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 agentmods add instructions/iflow-ai/niopd/agents-mdgit clone --depth 1 https://github.com/iflow-ai/NioPDWrote 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/iflow-ai/niopd/agents-md)<a href="https://agentmods.dev/instructions/iflow-ai/niopd/agents-md"><img src="https://agentmods.dev/badge/instructions/iflow-ai/niopd/agents-md.svg" alt="Measured on agentmods" 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 | $0.01443 | $0.01443 |
| Opus 5 | $0.00722 | $0.00722 |
| Sonnet 5 | $0.00289 | $0.00289 |
| Haiku 4.5 | $0.00144 | $0.00144 |
Grade A, and why
NioPD 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 6d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md for the NioPD System
This document provides guidance for AI agents working with the NioPD system.
1. System Overview
NioPD is an AI-powered assistant system for Product Managers. It is designed to automate and assist with the core PM workflow, from defining high-level initiatives to tracking success metrics.
The system is file-based and command-driven. All data is stored in markdown files within the niopd-workspace/ directory, and the AI's behavior is guided by a series of command prompts and agent definitions.
2. Core Philosophy: Agent-Driven Synthesis
NioPD relies on specialized agents to perform complex synthesis tasks. Unlike a general-purpose chatbot, NioPD uses agents to transform one type of document into another (e.g., turning feedback into a PRD, or turning initiatives into a roadmap).
Your primary role as an agent is to follow the instructions defined in the commands/niopd/ and agents/niopd/ directories to execute these transformations accurately.
3. Available Agents
🤖 competitor-analyzer
- Purpose: To analyze a competitor's website and produce a structured summary of their product, pricing, and positioning.
- Input: A single URL for a competitor's homepage.
- Output: A structured markdown competitor analysis report with core value proposition, features, target audience, and pricing model.
🤖 data-analyst
- Purpose: To analyze structured data from a file (like CSV) and answer natural language questions about that data.
- Input: A file containing structured data and a natural language query.
- Output: A markdown data analysis report with direct answers and supporting methodology.
🤖 feedback-synthesizer
- Purpose: To process raw user feedback and identify key themes, pain points, and feature requests.
- Input: A file containing user feedback.
- Output: A structured markdown summary of the feedback with themes, pain points, and user quotes.
🤖 interview-summarizer
- Purpose: To read user interview transcripts and extract critical insights, including user needs, pain points, and direct quotes.
- Input: A text file containing the full transcript of a user interview.
- Output: A markdown interview summary with key takeaways, core themes, and verbatim quotes.
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.
- 6d ago First seen · 100 lines · 1,443 tokens per session scan A 5be062e55027
NioPD AGENTS.md is an instructions file published in the GitHub repository iflow-ai/NioPD (118 stars, last pushed 8mo ago), licensed MIT. It adds 1,443 tokens to every session, about $0.0072 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.
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).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
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.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
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.