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 skills add squadcodercom/squadcoder --skill israeli-chatbot-analyticsgit clone --depth 1 https://github.com/squadcodercom/squadcoderWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/squadcodercom/squadcoder/israeli-chatbot-analytics)<a href="https://agentmods.dev/skills/squadcodercom/squadcoder/israeli-chatbot-analytics"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/israeli-chatbot-analytics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/squadcodercom/squadcoder/israeli-chatbot-analytics"><img src="https://agentmods.dev/badge/skills/squadcodercom/squadcoder/israeli-chatbot-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00232 | $0.08843 |
| Opus 5.5 | $0.00093 | $0.03537 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (12 stars, last pushed 3mo ago), licensed MIT. It adds 232 tokens to every session and 8,843 once invoked, about $0.0009 per session on Opus 5.5. 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
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…
semantic-compression
Re-encode verbose prose into a dense telegraphic register — punctuation as connectives, label frames, verbless assertions — without losing normativity or precision. Use when compressing system prompts, tool/function descriptions, skill bodies, or agent instructions; reducing token count or context bloat; making…
write-zot-themes
Help the user create, install, or package zot themes, including theme-only extensions.
writing-git-commits
Write clear, focused Git commit messages and keep commits easy to review.
hatch3r-board-groom
Ongoing backlog refinement for existing board items. Re-prioritize, reclassify, re-scope, archive stale items, decompose oversized issues, merge duplicates, refresh dependencies, and remediate board health findings.
hatch3r-cli-toolbox
Category-indexed reference for 34 specialist CLI tools beyond the always-on five (ripgrep, jq, gh, fd, fzf). Use to pick the right tool for HTTP clients, ai-chat, structural-search, sed-style edits, data ops, browser automation, container ops, and more.