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 chat_agentgit 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_agent)<a href="https://agentmods.dev/skills/deeleeramone/pywry/chat_agent"><img src="https://agentmods.dev/badge/skills/deeleeramone/pywry/chat_agent/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_agent"><img src="https://agentmods.dev/badge/skills/deeleeramone/pywry/chat_agent.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 176 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 | $0.00034 | $0.01784 |
| Opus 5.5 | $0.00014 | $0.00714 |
| Sonnet 5.5 | $0.00007 | $0.00357 |
| Haiku 4.5 | $0.00003 | $0.00178 |
Grade A, and why
chat_agent 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chat — Agent Operating Manual
You are running INSIDE a PyWry chat widget. This skill is not about creating a chat — it's about operating correctly when the chat is the UI you're attached to.
Where your input comes from
The user types a message; the chat manager packages it and passes it
to your provider (DeepagentProvider or equivalent). You receive:
- text — the user's literal message
- attachments — any
@<name>context the user inlined, expanded into a block prepended to the message - thread history — the running conversation stored against a
session_id/thread_idkeyed checkpointer
Your reply is streamed token-by-token into the UI. Tool calls you make are shown as collapsible tool-result cards in the chat.
The @<name> attachment format
When the user types @chart (or any other registered context
source), the chat manager prepends a block to the message like:
--- Attached: chart ---
widget_id: chart
<...any additional component context...>
--- End Attached ---
<the user's actual text>
The first line after the marker is ALWAYS widget_id: <id> for
widget attachments. Read that value out and use it as the
widget_id argument on every tool call for this turn. Never
guess — the attachment is the source of truth.
If the user references a widget without attaching it, either:
- Call
list_widgets()to look it up by name. - Ask the user to attach it (
"Type @chart so I know which widget you mean.").
Do NOT invent a widget_id.
Auto-attached context sources
Some examples register context sources that get auto-attached to
every user message. In that case you'll see the --- Attached ---
block even when the user didn't explicitly type @<name>. Treat
it the same way — read widget_id and use it.
Tool-call result cards
Every tool call you make is rendered in the chat as a card showing:
- Tool name (e.g.
tvchart_symbol_search) - Status — spinner while running, ✓ on success, ✗ on failure
- Collapsible payload: arguments in, result out
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 · 190 lines · 34 tokens per session scan A bdd6735fbe83
chat_agent is a skill published in the GitHub repository deeleeramone/PyWry (98 stars, last pushed 4d ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,784 once invoked, about $0.0001 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.
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