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Horizon

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GadaaLabs
horizon

Manage context window budget across long sessions — when to compress, what to give subagents, when to start fresh. Prevents silent quality degradation as conversations grow.

Overview

PublisherGadaaLabs
Repositoryclaude-code-on-steroids
Skill namehorizon
Stars
67
Forks
10
Bundled files
Instructions only
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 GadaaLabs on GitHub. Read the source before you install it.

Installation

Install the Horizon 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/GadaaLabs/claude-code-on-steroids.git /tmp/claude-code-on-steroids
mkdir -p .claude/skills
cp -r /tmp/claude-code-on-steroids/skills/horizon .claude/skills/horizon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Horizon 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 Horizon 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 Horizon 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.

Context Management

Overview

HORIZONThe horizon is where you look to see what's approaching before it arrives. When invoked: monitors context window health (Green / Yellow / Red), compresses stale reasoning, distills subagent context to the minimum required, and prepares clean handoff packages when a fresh session is needed.

Core principle: A full context window is a silent quality killer. Proactively manage it — don't wait until things break.

Long sessions accumulate stale reasoning, superseded decisions, and irrelevant history. Subagents given bloated context lose focus. Claude running near the limit compresses its own earlier thinking without telling you.

Announce at start: "Running HORIZON to assess and manage context health."


Context Health Monitor

Check context health at these triggers:

TRIGGER context health check when:
- Session has had 10+ back-and-forth exchanges
- About to dispatch a subagent
- About to start a new task after completing one
- User says "you seem confused" or repeats a correction
- You notice yourself re-reading earlier decisions

Health Assessment (30 seconds)

Ask yourself:

QuestionSignal
Can I summarize the current task in 2 sentences?Yes → healthy | No → overloaded
Are there superseded decisions still in context?Yes → stale
Would a subagent need > 500 words of context?Yes → compress first
Have I re-explained the same thing twice?Yes → context drift
Am I about to start a completely new task?Yes → consider fresh session

The 4 Context States

State 1: HEALTHY (< 40% full, task focused)

Action: Continue normally
Subagent dispatch: Include full relevant context

State 2: LOADED (40–70% full, some stale content)

Action: Compress before dispatching subagents
Run: Context Compression Protocol (below)
Subagent dispatch: Compressed context only

State 3: HEAVY (70–90% full, multiple topics mixed)

Action: Mandatory compression + summarize decisions
Consider: Fresh session for next major task
Subagent dispatch: Minimal essential context only
Warn: "Context is heavy — I'll give subagents a focused summary."

State 4: CRITICAL (> 90% full or visibly degrading)

Action: Stop. Save state. Recommend fresh session.
Announce: "Context is near limit. I recommend starting a fresh
           session with a state handoff to maintain quality."
DO NOT: Continue with complex reasoning at this state

Context Compression Protocol

Run this before dispatching any subagent or starting a new task:

Step 1: Extract Active State

Identify and keep only:

ACTIVE STATE (always keep):
- Current task: [one sentence]
- Current files in play: [list of paths]
- Decisions made THIS session that affect the work: [list]
- Constraints discovered THIS session: [list]
- Where we are in the plan: [step N of M]

Step 2: Archive Superseded Content

Mentally mark as "stale" (don't repeat in subagent context):

STALE (exclude from subagent context):
- Approaches we tried and rejected
- Earlier versions of the same decision
- Diagnostic output that led to a fix (keep the fix, drop the output)
- Exploration that dead-ended
- Re-explanations of things already understood

Step 3: Build Subagent Context Block

markdown
## Subagent Context (Compressed)

**Task:** [1 sentence]
**Goal:** [1 sentence — what done looks like]

**Key files:**
- `path/to/file.ts` — [what it does, 1 line]
- `path/to/other.py` — [what it does, 1 line]

**Decisions already made (don't re-decide):**
- [decision 1: what + why]
- [decision 2: what + why]

**Constraints (non-negotiable):**
- [constraint 1]
- [constraint 2]

**Completed steps:**
- [x] Step 1: [what was done]
- [x] Step 2: [what was done]

**Your task:**
- [ ] Step N: [exact instructions]

Target: < 400 tokens for subagent context. Ruthlessly cut anything not needed for the specific task.


Fresh Session Handoff Protocol

When context is CRITICAL or switching to a completely new major task:

Step 1: Write State File

Save to docs/superpowers/session-state-<date>.md:

markdown
# Session State — <date> <time>

## What Was Accomplished
- [bullet list of completed tasks with file paths]

## Current State
- In progress: [task name]
- Next steps: [numbered list]
- Blockers: [any open questions]

## Key Decisions Made
| Decision | Rationale | Affects |
|----------|-----------|---------|
| [decision] | [why] | [files] |

## Architecture Discovered
- [any non-obvious patterns in the codebase]
- [any gotchas or traps to know]

## Files Modified This Session
[git diff --stat output]

## Resume Prompt
To resume: "Continue work on [task]. State file at docs/superpowers/session-state-<date>.md. 
Run `git log --oneline -10` to see recent changes."

Step 2: Commit State

bash
git add docs/superpowers/session-state-<date>.md
git commit -m "chore: save session state for handoff"

Step 3: Announce Handoff

SESSION STATE SAVED
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
State file: docs/superpowers/session-state-<date>.md
Commit:     <sha>

To resume in a fresh session, share the state file and say:
"Resume from session state."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Subagent Context Budget Rules

Subagent TypeMax ContextWhat to Include
Implementer (single task)300 tokensTask only + relevant file paths
Code reviewer500 tokensWhat was built + requirements + git range
Debugger400 tokensError + reproduction + relevant files
Researcher/Explorer200 tokensQuestion only + scope constraint
Coordinator (swarm)600 tokensFull plan + progress state

Rule: If your context block exceeds budget → cut. If you can't cut below budget → split into two subagents.


Context Anti-Patterns

Anti-PatternProblemFix
Sending full conversation to subagentSubagent distracted by irrelevant historyCompress to active state only
Keeping rejected approaches in contextMuddies current directionMark stale, exclude from subagent context
Re-explaining decisions each dispatchWastes tokens, inconsistentMake one canonical decision block, reuse
Ignoring context state until it breaksQuality degrades silentlyCheck health at every task boundary
Never starting fresh sessionsContext accumulates errorsSave state, start fresh for major task switches

Integration with Superpowers

Run before:

  • commander — compress before each dispatch
  • phantom — check health before each task
  • legion — build clean context per topology type

Triggered by:

  • oracle — check context health as part of pre-task assessment
  • chronicle — store patterns before fresh session

Final Rule

Context is a resource — budget it like tokens
Compress before dispatching
Save state before switching
Fresh session > degraded session
Quality over continuity

Frequently asked questions

What does the Horizon AI skill do?

Manage context window budget across long sessions — when to compress, what to give subagents, when to start fresh. Prevents silent quality degradation as conversations grow.

Why use Horizon on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GadaaLabs/claude-code-on-steroids/tree/main/skills/horizon. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Horizon?

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 Horizon?

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

Is the Horizon AI skill free?

It is published on GitHub by GadaaLabs. Check the repository for licensing terms. 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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