web-navigation-strategies
Strategic navigation patterns and selector guides for thorough web exploration using Playwright MCP. Provides decision trees, navigation str
Open source Repository Open in the app JSON README (API)
About
Strategic navigation patterns and selector guides for thorough web exploration using Playwright MCP. Provides decision trees, navigation strategies, and site-specific selectors for reading multiple pa
Details
- Kind
- Agent skills
- Topic
- Web search, scraping & browser
- Publisher
- krafton-ai
- Origin
- majiayu
- Category
- ferramentas
- Stars
- 932
- Forks
- 106
- Open pull requests
- 2
- Last push
- 2026-05-29T07:42:04Z
- Repository state
- ativo
- Language
- Python
- License
- Apache-2.0
- Added
- 2026-09-02 18:18:42
- Updated
- 2026-09-02 18:18:42
- Origin id
krafton-ai/kira/data/web-navigation-strategies@main
README
<table align="center"><tr><td>
```
● ●
▄████◣◢████▄
◥▄████▀▀ ▀▀████▄◤
▀▀ ▀▀
_____ ___ ___ __ __ ___ _ _ _ _ ___ _ _____ ___ _
|_ _| __| _ \ \/ |_ _| \| | | | / __|___| |/ /_ _| _ \ /_\
| | | _|| / |\/| || || .` | |_| \__ \___| ' < | || / / _ \
|_| |___|_|_\_| |_|___|_|\_|\___/|___/ |_|\_\___|_|_\/_/ \_\
```
</td></tr></table>
<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>
<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>
## What is Terminus-KIRA?
Terminus-KIRA is an agent harness for [Terminal-Bench](https://github.com/laude-institute/terminal-bench), built on top of [Terminus 2](https://github.com/laude-institute/terminal-bench). It boosts frontier model performance on Terminal-Bench through a set of minimal but effective harness-level improvements — native tool calling, multimodal support, execution optimization, and smarter completion verification.
---
## 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
- **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:**
1. Calls `litellm.acompletion` directly with `tools=TOOLS`, bypassing the base `Chat` class to access native tool calling
2. The model returns structured tool calls instead of free-form text — no regex/JSON parsing needed
3. On context window overflow, automatically summarizes conversation history and retries
4. 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
```bash
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](https://krafton-ai.github.io/blog/terminus_kira_en/).
---
## 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:
```bibtex
@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},
}
```
> [!NOTE]
> **TerminalBench Submission Correspondence**
>
> The agent used for the official TerminalBench submission was logged under the internal path
> `kraftonai-terminus/terminal_bench/agents/terminus_3/terminus_3_3.py`.
> This corresponds to [`terminus_kira/terminus_kira.py`](./terminus_kira/terminus_kira.py) in this repository —
> the file name and path were cleaned up during open-source preparation.
---
## 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. |
---
KRAFTON AI & Ludo Robotics