AI Skills

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</td></tr></table> <p align="center"> <p align="center"> A smarter agent harness for <a href="https://github.com/laude-institute/terminal-bench">Terminal-Bench</a>, built on <a href="https://github.com/laude-institute/terminal-bench">Terminus 2</a> with native tool calling <br/> <em>Simple fixes, significant gains.</em> </p> <p align="center"> <img src="https://img.shields.io/badge/Codex_5.3-75.5%25-blue?style=for-the-badge" alt="Codex 5.3: 75.5%"> <img src="https://img.shields.io/badge/Opus_4.6-75.7%25-blueviolet?style=for-the-badge" alt="Opus 4.6: 75.7%"> <img src="https://img.shields.io/badge/Gemini_3.1_Pro-74.8%25-orange?style=for-the-badge" alt="Gemini 3.1 Pro: 74.8%"> </p> </p>

Changelog

Version	Description
v1.1	Migrated from In-Context Learning (ICL) to native tool calling via LLM `tools` parameter. Removed verbose JSON/XML response format instructions from system prompt — the model now receives structured tool definitions directly, resulting in a significantly shorter prompt and more reliable outputs.
v1.0	Initial release. Fork of Terminus 2 with ICL-based JSON response parsing and full response format instructions in the system prompt.

Key Features

Native Tool Calling — Replaces ICL JSON/XML parsing with the LLM tools parameter for structured, reliable outputs
Image Analysis (Multimodal) — image_read tool for base64-encoded image analysis directly from the terminal
Marker-based Polling — Early command completion detection using echo markers, cutting unnecessary wait time
Block Timeout Protection — 10-minute timeout on infrastructure API calls to prevent indefinite hangs
Smart Completion Verification — Double-confirmation checklist covering requirements, robustness, and multi-perspective QA (test engineer, QA engineer, user)
Prompt Caching — Anthropic ephemeral caching on recent messages to reduce latency and cost

Architecture

Terminus-KIRA extends Terminus 2 by replacing its ICL (In-Context Learning) response parsing with native LLM tool calling.

Tool definitions passed via the tools parameter:

Tool	Purpose
`execute_commands`	Run shell commands with analysis and plan
`task_complete`	Signal task completion (triggers double-confirmation)
`image_read`	Analyze image files via base64 multimodal input

How it works:

Calls litellm.acompletion directly with tools=TOOLS, bypassing the base Chat class to access native tool calling
The model returns structured tool calls instead of free-form text — no regex/JSON parsing needed
On context window overflow, automatically summarizes conversation history and retries
Marker-based polling appends echo '__CMDEND__<seq>__' after each command; if the marker appears before the requested duration, execution moves on immediately

Evolution

Key milestones from development history:

#	Milestone	Description
1	Genesis	Copy of Terminus 2 as starting point
2	Native Tool Use	Replaced ICL JSON/XML parsing with LLM `tools` parameter
3	Output Limiting	30 KB cap on terminal output to prevent context bloat
4	Autonomy & Constraints	Prompt engineering for agent autonomy and environment constraints
5	Completion Confirmation	Include original instruction in completion check
6	Multimodal	`image_read` tool for visual analysis of terminal screenshots
7	Completion Checklist	Multi-perspective QA checklist (test engineer, QA, user)
8	Execution Optimization	Marker-based polling and block timeout protection
9	Temperature Fix	Set temperature to 1 when using reasoning effort

Usage

uv run harbor run \
    --dataset terminal-bench-sample@2.0 \
    --n-tasks 1 \
    --agent-import-path "terminus_kira.terminus_kira:TerminusKira" \
    --model anthropic/claude-opus-4-6 \
    --env docker \
    -n 1

For more details, visit our blog post.

Project Structure

├── terminus_kira/
│   ├── __init__.py
│   └── terminus_kira.py        # Main agent (native tool calling)
├── prompt-templates/
│   └── terminus-kira.txt        # System prompt
├── run-scripts/
│   ├── run_docker.sh            # Local Docker execution
│   ├── run_daytona.sh           # Daytona cloud execution
│   └── run_runloop.sh           # Runloop cloud execution
├── anthropic_caching.py         # Prompt caching utility
└── pyproject.toml

Citing Us

If you found Terminus-KIRA useful, please cite us as:

@misc{terminuskira2026,
      title={Terminus-KIRA: Boosting Frontier Model Performance on Terminal-Bench with Minimal Harness },
      author={{KRAFTON AI} and {Ludo Robotics}},
      year={2026},
      url={https://github.com/krafton-ai/kira},
}

KRAFTON AI & Ludo Robotics

Name	slack-memory-cleanup
Description	Memory cleanup and organization skill for AI employees. Provides guidelines for detecting duplicates, fixing misclassified files, and removing stale information from memory storage.

slack-memory-cleanup