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 InternScience/MolClaw --skill molclaw-file-transfergit clone --depth 1 https://github.com/InternScience/MolClawWrote 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/internscience/molclaw/molclaw-file-transfer)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-file-transfer"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-file-transfer/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/internscience/molclaw/molclaw-file-transfer"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-file-transfer.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 | $0.00066 | $0.00798 |
| Opus 5.5 | $0.00026 | $0.00319 |
| Sonnet 5.5 | $0.00013 | $0.00160 |
| Haiku 4.5 | $0.00007 | $0.00080 |
Grade A, and why
molclaw-file-transfer 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
File Transfer
Invocation
Use the exact tool name and parameter schema exposed by the current harness. Tool names in SDK examples below are raw MCP names, not model-facing aliases. Local files must be uploaded with molclaw-file-transfer before a server tool can use them. Repair PDB inputs first when the workflow requires it.
When the client-exposed schema includes local_file_path, prefer this client-side form for files already present in the current local workspace. For upload, call base64_to_server_file with file_name and local_file_path and omit file_base64_string. For download, call server_file_to_base64 with the server's file_path and a new, unique filename in local_file_path; a successful result returns its absolute local path. Reuse that returned path for later local operations or uploads. An absolute path inside the allowed workspace is also accepted; use it for a file created by a local tool. Do not copy file bytes through the model output. The client validates the path and performs the encoding or decoding; the MCP server still uses its original Base64 interface. If the exposed schema lacks local_file_path, use the original Base64 form below. Do not supply both upload forms in one call.
The Python examples below call the public SCP service directly and demonstrate its original Base64 interface. They do not show the client-side path extension.
1. Transfer local file to SCP server
Example code:
import base64
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
print("connection failed")
return
## Convert local file to base64 string
local_file = "/path/a.txt" #Input local file path
with open(local_file, "rb") as f:
file_content = f.read()
file_base64_string = base64.b64encode(file_content).decode('utf-8')
## Call tool base64_to_server_file to save base64 string as server file
response = await client.session.call_tool(
"base64_to_server_file",
arguments={
"file_name": "a.txt",
"file_base64_string": file_base64_string
}
)
result = client.parse_result(response)
save_file = result["save_file"] #Server file path after transfer
await client.disconnect()
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 Changed · +3 lines · +45 tokens per session 1bae5847555b
- 1mo ago First seen · 79 lines · 21 tokens per session scan A db0a53acdd41
molclaw-file-transfer is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 798 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 2026-08-30.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…