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 algorand-devrel/algorand-agent-skills --skill algorand-x402-pythongit clone --depth 1 https://github.com/algorand-devrel/algorand-agent-skillsWrote 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/algorand-devrel/algorand-agent-skills/algorand-x402-python)<a href="https://agentmods.dev/skills/algorand-devrel/algorand-agent-skills/algorand-x402-python"><img src="https://agentmods.dev/badge/skills/algorand-devrel/algorand-agent-skills/algorand-x402-python.svg" alt="Measured on agentmods" 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 | $0.00082 | $0.01729 |
| Opus 5 | $0.00041 | $0.00864 |
| Sonnet 5 | $0.00016 | $0.00346 |
| Haiku 4.5 | $0.00008 | $0.00173 |
Grade A, and why
algorand-x402-python 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 8d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
x402 on Algorand - Python
Build x402 HTTP-native payment applications on Algorand with Python. Use the reference files below for detailed guidance on each component.
Python Quick Start
The x402-avm package on PyPI bundles core protocol, AVM mechanism, HTTP clients, and server middleware. Pick the extras you need:
# AVM only (no HTTP client/server)
pip install "x402-avm[avm]"
# Server middleware (pick one)
pip install "x402-avm[fastapi,avm]" # FastAPI async
pip install "x402-avm[flask,avm]" # Flask sync
# HTTP clients (pick one)
pip install "x402-avm[httpx,avm]" # Async with httpx
pip install "x402-avm[requests,avm]" # Sync with requests
# Bazaar discovery extension
pip install "x402-avm[extensions,avm]"
# Everything
pip install "x402-avm[all]"
Distribution name is
x402-avmbut the import root isx402(notx402_avm).
Register AVM Scheme
Every component registers the AVM exact scheme unconditionally — no environment variable guards:
# Client
from x402 import x402Client
from x402.mechanisms.avm.exact import ExactAvmScheme
client = x402Client()
client.register("algorand:*", ExactAvmScheme(signer=my_signer))
# Server
from x402.server import x402ResourceServer
from x402.mechanisms.avm.exact import ExactAvmServerScheme
server = x402ResourceServer()
server.register("algorand:*", ExactAvmServerScheme())
# Facilitator
from x402 import x402Facilitator
from x402.mechanisms.avm.exact import ExactAvmFacilitatorScheme
facilitator = x402Facilitator()
facilitator.register("algorand:*", ExactAvmFacilitatorScheme(signer=my_signer))
The register_exact_avm_client/server/facilitator helpers from x402.mechanisms.avm.exact are also valid.
Python algosdk Encoding
Python algosdk's msgpack_decode() expects base64 strings, msgpack_encode() returns base64 strings. Boundary conversion: msgpack_decode(base64.b64encode(raw_bytes).decode()) / base64.b64decode(msgpack_encode(obj)).
Reference Guide
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/create-python-x402-client-examples.md 12 KB
- references/create-python-x402-client-reference.md 12 KB
- references/create-python-x402-client.md 7.0 KB
- references/create-python-x402-facilitator-examples.md 27 KB
- references/create-python-x402-facilitator-reference.md 21 KB
- references/create-python-x402-facilitator.md 16 KB
- references/create-python-x402-server-examples.md 20 KB
- references/create-python-x402-server-reference.md 12 KB
- references/create-python-x402-server.md 7.2 KB
- references/explain-algorand-x402-python-examples.md 17 KB
- references/explain-algorand-x402-python-reference.md 14 KB
- references/explain-algorand-x402-python.md 6.7 KB
- references/use-python-x402-core-avm-examples.md 15 KB
- references/use-python-x402-core-avm-reference.md 11 KB
- references/use-python-x402-core-avm.md 6.0 KB
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.
- 8d ago First seen · 135 lines · 82 tokens per session scan A 66af0a9de460
algorand-x402-python is a skill published in the GitHub repository algorand-devrel/algorand-agent-skills (33 stars, last pushed 21d ago), licensed MIT. It adds 82 tokens to every session and 1,729 once invoked, about $0.0004 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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