pythinker-cli AGENTS.md

Repository instructions for developing Pythinker CLI, a Python command-line coding agent with integrations for editors, tools, background work, and language-model providers.

In plain words
What is it for?
Use them when adding or modifying Pythinker CLI features, including its shell interface, editor integration, tool loading, subagents, skills, web features, or authentication.
Why use it?
They give an agent the project’s goals, required development checks, expected inputs and outputs, side effects, failure handling, and edge cases before code is changed.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/pymodel/pythinker-cli/agents-md
Clone the repo
git clone --depth 1 https://github.com/PyModel/pythinker-cli

Made for: Codex, OpenCode.

Per session 9,082 This file is loaded in full into every session.
When invoked 9,082 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.09082 $0.09082
Opus 5 $0.04541 $0.04541
Sonnet 5 $0.01816 $0.01816
Haiku 4.5 $0.00908 $0.00908

Measured yesterday against content hash ed6c494a51f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pythinker-cli AGENTS.md 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 yesterday.

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.

AGENTS.md · 596 lines

How it starts

The opening of the file, as written. The whole thing — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pythinker CLI Agent Instructions

This file is the root guidance for AI agents working in this repository. It is injected into Pythinker sessions via PYTHINKER_AGENTS_MD; keep it durable, portable, and focused on rules that should apply across many tasks.

Local-only instructions

If AGENTS.local exists at the repository root, read it after this file for machine-specific or private local instructions. AGENTS.local is intentionally gitignored; do not commit it or copy its contents into tracked files. Local instructions may add workflow details, but they must not weaken or override this repository's non-negotiable rules.

Mission

Pythinker CLI is a Python CLI agent for software engineering workflows. It supports an interactive shell UI, ACP server mode for IDE integrations, MCP tool loading, background work, subagents, skills, web/visualization UIs, and multi-provider LLM authentication.

Feature Development Standard

Build every feature as production code, not a happy-path demo. Before implementing, answer: what the feature does, who or what calls it, its inputs, its outputs and side effects, how it can fail, what happens on failure, which edge cases apply, and what test proves it works. If requirements are ambiguous, make the safest reasonable assumption and document it — only block when the missing detail would change the implementation.

Handle the failure and edge cases, not just the happy path: missing / empty / invalid / malformed input, unauthorized access, expired tokens, timeouts and network errors, partial success, concurrent or duplicate requests, rate limits, large payloads, stale cache, missing records, retry exhaustion, cancellation, and rollback/cleanup failure. Never silently ignore an unexpected state.

Make errors explicit: typed or categorized, logged with actionable context, recoverable where possible, and safe to surface — never leaking secrets, tokens, or stack traces. No bare except/catch that swallows the error. For feature work this restates the Failure truthfulness contract and C01–C15 tripwires below; ship the matching tests and verification with the feature.

Read the full file on GitHub · 596 lines

Changes

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.

  1. yesterday First seen · 596 lines · 9,082 tokens per session scan A ed6c494a51f5

Subscribe to this mod's changes

pythinker-cli AGENTS.md is an instructions file published in the GitHub repository PyModel/pythinker-cli (20 stars, last pushed 4d ago), licensed Apache-2.0. It adds 9,082 tokens to every session, about $0.0454 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.