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/deeleeramone/pywry/tvchartnpx skills add deeleeramone/PyWry --skill tvchartgit clone --depth 1 https://github.com/deeleeramone/PyWryWhat 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 | $0.00033 | $0.02188 |
| Opus 5 | $0.00016 | $0.01094 |
| Sonnet 5 | $0.00007 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
tvchart 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TradingView Chart — Agent Reference
Use this when an agent needs to read or mutate a live
tvchartwidget. Every action is an MCP tool call on the PyWry FastMCP server — there are no local helpers, no side channels, no custom tools. Pick the typed tool that matches the user's intent, pass the required arguments, and quote the tool's return values in your reply.
Every tool takes widget_id
widget_id identifies which chart to operate on. On a single-chart
server the framework auto-resolves it from the registry; on a
multi-chart server you must pass it explicitly. Read the value from
the user's @<name> attachment (the chat prepends --- Attached: <name> ---\nwidget_id: <id>) or call list_widgets() to enumerate.
Reading chart state — always via tvchart_request_state
Never report symbol / interval / indicators / bars / last close from memory. Call the tool, quote the return.
tvchart_request_state(widget_id)
→ {
"widget_id": "chart",
"state": {
"symbol": "AAPL",
"interval": "1D",
"series": [{ "seriesId": "main", "bars": [...], ... }],
"indicators": [...],
"visibleRange": { "from": ..., "to": ... },
"chartType": "Candles",
...
}
}
When the user asks "what's on the chart", "what's the current price",
"what indicators are applied", call this and quote from state.
Mutating tools — all confirm the change
Every mutation returns the real post-change state. The model never has
to guess whether the change took effect. If the mutation didn't land
within the settle window, the tool includes a note field — relay
it to the user.
Symbol change
tvchart_symbol_search(widget_id, query, auto_select=True,
symbol_type=None, exchange=None)
→ { "widget_id": "chart", "symbol": "MSFT", "state": {...} }
Use this to switch the ticker. auto_select=True commits the
selection; auto_select=False just opens the search dialog for the
user. The tool polls chart state until the symbol actually changes
to the target (up to ~6s) so the return reflects reality.
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 · 245 lines · 33 tokens per session scan A e77e4703fe95
tvchart is a skill published in the GitHub repository deeleeramone/PyWry (93 stars, last pushed 8d ago), licensed Apache-2.0. It adds 33 tokens to every session and 2,188 once invoked, about $0.0002 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.
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