Atomic Agent is a local-first AI agent that runs its control loop and state on a user's machine while using local or cloud models. It drives browsers, edits files, runs approved commands, remembers context, schedules follow-ups, and connects to external tools, with the catalogue providing skills for its use.
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 AtomicBot-ai/atomic-agent --skill pdfgit clone --depth 1 https://github.com/AtomicBot-ai/atomic-agentWrote 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/atomicbot-ai/atomic-agent/pdf)<a href="https://agentmods.dev/skills/atomicbot-ai/atomic-agent/pdf"><img src="https://agentmods.dev/badge/skills/atomicbot-ai/atomic-agent/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/atomicbot-ai/atomic-agent/pdf"><img src="https://agentmods.dev/badge/skills/atomicbot-ai/atomic-agent/pdf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.01013 |
| Opus 5.5 | $0.00022 | $0.00405 |
| Sonnet 5.5 | $0.00011 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
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 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Process PDF files from the terminal. Reading PDF text is best done with the
built-in os.fs.read_document tool (pure-JS, no install). Reach for the CLIs
below only for structural operations: merge, split, page extraction,
PDF↔image rendering, and OCR.
Tooling:
qpdf— merge / split / linearize / encrypt (pure structural ops).poppler—pdfinfo,pdftotext,pdftoppm,pdfimages(inspect + render).ocrmypdf— add a searchable text layer to scanned PDFs (optional).
Setup health check (run first, every session)
Verify with one solo step:
[{ "tool": "os.shell.run", "args": { "cmd": "pdfinfo", "args": ["-v"] } }]
Outcome map:
exit 0+ version →popplerpresent, proceed.- stderr
command not found→ enter Setup playbook → "tools missing".
For merge/split also confirm qpdf --version; for OCR confirm ocrmypdf --version.
Setup playbook (when prerequisites are missing)
OFFER concrete help and EXECUTE the fix yourself — do not dump docs on the user.
tools missing
Reply (solo reply step):
"The PDF utilities are not installed. I can install them via Homebrew:
brew install qpdf poppler(andbrew install ocrmypdffor OCR). Install them?"
On yes:
[{ "tool": "os.shell.run", "args": { "cmd": "brew", "args": ["install", "qpdf", "poppler"] } }]
On Linux use apt-get install qpdf poppler-utils ocrmypdf. If brew itself is
missing, point the user at https://brew.sh/ and stop.
When to use
- "Merge these PDFs", "split pages 3-7", "extract text from this PDF".
- "Convert PDF to images" / "make a PDF from these PNGs".
- "OCR this scanned PDF so it's searchable".
When NOT to use
- Simple text extraction for reading — use
os.fs.read_document(no install). - Editing PDF content/layout — out of scope; guide the user to a PDF editor.
- Filling AcroForm fields programmatically — not covered on v1.
Common operations
All examples invoke os.shell.run. Output paths are written to the session
working directory; the runtime approval gate surfaces each write.
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 · 92 lines · 56 tokens per session scan A d1e2f0cf7afd
pdf is a skill published in the GitHub repository AtomicBot-ai/atomic-agent (3,431 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 1,013 once invoked, about $0.0002 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.
Other skills, from other repositories
hatch3r-api-spec
Generates and validates OpenAPI specifications from codebase. Covers endpoint design, schema validation, and documentation generation.
api2pdf-automation
Automate Api2pdf tasks via Rube MCP (Composio). Always search tools first for current schemas.
boldsign-automation
Automate Boldsign tasks via Rube MCP (Composio). Always search tools first for current schemas.
api2pdf-automation
Automate Api2pdf tasks via Rube MCP (Composio). Always search tools first for current schemas.
pdf-extract
Extract plain text from a PDF document.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.