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 deeleeramone/PyWry --skill chatgit clone --depth 1 https://github.com/deeleeramone/PyWryWrote 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/deeleeramone/pywry/chat)<a href="https://agentmods.dev/skills/deeleeramone/pywry/chat"><img src="https://agentmods.dev/badge/skills/deeleeramone/pywry/chat/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/deeleeramone/pywry/chat"><img src="https://agentmods.dev/badge/skills/deeleeramone/pywry/chat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 148 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00000 | $0.01463 |
| Opus 5.5 | $0.00000 | $0.00585 |
| Sonnet 5.5 | $0.00000 | $0.00293 |
| Haiku 4.5 | $0.00000 | $0.00146 |
Grade A, and why
chat 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chat Component - Reference Guide
Read this before creating chat widgets.
Overview
The chat component provides a full-featured conversational UI with:
- Message rendering with inline markdown
- Streaming responses with token-by-token display
- Stop-generation (cancel in-flight LLM responses)
- Thread management (create, switch, delete)
- Slash command palette (type
/to see commands) - Settings panel (model, temperature, system prompt)
- LLM provider adapters (OpenAI, Anthropic, custom callback)
Quick Start
Via MCP Tool
{
"name": "create_chat_widget",
"arguments": {
"title": "AI Assistant",
"model": "gpt-4",
"system_prompt": "You are a helpful assistant.",
"streaming": true,
"provider": "openai"
}
}
Via Python
from pywry import App
from pywry.chat import ChatConfig, ChatWidgetConfig
app = App()
config = ChatWidgetConfig(
title="AI Chat",
height=600,
chat=ChatConfig(
system_prompt="You are helpful.",
model="gpt-4",
streaming=True,
provider="openai",
),
)
# Widget creation handled by MCP or directly via app.show()
MCP Tools
create_chat_widget
Creates a chat widget. Returns {widget_id, thread_id}.
| Parameter | Type | Default | Description |
|---|---|---|---|
| title | string | "Chat" | Window title |
| height | integer | 600 | Window height |
| system_prompt | string | "" | System prompt for LLM |
| model | string | "gpt-4" | Model name |
| temperature | number | 0.7 | Sampling temperature (0-2) |
| max_tokens | integer | 4096 | Max tokens per response |
| streaming | boolean | true | Enable streaming |
| persist | boolean | false | Persist threads in ChatStore |
| provider | string | — | "openai", "anthropic", "callback" |
| show_sidebar | boolean | true | Show thread sidebar |
| slash_commands | array | — | Custom slash commands |
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 · 193 lines · 0 tokens per session scan A 2fd4ad0af641
chat is a skill published in the GitHub repository deeleeramone/PyWry (98 stars, last pushed 4d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,463 tokens. 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.
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