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 mamahoos/dot-files --skill tg-rich-sendgit clone --depth 1 https://github.com/mamahoos/dot-filesWrote 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/mamahoos/dot-files/tg-rich-send)<a href="https://agentmods.dev/skills/mamahoos/dot-files/tg-rich-send"><img src="https://agentmods.dev/badge/skills/mamahoos/dot-files/tg-rich-send/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/mamahoos/dot-files/tg-rich-send"><img src="https://agentmods.dev/badge/skills/mamahoos/dot-files/tg-rich-send.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 | $0.00077 | $0.00463 |
| Opus 5.5 | $0.00031 | $0.00185 |
| Sonnet 5.5 | $0.00015 | $0.00093 |
| Haiku 4.5 | $0.00008 | $0.00046 |
Grade A, and why
tg-rich-send 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 20d 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.
What it actually says
tg-rich-send
Send Markdown to the user's Telegram. All logic (env, chunking, retries) lives in the script — call it, do not re-implement it.
Quick start
python3 ~/dev/scripts/tg-rich FILE.md # send a file
cat doc.md | python3 ~/dev/scripts/tg-rich - # send stdin
python3 ~/dev/scripts/tg-rich FILE.md --dry-run # preview chunking only
Write the Markdown to a temp file first if it only exists in the conversation
(/tmp/opencode/ is the approved scratch dir).
What the script does
- Reads
TELEGRAM_BOT_TOKEN/TELEGRAM_CHAT_IDfrom env or~/dev/.env - Sends via
sendRichMessagewithrich_message.markdown(rich markdown style:#headings,>quotes, lists,---dividers, ``` fences) andis_rtl: true - Chunks at top-level
---boundaries, never inside code fences (limit 3900 chars) - Prints
part i/N: ok=True · message_id=...per chunk; exits non-zero on failure
Flags
| Flag | Effect |
|---|---|
--dry-run |
show chunk sizes without sending |
--no-rtl |
left-to-right (English/LTR-only content) |
--limit N |
chunk size in chars |
--chat ID |
override chat id |
Notes
- On failure the script prints the Telegram API error body — report it verbatim.
- Missing env → check
~/dev/.env; do not hard-code the token anywhere. - Order of chunks is preserved; tell the user how many parts were sent.
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.
- 20d ago First seen · 44 lines · 77 tokens per session scan A fc9ea0370d3f
tg-rich-send is a skill published in the GitHub repository mamahoos/dot-files (5 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 463 once invoked, about $0.0003 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 2026-10-07.
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