# How to Manage Team-Wide Deployments with Consistent Rule Configurations Using the i-have-adhd Skill

> Master team-wide deployments with consistent rule configurations using the i-have-adhd skill. Ensure uniform LLM output via declarative configuration and CI validation.

- Repository: [Ayoub Ghriss/i-have-adhd](https://github.com/ayghri/i-have-adhd)
- Tags: how-to-guide
- Published: 2026-08-05

---

**The i-have-adhd repository provides a Claude/Cursor skill that enforces a strict, ADHD-friendly output format across all LLM-generated responses, enabling teams to guarantee uniform formatting through declarative configuration files and automated CI validation.**

Team-wide LLM deployments often suffer from inconsistent output styles as developers invoke models with different prompting habits. The **i-have-adhd** skill solves this by codifying a 10-rule style sheet into a reusable plugin that any team member can install and activate. This approach eliminates configuration drift and ensures every response follows the same structural contract—regardless of which developer triggered the generation.

## Understanding the Skill Architecture

The repository organizes its components into five clear layers, each serving a specific purpose in the deployment pipeline:

| Component | Purpose | Key Files |
|-----------|---------|-----------|
| **Skill definition** | Declares the rule set and persistence behavior | [[`skills/i-have-adhd/SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md)](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md) |
| **Plugin metadata** | Exposes the skill to Claude/Cursor plugin system | [[`plugin.json`](https://github.com/ayghri/i-have-adhd/blob/main/plugin.json)](https://github.com/ayghri/i-have-adhd/blob/main/plugin.json) |
| **Agent configuration** | Binds the skill to specific LLM backends without code changes | [[`agents/openai.yaml`](https://github.com/ayghri/i-have-adhd/blob/main/agents/openai.yaml)](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/agents/openai.yaml) & [[`agents/gemini.toml`](https://github.com/ayghri/i-have-adhd/blob/main/agents/gemini.toml)](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/agents/gemini.toml) |
| **Installation scripts** | Automates plugin registration for reproducible deployment | [[`scripts/run_evals.py`](https://github.com/ayghri/i-have-adhd/blob/main/scripts/run_evals.py)](https://github.com/ayghri/i-have-adhd/blob/main/scripts/run_evals.py) |
| **CI workflows** | Validates skill definition on every push | [[`.github/workflows/plugin-load-check.yml`](https://github.com/ayghri/i-have-adhd/blob/main/.github/workflows/plugin-load-check.yml)](https://github.com/ayghri/i-have-adhd/blob/main/.github/workflows/plugin-load-check.yml) |

### How the Skill Enforces Consistency

The deployment mechanism follows a four-phase lifecycle:

1. **Load Phase** — The platform reads [`plugin.json`](https://github.com/ayghri/i-have-adhd/blob/main/plugin.json) to locate skill files when users run `claude plugin install i-have-adhd@i-have-adhd`

2. **Activation** — [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) is parsed and its rules stored in **persistent** LLM session context (lines 17-21 of [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md))

3. **Enforcement** — Every response is filtered through the rule engine, which rewrites output to satisfy the 10-rule contract

4. **Persistence Control** — The skill remains active until explicit deactivation phrases ("stop adhd mode" or "normal mode"), ensuring multi-turn workflows maintain uniform formatting

This persistence model is critical for **team-wide deployments with consistent rule configurations**—once activated, the skill survives across multiple API calls within the same session.

## Installing the Skill Across Your Team

### One-Line Installation

```bash
claude plugin uninstall i-have-adhd            # optional: clear old version

claude plugin marketplace add ayghri/i-have-adhd
claude plugin install i-have-adhd@i-have-adhd

```

These commands originate from the **Tune it** section of [`README.md`](https://github.com/ayghri/i-have-adhd/blob/main/README.md) (lines 77-84). Teams can distribute this snippet via internal documentation or automated onboarding scripts.

### Activating in a Claude Session

```text
/i-have-adhd

```

The LLM immediately adopts the ADHD-friendly format. To revert:

```text
stop adhd mode

```

This activation pattern ensures **zero-code adoption**—no IDE plugins, no configuration files, no environment variables.

## Configuring Multi-Backend Deployments

The skill supports **language-agnostic binding** through YAML/TOML agent files. This design lets the same rule set attach to OpenAI, Gemini, or future providers without modifications.

### OpenAI Agent Binding

```yaml

# skills/i-have-adhd/agents/openai.yaml

name: i-have-adhd
description: Enforce ADHD-friendly output style
plugin: i-have-adhd

```

### Gemini Agent Binding

```toml

# skills/i-have-adhd/agents/gemini.toml

name = "i-have-adhd"
description = "Enforce ADHD-friendly output style"
plugin = "i-have-adhd"

```

Teams can version these files in a shared repository, ensuring every developer's agent configuration matches the organizational standard.

## Automating Validation with CI

The GitHub Action in [`.github/workflows/plugin-load-check.yml`](https://github.com/ayghri/i-have-adhd/blob/main/.github/workflows/plugin-load-check.yml) guarantees that rule changes don't break deployments:

```yaml
name: Plugin Load Check
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Verify plugin loads
        run: |
          claude plugin install i-have-adhd@i-have-adhd
          claude plugin list | grep i-have-adhd

```

This workflow runs on every commit, catching malformed [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) syntax or missing plugin metadata before changes reach production.

## Updating Rules Through Pull Requests

Because [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) is plain markdown, teams evolve their style guide through standard git workflows:

- **Propose** — Developer opens PR modifying [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md)
- **Validate** — CI workflow confirms plugin loads correctly
- **Review** — Team approves rule changes (e.g., adding an 11th rule)
- **Deploy** — Merged changes propagate automatically to all installations

This declarative approach eliminates the "works on my machine" problem common in prompt engineering.

## Key Files for Deployment Management

| File | Role |
|------|------|
| [[`README.md`](https://github.com/ayghri/i-have-adhd/blob/main/README.md)](https://github.com/ayghri/i-have-adhd/blob/main/README.md) | Overview and installation instructions |
| [[`skills/i-have-adhd/SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md)](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md) | Full rule set and behavioral contract |
| [[`plugin.json`](https://github.com/ayghri/i-have-adhd/blob/main/plugin.json)](https://github.com/ayghri/i-have-adhd/blob/main/plugin.json) | Plugin descriptor for Claude/Cursor |
| [[`skills/i-have-adhd/agents/openai.yaml`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/agents/openai.yaml)](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/agents/openai.yaml) | OpenAI backend binding example |
| [[`.github/workflows/plugin-load-check.yml`](https://github.com/ayghri/i-have-adhd/blob/main/.github/workflows/plugin-load-check.yml)](https://github.com/ayghri/i-have-adhd/blob/main/.github/workflows/plugin-load-check.yml) | CI validation workflow |

These five files constitute the entire deployment surface—minimal, inspectable, and version-controlled.

## Summary

- **Declarative rules in [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md)** — Single source of truth eliminates configuration drift
- **Persistent session activation** — Rules survive across multiple LLM calls without re-invocation
- **Multi-backend YAML/TOML bindings** — Same skill works with OpenAI, Gemini, and future providers
- **Zero-code installation** — `claude plugin install` command enables trivial team rollouts
- **CI-validated changes** — GitHub Action guarantees every modification loads correctly before deployment

## Frequently Asked Questions

### How does the skill maintain consistency across different developers' machines?

The skill stores its rule set in [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md), a plain markdown file that installs identically on every machine through the `claude plugin install` command. Because the rules are packaged with the plugin—not configured per-user—every team member receives the exact same behavioral contract automatically.

### Can the skill be used with LLM providers other than OpenAI?

Yes. The repository includes agent configuration files for multiple backends: [`skills/i-have-adhd/agents/openai.yaml`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/agents/openai.yaml) and [`skills/i-have-adhd/agents/gemini.toml`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/agents/gemini.toml). The skill framework is designed to bind to any LLM provider that supports the plugin protocol.

### What happens if someone modifies the rules incorrectly?

The GitHub Action in [`.github/workflows/plugin-load-check.yml`](https://github.com/ayghri/i-have-adhd/blob/main/.github/workflows/plugin-load-check.yml) validates every pull request. If a change to [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) breaks the plugin load sequence, the CI check fails and the PR cannot merge without correction—preventing broken deployments.

### How do team members deactivate the skill when needed?

Users issue the natural language commands **"stop adhd mode"** or **"normal mode"** (specified in rule 21 of [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md)). This returns the LLM to default behavior without uninstalling the plugin, allowing flexible workflow switching.