Borrowing it
Nothing to install: this file belongs to jkanselaar/python-code-validator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jkanselaar/python-code-validator/main/AGENTS.mdgit clone --depth 1 https://github.com/jkanselaar/python-code-validatorWrote 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/instructions/jkanselaar/python-code-validator/agents-md)<a href="https://agentmods.dev/instructions/jkanselaar/python-code-validator/agents-md"><img src="https://agentmods.dev/badge/instructions/jkanselaar/python-code-validator/agents-md/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/instructions/jkanselaar/python-code-validator/agents-md"><img src="https://agentmods.dev/badge/instructions/jkanselaar/python-code-validator/agents-md.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 | $0.00654 | $0.00654 |
| Opus 5.5 | $0.00262 | $0.00262 |
| Sonnet 5.5 | $0.00131 | $0.00131 |
| Haiku 4.5 | $0.00065 | $0.00065 |
Grade A, and why
python-code-validator AGENTS.md scanned grade A with 1 finding 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.
curl -sf https://api.statemind.ai/v1/client -o validate.py Copies of this mod
1 near-identical copy found in the catalogue:
- python-code-validator GEMINI.md — 91% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python you write here gets checked
Use this service the way a compiler is used: not once at the end, but at every step, and never present code that has not come back accepted.
Say what the code is for, before you write it
Turn the request into examples first — assert bitcount(127) == 7, or doctest
lines — and pass them in options.examples on every call from then on. This is
the only check that can fail code which parses, lints, type-checks and runs, and
generated code almost never carries examples of its own: the agent that was
asked knows what was asked, and nothing else does.
While you write
- After every draft or edit of a file, call
validate_python. It parses, lints (ruff), type-checks (mypy), applies an AST security policy and scans for credentials, without running anything. - When a function is finished, call
execute_pythonwith your examples. It runs the code in a throwaway container with no network and a read-only filesystem, and checks the examples against what it actually does. Call it again when what the code does has changed — not after renaming a variable, reformatting, or editing a comment, and never twice on the same source: the answer cannot differ, and running it costs the caller ten times what a check costs. repair_pythonis the same repair without running anything: use it when the code is not meant to run, or when the checks alone already name the problem.
When something comes back wrong
- Read
fixed_codefirst. When it is filled in, the service already found and ran a program that satisfies your examples — take it. That is a proven fix, not a suggestion, and rewriting the algorithm yourself instead throws the proof away. - A null
fixed_codemeans nothing could be proven, not that the call failed. Then rewrite it yourself, using the diagnostics as evidence: apython:example-mismatchsays which example failed and what the code gave instead, which is the fact your next attempt has to account for. - Validate again after the rewrite. An unvalidated fix is a new draft.
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 · 58 lines · 654 tokens per session scan A e7a1ded3232c
python-code-validator AGENTS.md is an instructions file published in the GitHub repository jkanselaar/python-code-validator (0 stars, last pushed 2mo ago), licensed MIT. It adds 654 tokens to every session, about $0.0026 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).