ppxai AGENTS.md

ppxai AGENTS.md is an instructions file for Codex, OpenCode from rcconsult/ppxai. It costs 11,974 tokens per session, scanned C, original, MIT.

A project-specific instruction file for ppxai, a terminal application that lets users chat with several AI services, including local models.

In plain words
What is it for?
Guiding changes to ppxai, including its provider integrations, core engine, tool system, and tests.
Why use it?
It gives coding agents the project's language, architecture, testing, coding-style, and tool-use rules.

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/rcconsult/ppxai/agents-md
Clone the repo
git clone --depth 1 https://github.com/rcconsult/ppxai

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for ppxai AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/rcconsult/ppxai/agents-md.svg)](https://agentmods.dev/instructions/rcconsult/ppxai/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/rcconsult/ppxai/agents-md"><img src="https://agentmods.dev/badge/instructions/rcconsult/ppxai/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 11,974 This file is loaded in full into every session.
When invoked 11,974 The same file — it is already loaded in full.
Security scan C 1 finding. 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 $0.11974 $0.11974
Opus 5 $0.05987 $0.05987
Sonnet 5 $0.02395 $0.02395
Haiku 4.5 $0.01197 $0.01197

Measured 3d ago against content hash fb49cc05e5c0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

ppxai AGENTS.md scanned grade C 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 3d 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.

Reaches for credential fileshighPrivilege escalation

SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.

# - claim_without_action: safety guardrails refuse /etc/shadow
AGENTS.md · 583 lines

How it starts

The opening of the file, as written. The whole thing — 583 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Global Preferences

Code Style

  • Python 3.10+ with type hints
  • Use dataclasses for data structures
  • Async/await for I/O operations
  • pytest for testing

Tool Usage

  • Prefer apply_patch / replace_block over write_file for existing files
  • Use read_file instead of cat / type shell commands
  • Execute tools directly - don't explain what you're about to do
  • Report results briefly after tool execution

Project: ppxai

ppxai is a terminal-based UI application for interacting with multiple AI providers (Perplexity AI, OpenAI, Gemini, local models via Ollama/vLLM).

Architecture

  • ppxai/engine/ - Core business logic (no UI dependencies)
  • ppxai/engine/providers/ - Provider implementations:
    • openai_native.py - Native OpenAI (GPT-5.x, o-series, Codex via Responses API)
    • gemini.py - Native Gemini (google-genai SDK; native function_call/function_response tool threading)
    • openai_compat.py - OpenAI-compatible (Perplexity, local/vLLM, custom)
  • ppxai/engine/model_profiles.py - Per-model behavioral profiles (tool calling, API routing)
  • ppxai/engine/tools/ - Tool system with builtins + brace-counting JSON parser
    • network_policy.py - AC-2 egress allowlist (fail-closed, https-only, SSRF guard)
    • filesystem_policy.py - filesystem seal (per-run read/write jail; tools.agent.sandbox)
    • agent_scoped_tools.py - AC-1 per-run tool allowlist (ScopedToolManager chokepoint)
  • ppxai/engine/agent_runs.py - agent-platform run registry (AgentRunRegistry: lifecycle, events.jsonl, budgets, consent/ack/resume)
  • ppxai/engine/agent_spec.py / agent_skill.py - --spec / --skill loaders for the /task tier
  • ppxai/server/ - HTTP/SSE server for IDE integration
    • routes/agent_v1.py - /v1/agent/{run,task,runs,...} (agent platform); routes/oneshot.py - /v1/oneshot gateway
  • ppxai/commands/ - Slash command handlers
  • ppxai/config/ - Configuration system
  • vscode-extension/ - TypeScript VSCode extension (bundled via esbuild; taskController.ts for the /task family)

Read the full file on GitHub · 583 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. 3d ago First seen · 583 lines · 11,974 tokens per session scan C fb49cc05e5c0

Subscribe to this mod's changes

ppxai AGENTS.md is an instructions file published in the GitHub repository rcconsult/ppxai (5 stars, last pushed 3d ago), licensed MIT. It adds 11,974 tokens to every session, about $0.0599 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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