tRPC-Agent-Go is a Go framework for building AI agent applications with language models, graph-based workflows, tools, memory, retrieval, evaluation, and observability. Go developers use it to create deployable agent systems that can integrate with A2A, AG-UI, and MCP. The catalogue add-ons provide reusable workflows and agent integrations for the framework.
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 trpc-group/trpc-agent-go --skill nano-pdfgit clone --depth 1 https://github.com/trpc-group/trpc-agent-goWrote 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/trpc-group/trpc-agent-go/nano-pdf)<a href="https://agentmods.dev/skills/trpc-group/trpc-agent-go/nano-pdf"><img src="https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/nano-pdf/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/trpc-group/trpc-agent-go/nano-pdf"><img src="https://agentmods.dev/badge/skills/trpc-group/trpc-agent-go/nano-pdf.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 | $0.00017 | $0.00239 |
| Opus 5 | $0.00009 | $0.00120 |
| Sonnet 5 | $0.00003 | $0.00048 |
| Haiku 4.5 | $0.00002 | $0.00024 |
Grade A, and why
nano-pdf 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 9d 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.
This is a copy
91% identical to nano-pdf — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
nano-pdf
Use nano-pdf to apply edits to a specific page in a PDF using a natural-language instruction.
Quick start
nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle"
Notes:
- Page numbers are 0-based or 1-based depending on the tool’s version/config; if the result looks off by one, retry with the other.
- Always sanity-check the output PDF before sending it 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.
- 9d ago First seen · 39 lines · 17 tokens per session scan A 28c360b5b915
nano-pdf is a skill published in the GitHub repository trpc-group/trpc-agent-go (1,770 stars, last pushed today), licensed Apache-2.0. It adds 17 tokens to every session and 239 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to nano-pdf, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…
nano-pdf
Edit PDFs with natural-language instructions using the nano-pdf CLI.
liteparse
Use this skill when the user asks to parse, perform multi-format document conversion or spatially extract text from an unstructured file (PDF, DOCX, PPTX, XLSX, images, etc.) locally without cloud dependencies.
meta-pdf-intelligence
Use this meta-skill instead of answering directly when the user needs PDF analysis, pasted PDF excerpt analysis, digesting, comparison, or question answering that benefits from multi-skill orchestration across PDF extraction, summarization, cross-document synthesis, traceable evidence indexing, and memory capture.
meta-research-to-slide-deck
Use this meta-skill instead of answering directly when the user needs a researched presentation, leadership briefing, competitive analysis deck, or source-backed slide outline that benefits from multi-skill orchestration across search, source curation, synthesis, slides, and document export.
meta-multi-format-export-pack
From one piece of source content, render four deliverables: .docx report, .pptx slides, .xlsx data, and an HTML/PDF public version.