# How i-have-adhd Suppresses Tangents in AI Responses: System Prompt Engineering > Learn how i-have-adhd uses system prompt engineering to suppress tangents in AI responses. Discover the technique for focused and relevant AI output. - Repository: [Ayoub Ghriss/i-have-adhd](https://github.com/ayghri/i-have-adhd) - Tags: deep-dive - Published: 2026-08-23 --- **The i-have-adhd project suppresses tangents by injecting a specific rule into the AI's system prompt that commands the model to finish the current issue before offering secondary items as separate, optional follow-ups.** The `ayghri/i-have-adhd` repository provides a Claude-Code and OpenCode skill designed to keep AI-generated responses focused and actionable. By embedding concrete behavioral rules directly into the system prompt, the tool leverages the model's instruction-following capabilities to eliminate "by-the-way" digressions that derail task completion for users with ADHD. ## The "Suppress Tangents" Rule Definition The core instruction resides in [`skills/i-have-adhd/SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md) at lines 64-70. This file serves as the **source of truth** for all ADHD-style interaction rules. The specific rule states: > *If a second issue exists, finish the first, then offer the second as a separate question.* > *Bad: “Here’s the fix. By the way, your dependency is also stale, …”* > *Good: “Here’s the fix. Separately: there is also a stale dependency. Want me to handle that next?”* When active, this directive functions as a hard constraint. The model treats it as a **system-level instruction**, meaning it applies to every turn of the conversation without requiring reinforcement in user prompts. ## System Prompt Injection Mechanism No runtime logic monitors the AI output. Instead, the rule achieves enforcement through **prompt injection** at initialization. The repository provides two implementations that strip YAML front-matter from [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) and append the rule body to the system prompt. ### Claude-Code Integration The `hooks/always-on.mjs` file handles automatic injection for Claude-Code environments. Lines 27-34 read the [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) file, remove its front-matter, and write the remaining content to `stdout`, effectively appending it to the model's context: - Reads the skill definition from disk - Parses and removes YAML front-matter - Streams the rule set body into the active system prompt ### OpenCode Integration The `.opencode/plugins/i-have-adhd.mjs` file mirrors this behavior for OpenCode. Its `rulesetBody()` function (lines 35-43) performs the identical transformation: - Locates [`SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/SKILL.md) within the plugin directory - Strips metadata headers programmatically - Returns the raw rule text for system prompt concatenation ## Enabling Always-On Tangent Suppression Users can activate the rule set either **on-demand** via the `/i-have-adhd` command or **always-on** by creating specific flag files in their home directory. The always-on mode ensures the "suppress tangents" instruction persists across sessions without manual activation. **For Claude-Code:** ```bash touch ~/.claude/.i-have-adhd-always ``` **For OpenCode:** ```bash mkdir -p ~/.config/opencode touch ~/.config/opencode/.i-have-adhd-always ``` Once these files exist, the hook scripts automatically inject the complete rule set—including the tangent suppression directive—into every new AI session. ## Practical Impact on AI Output With the rule active in the system prompt, the model restructures its responses to enforce **sequential task completion**. Instead of embedding secondary issues within the primary solution, the AI separates them distinctly. **Example of suppressed tangents:** ``` ✅ Fixed the broken import path. Separately: the project's README still references the old module name. Want me to update it now? ``` This output demonstrates the "Good" pattern from the rule definition. The first issue receives full attention and closure. The secondary issue appears only after a clear break, framed as an **optional follow-up question** rather than a mid-sentence diversion. The model avoids "by-the-way" style asides that would otherwise fragment the user's cognitive focus. ## Summary - **i-have-adhd** suppresses tangents by embedding a concrete behavioral rule in [`skills/i-have-adhd/SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md) (lines 64-70) that instructs the model to finish one issue before introducing another. - **System prompt injection** occurs via `hooks/always-on.mjs` (Claude-Code) and `.opencode/plugins/i-have-adhd.mjs` (OpenCode), which strip YAML front-matter and append the rule body to the AI context. - **Always-on activation** requires creating flag files at `~/.claude/.i-have-adhd-always` or `~/.config/opencode/.i-have-adhd-always`. - **No runtime filtering** is necessary; the model's inherent instruction-following capabilities enforce the rule once present in the system prompt. - **Output structure** changes from blended asides to sequential, opt-in follow-ups (e.g., "Separately: ...?"). ## Frequently Asked Questions ### How does the "Suppress tangents" rule technically prevent digressions? The rule does not rely on post-processing or output filtering. Instead, it operates at the **prompt engineering layer**. By including the instruction in the system prompt, the model internalizes it as a core behavioral constraint before generation begins. According to the source code in [`skills/i-have-adhd/SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md), the rule explicitly defines the desired output structure, causing the model to self-correct during token generation to avoid "by-the-way" patterns. ### What is the difference between on-demand and always-on activation? **On-demand** activation requires manually invoking the `/i-have-adhd` command when you need focused responses. **Always-on** activation creates a persistent flag file (`~/.claude/.i-have-adhd-always` or `~/.config/opencode/.i-have-adhd-always`) that triggers automatic injection via the hook scripts (`always-on.mjs` or `i-have-adhd.mjs`) every time a new AI session starts. The always-on mode suits users who require consistent tangent suppression across all interactions. ### Where is the rule definition physically stored in the repository? The canonical definition resides in **[`skills/i-have-adhd/SKILL.md`](https://github.com/ayghri/i-have-adhd/blob/main/skills/i-have-adhd/SKILL.md)** at lines 64-70. This file contains the complete rule set written in Markdown with YAML front-matter. Both the Claude-Code hook and the OpenCode plugin reference this specific file path when injecting rules into the system prompt, making it the single source of truth for the project's behavior. ### Why does the injection code strip YAML front-matter from SKILL.md? The front-matter contains metadata (such as skill name and version) intended for human readers or package managers, not for the AI model. The stripping logic in `hooks/always-on.mjs` (lines 27-34) and `.opencode/plugins/i-have-adhd.mjs` (lines 35-43) ensures that only the **behavioral rules** (the actual content) reach the system prompt. Including metadata would consume context window tokens without providing the model actionable instructions, potentially diluting the effectiveness of the "suppress tangents" directive.