Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/squadcodercom/squadcoder/israeli-chatbot-analyticsnpx skills add squadcodercom/squadcoder --skill israeli-chatbot-analyticsgit clone --depth 1 https://github.com/squadcodercom/squadcoderWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00232 | $0.08843 |
| Opus 5 | $0.00116 | $0.04422 |
| Sonnet 5 | $0.00046 | $0.01769 |
| Haiku 4.5 | $0.00023 | $0.00884 |
Grade A, and why
israeli-chatbot-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 545 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Israeli Chatbot Analytics
Analyze and optimize Hebrew chatbot performance. This skill covers conversation flow analytics, Hebrew-specific sentiment analysis, drop-off detection, user satisfaction scoring, A/B testing for Hebrew response variants, intent recognition accuracy tracking, anomaly alerting, and reporting dashboards. Use it to understand whether your Hebrew chatbot is actually helping users and where to focus improvements.
Instructions
Step 1: Collect and Structure Conversation Logs
Before analyzing, ensure conversation data is structured consistently. Each conversation session should include:
# Standard conversation log schema
conversation_log = {
"session_id": "uuid-string",
"user_id": "anonymous-or-identified",
"channel": "whatsapp|telegram|web|app",
"language": "he", # Primary language detected
"started_at": "ISO-8601",
"ended_at": "ISO-8601",
"messages": [
{
"timestamp": "ISO-8601",
"sender": "user|bot",
"text": "שלום, אני צריך עזרה",
"intent": "greeting", # Detected intent
"intent_confidence": 0.92, # Model confidence
"entities": [], # Extracted entities
"response_time_ms": 340, # Bot response latency
}
],
"outcome": "resolved|escalated|abandoned|unknown",
"satisfaction_score": null, # CSAT score if collected
"metadata": {
"bot_version": "2.1.0",
"ab_variant": "formal_he",
}
}
If your platform does not export in this format, write a transformer to normalize logs before analysis. Common platforms and their export formats:
| Platform | Export Method | Format |
|---|---|---|
| Dialogflow CX | BigQuery export | JSON rows with session context. Use the he-il language code on new agents; iw is deprecated and frozen for new features (https://docs.cloud.google.com/dialogflow/cx/docs/reference/language). |
| Rasa Pro / CALM | Analytics dashboard + tracker events | Flow-step events (Rasa Pro 3.x with CALM is dialogue-driven, not intent-driven, so legacy intent-accuracy metrics map differently). |
| Rasa Open Source (legacy) | Tracker Store (SQL/Mongo) | Events list per conversation. Rasa OSS entered maintenance mode in 2025, see https://legacy-docs-oss.rasa.com/docs/rasa/. |
| Botpress | Conversation export / DB | JSON. Hebrew is listed as a supported language but full RTL alignment in the default web webchat is still a community-reported gap as of 2026, verify message bubble alignment in your widget before reporting on dialect distribution. |
| Custom bots | Application logs | Varies (normalize to schema above) |
| WhatsApp Cloud API | Webhook logs | Message objects with metadata. See ## WhatsApp Business Platform pricing notes below for the per-message cost model that started July 2025. |
| ManyChat | Audience + flow exports | CSV/JSON. WhatsApp send-out costs flow through Meta's per-message tariff. |
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 545 lines · 232 tokens per session scan A b442e5f184a3
israeli-chatbot-analytics is a skill published in the GitHub repository squadcodercom/squadcoder (11 stars, last pushed 2mo ago), licensed MIT. It adds 232 tokens to every session and 8,843 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
docker-extend
Use when: User wants to extend Docker with custom tools, personalize the Docker environment, or set up user-specific Docker customization. Triggers: 'extend docker', 'docker-extend', 'add tools to docker', 'customize docker', 'add my tools to the container', 'personalize docker setup', 'docker user setup', 'install…
write-zot-themes
Help the user create, install, or package zot themes, including theme-only extensions.
database-migrations
Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Django, TypeORM, golang-migrate). Use when planning or implementing database schema changes.
python-testing
Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests.
docker-patterns
Docker and Docker Compose patterns for local development, container security, networking, volume strategies, and multi-service orchestration. Use when setting up containerized development environments or reviewing Docker configurations.
python-patterns
Python-specific design patterns and best practices including protocols, dataclasses, context managers, decorators, async/await, type hints, and package organization. Use when working with Python code to apply Pythonic patterns.