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 ericrisco/rsc-harness --skill pythongit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/python)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/python"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/python/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/ericrisco/rsc-harness/python"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 289 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00094 | $0.04030 |
| Opus 5.5 | $0.00038 | $0.01612 |
| Sonnet 5.5 | $0.00019 | $0.00806 |
| Haiku 4.5 | $0.00009 | $0.00403 |
Grade A, and why
python scanned grade A with 2 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
return await asyncio.gather(*(fetch(i) for i in ids)) Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, shell=True) subprocess.run(["ls", path]) # list args, no shell=True How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modern Python at the language level
Write, review, modernize, type, and package Python that reads like a typed,
flat-control-flow, stdlib-first program whose dependencies and tooling all live in one
pyproject.toml. Types are part of the design, not decoration; the stdlib is large and you
reach for it before a dependency; correctness is ruff + a type checker + pytest in one gate.
Targets Python 3.12+ (floor) / 3.14 (current, released 7 Oct 2025): PEP 695 inline
type parameters (class Box[T]:, type Alias = ...), asyncio.TaskGroup, and — in 3.14 —
deferred annotation evaluation by default (PEP 649/749, no more from __future__ import annotations), PEP 750 template strings (t"..."), and compression.zstd. Tooling pins:
uv 0.11 (project + package manager), ruff 0.15 (lint + format), mypy 1.20
--strict (or Astral's ty, still preview — default to mypy), pytest 8.
Scope — what this skill delegates
- Building a FastAPI / ASGI service (routes, Pydantic models, SQLAlchemy, uvicorn) ->
fastapi. That skill owns the service shape; this one owns the language it is written in. - A pytest suite as the primary task — fixtures, parametrize matrices, mocking, coverage,
property-based testing ->
testing-py(this skill keeps only the baseline: a few tests soverify.shhas something to run, then hands off). - Language-agnostic threat modeling / authz / OWASP review ->
secure-coding(this skill keeps Python-specific safety: noeval/pickleof untrusted data,subprocesswithoutshell=True,secretsoverrandom). - Containerfile / CI pipeline / deploy mechanics ->
deployment(this skill ships only a uv-based CI note). - Another language ->
go,typescript,rust, etc. Django ORM/models/migrations ->django.
Python typing, async language semantics, and uv packaging live here, not in a separate skill — this skill is the canonical authority for the language substrate under any Python program.
What ships with it
6 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.
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 · 321 lines · 94 tokens per session scan A 6938dd0d15dc
python is a skill published in the GitHub repository ericrisco/rsc-harness (231 stars, last pushed today), licensed MIT. It adds 94 tokens to every session and 4,030 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
Python Patterns
Use this skill when writing or reviewing modern Python code and you want consistent patterns for typing, async, structure, packaging, and correctness (especially in services, scripts, CLIs, and data tooling).
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
python-testing
Guidelines for writing and running tests in the Agent Framework Python codebase. Use this when creating, modifying, or running tests.
python
Python development with ruff, mypy, pytest - TDD and type safety.
typescript
TypeScript strict mode with eslint and jest.
python-testing
A Python testing guide covering pytest, test coverage, and test-driven development (TDD), a method of writing a failing test before the code that makes it pass.