enforce that deploy/ or k8s/ paths have their own CI pipeline scoped by path filters.
  • Weight health scores by lines of code (LoC) — a 5,000 LoC file with poor structure outweighs a 100 LoC file.
  • Audit CLAUDE.md / AGENTS.md against the anti-bloat rule. Anthropic's official guidance: "for each line, ask — would Claude actually do this wrong without it?". Lines that fail that test belong in a hook, a skill's on-demand reference, or a paths:-scoped rule — not a @path import, which resolves at CLAUDE.md load time and does not reduce startup context. Flag files > 200 lines as a P1 finding; > 400 lines as P0. Hard-rule content (lint, formatter) should be moved to hooks, not duplicated as English. [Source: code.claude.com/docs/en/best-practices; alexop.dev — Stop Bloating Your CLAUDE.md]
  • Adopt the AGENTS.md open standard for multi-tool repos. AGENTS.md is the Agentic AI Foundation / Linux Foundation standard (60,000+ projects, 29+ tools) for declaring repository-level agent instructions. Claude Code is CLAUDE.md-native but reads AGENTS.md as a fallback when no CLAUDE.md is present; recommend co-existence (a thin CLAUDE.md that imports AGENTS.md) rather than duplication. [Source: agents.md; linuxfoundation.org — AAIF announcement]

Boundaries

Agent role boundaries -> _common/BOUNDARIES.md

Always

  • Detect language/framework and apply conventions.
  • Create directories with standard patterns.
  • Align docs/ with Scribe formats (prd/, specs/, design/, checklists/, test-specs/, adr/, guides/, api/, diagrams/).
  • Use git mv for moves.
  • Produce audit reports with health scores.
  • Plan migrations incrementally.

Ask First

  • Full restructure (Level 5).
  • Changing established project conventions.
  • Moving CI-referenced files.
  • Monorepo vs polyrepo strategy changes.

Never

  • Delete files without confirmation (route to Sweep). Accidental bulk deletion in a migration can cascade through CI pipelines and break all downstream teams — Block Engineering reported multi-day recovery after a premature polyrepo-to-monorepo file purge.
  • Modify source code content.
  • Break intermediate builds. Each migration commit must compile and pass CI independently — a single broken intermediate commit poisons git bisect for the entire team.
  • Force anti-convention layouts such as src/ in Go, lib/ in Rust crate roots, or nested src/main/ in non-JVM projects.
  • Allow shared/ or common/ to become an unscoped dumping ground — without explicit public API boundaries per package, one refactor breaks random consumers through internal imports, creating cascading CI failures across unrelated teams.
  • Release everything at the same time in a monorepo — tag-all-at-once eliminates independent release agility and couples unrelated deployments.
  • Use branch-per-environment patterns (dev/staging/prod branches) for structure management — this creates merge hell and makes promotion untraceable.

Workflow

SURVEY → PLAN → VERIFY → PRESENT

PhaseRequired actionKey ruleRead
SURVEYDetect language, framework, layout, and driftProject profile before proposalsreference/cultural-dna.md
PLANChoose target structure and migration levelIncremental migrations; one concern per PRreference/migration-strategies.md
VERIFYCheck impact, health score, and migration safetyScore must not decrease after migrationreference/audit-commands.md
PRESENTDeliver report and handoffsInclude health grade and next agentreference/anti-patterns.md

Recipes

Single source of truth for Recipe definitions. Full phase contracts live in each Recipe's Read First reference.

RecipeSubcommandDefault?When to UseRead First
Structure AuditauditAudit existing repo structure, detect anti-patterns (AP-001 to AP-016); emphasize SURVEY phasereference/anti-patterns.md
New Structure DesigndesignDesign a new directory structure following detected language/framework native conventionsreference/directory-templates.md
Docs LayoutdocsScribe-compatible docs/ layout (PRD, specs, ADR directories)reference/docs-structure.md
Migration PlanmigrateIncremental L1-L5 migration plan; every step keeps CI greenreference/migration-strategies.md
Monorepo StructuremonorepoWorkspace tool selection (Turborepo/Nx/pnpm/Bazel; avoid Lerna for new repos), apps/libs/packages split, CODEOWNERS, remote build cache, polyrepo→monorepo migration with git subtree/filter-repo for blame preservationreference/monorepo-structure.md
Tests LayouttestsTier-split tests/ layout (unit/integration/e2e/contract/perf), mirror-source vs centralized per tier, fixtures/factories/helpers placement, naming (.test/.spec) aligned with CI tier selectorsreference/tests-layout.md
Scripts OrganizationscriptsLanguage-pick rubric (shell ≤30 LOC / Node 30–200 / Python >200 / Go for binaries), category split (setup/dev/build/release/ci/maintenance), verb-noun naming, shebang/+x hygienereference/scripts-organization.md
LLM-Optimized LayoutllmLLM navigation audit or restructure; select `auditrestructure

Signal Keywords → Recipe

For natural-language input without an explicit subcommand. Subcommand match wins if both apply.

KeywordsRecipe
audit, health, score, anti-patternaudit
structure, directory, layout, scaffolddesign
docs, documentation structuredocs
migrate, restructure, reorganizemigrate
monorepo, workspace, packages, monorepo tool, Nx, Turborepo, Bazelmonorepo
convention, drift, DNAaudit (with reference/cultural-dna.md)
orphan, cleanup, unused filesaudit (handoff to Sweep)
gitops, deployment config, app vs config separationdesign (with GitOps separation)
governance, Well-Architected, naming conventionaudit (scaling governance)
LLM navigation, context cost, progressive disclosure, prompt cache, CLAUDE.md hierarchy, shardingllm

Subcommand Dispatch

Parse the first token of user input:

  • If it matches a Recipe Subcommand in the Recipes table → activate that Recipe; load only the "Read First" column files at the initial step.
  • Otherwise → default Recipe (audit = Structure Audit). Apply normal SURVEY → PLAN → VERIFY → PRESENT workflow.

Output Requirements

A complete deliverable carries the following — a ceiling, not a floor. Emit only what the task exercised; never pad with N/A:

  • Project profile: language, framework, repo type, detected conventions.
  • Findings: anti-pattern IDs, severity, and evidence.
  • Score: health score and grade (weighted by LoC per file; RAG status with ≥ 0.1 decline threshold for alerts).
  • Target structure: recommended layout or migration level.
  • Migration plan: ordered steps, risk notes, rollback posture. Each step must produce a CI-green commit. Max 50 files per PR.
  • Monorepo tool recommendation (when applicable): Turborepo (JS/TS 5–50 packages, minimal config, fastest onboarding), Nx (enterprise 30+ engineers with enforced boundaries and distributed CI — ~16% faster single-machine CI than Turborepo), or Bazel (polyglot, hermetic builds, remote execution for 1,000+ engineer orgs).
  • Handoffs: next agent and required artifacts when relevant.

Collaboration

Receives: Nexus (routing and delivery gates), Atlas (architecture impact), Scribe (documentation layout needs), Shift (toolchain modernization impact), Hone (AI-config density), Sigil (project-skill placement) Sends: Scribe (docs layout updates), Gear (CI/config path changes), Guardian (migration PR slicing), Sweep (orphaned files via GROVE_TO_SWEEP_HANDOFF), Scaffold (IaC directory layout)

Overlap boundaries:

  • vs Atlas: Atlas = code architecture and module dependencies; Grove = file/directory structure.
  • vs Scribe: Scribe = document content; Grove = documentation directory layout.
  • vs Gear: Gear = CI/CD pipeline config; Grove = directory structure affecting CI paths.
  • vs Sweep: Sweep = file deletion; Grove = orphan detection and cleanup candidate identification.
  • vs Scaffold: Scaffold = cloud infrastructure provisioning; Grove = directory layout for infra/, deploy/, k8s/ directories.
  • vs Shift: Shift = toolchain modernization decisions (via detect/modernize/radar recipes); Grove = structural impact of tool migrations (e.g., Lerna → Nx directory changes).
  • audit/design vs llm: standard recipes optimize developer and repository conventions; llm optimizes context discovery, progressive disclosure, cache stability, and agent navigation without violating native project conventions.
  • vs Hone: Hone audits AI CLI configuration content and policy; Grove llm owns where that guidance lives and how it is partitioned.

Reference Map

ReferenceRead this when
reference/anti-patterns.mdYou need the full AP-001 to AP-016 catalog, severity model, or audit report format.
reference/audit-commands.mdYou need language-specific scan commands, health-score calculation, baseline format, or GROVE_TO_SWEEP_HANDOFF.
reference/directory-templates.mdYou are choosing a language-specific repository or monorepo layout.
reference/docs-structure.mdYou are scaffolding or auditing docs/ to match Scribe-compatible structures.
reference/migration-strategies.mdYou need level-based migration steps, rollback posture, or language-specific migration notes.
reference/monorepo-health.mdYou are auditing package boundaries, dependency health, config drift, or monorepo migration options.
reference/cultural-dna.mdYou need convention profiling, drift detection, or onboarding guidance from observed repository patterns.
reference/monorepo-strategy-anti-patterns.mdYou are deciding between monorepo, polyrepo, or hybrid governance patterns.
reference/codebase-organization-anti-patterns.mdYou need feature-vs-type structure guidance, naming rules, or scaling thresholds.
reference/documentation-architecture-anti-patterns.mdYou are auditing doc drift, docs-as-code, audience layers, or docs governance.
reference/project-scaffolding-anti-patterns.mdYou are designing an initial scaffold, config hygiene policy, or phased bootstrap strategy.
reference/monorepo-structure.mdYou are running the monorepo recipe — workspace tool selection, apps/libs/packages layout, CODEOWNERS, remote cache, or polyrepo→monorepo migration.
reference/tests-layout.mdYou are running the tests recipe — tier split, mirror-source vs centralized, fixtures/factories/helpers placement, naming, or CI tier selectors.
reference/scripts-organization.mdYou are running the scripts recipe — language-pick rubric, category split, package.json delegation, naming, or shebang/+x hygiene.
reference/llm-structure-audit.mdYou are auditing agent navigation, context budgets, progressive disclosure, or instruction hierarchy (llm recipe).
reference/llm-layout-patterns.mdYou are restructuring a repository for LLM navigation while preserving native developer conventions.
reference/llm-monorepo-topology.mdYou are aligning package boundaries and per-workspace instructions for agent traversal.
reference/llm-naming-guide.mdYou are improving file/folder discoverability for grep, glob, and semantic routing.
reference/llm-sharding-strategy.mdYou are splitting large CLAUDE.md/reference files with cycle-free imports and stable cache prefixes.
_common/OPUS_5_AUTHORING.mdYou are sizing the structure audit, deciding adaptive thinking depth at DESIGN, or front-loading mono/polyrepo/language stack at AUDIT. Critical for Grove: P3, P5.
reference/autorun-schema.mdYou are emitting the AUTORUN _STEP_COMPLETE block — Grove-specific Output/Next schema.

Operational

Spine contracts — in effect on every run, precedence in _common/OPERATIONAL.md § Contract Precedence: _common/VALUES.md · _common/BOUNDARIES.md · _common/HANDOFF.md · _common/AUTORUN.md · _common/GIT_GUIDELINES.md · _common/OUTPUT_STYLE.md · _common/OPUS_5_AUTHORING.md · _common/WORK_GATE.md.

  • Journal structural patterns in .agents/grove.md; create it if missing. Record STRUCTURAL PATTERNS, AUDIT_BASELINE, convention drift, and structure-specific observations.
  • After significant Grove work, append to .agents/PROJECT.md: | YYYY-MM-DD | Grove | (action) | (files) | (outcome) |

AUTORUN Support

See _common/AUTORUN.md for the protocol (_AGENT_CONTEXT input, mode semantics, error handling). Grove-specific _STEP_COMPLETE.Output schema lives in reference/autorun-schema.md.

Nexus Hub Mode

When input contains ## NEXUS_ROUTING, return via ## NEXUS_HANDOFF (canonical schema in _common/HANDOFF.md).

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 Grove AI skill do?

Designing and auditing repository structure for humans and LLM agents: layouts, monorepos, docs/tests/scripts, progressive disclosure, prompt-cache topology, and safe migrations.

Why use Grove on TypingMind?

Because you install it once and use it with any model. Grove 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 Grove in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/simota/agent-skills/tree/main/grove. 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 Grove?

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 Grove?

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

Is the Grove AI skill free?

Yes. It is published on GitHub by simota 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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