AgenticProductTeam: Instructions file for Claude Code

CLAUDE.md

AgenticProductTeam CLAUDE.md is an instructions file for Claude Code from Jason-Cyr/AgenticProductTeam. It costs 2,057 tokens per session, scanned A, original, MIT.

Instructions for using AgenticPod, a product-development pipeline with four stages: product management, research, design, and engineering. It creates structured Markdown and JSON files for each project.

In plain words
What is it for?
Use them to create a project, run a specific stage, and work with the generated files in the project’s artifacts folder.
Why use it?
They tell a coding agent how to start projects, run stages, and use the project’s functions correctly. This avoids guessing how the pipeline or its scripts work.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions Claude Code.

This is Jason-Cyr/AgenticProductTeam's own configuration. It tells Claude Code how to work on AgenticProductTeam itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything AgenticProductTeam configures →

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is import { runStage } from '../src/index.js';.

Reuse

Borrowing it

Nothing to install: this file belongs to Jason-Cyr/AgenticProductTeam. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Jason-Cyr/AgenticProductTeam/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/Jason-Cyr/AgenticProductTeam

Made for: Claude Code.

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Per session 2,057 This file is loaded in full into every session.
When invoked 2,057 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.02057 $0.02057
Opus 5.5 $0.00823 $0.00823
Sonnet 5.5 $0.00411 $0.00411
Haiku 4.5 $0.00206 $0.00206

Measured 2mo ago against content hash 318fa3b03d05, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

AgenticProductTeam CLAUDE.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 2mo 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.

CLAUDE.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AgenticPod — Claude Code Instructions

AgenticPod is an agentic product lifecycle accelerator. You (Claude Code) are the UI. Users interact with you conversationally, and you drive the pipeline by calling library functions.

How It Works

AgenticPod runs a staged pipeline across four agent roles (PM → Research → Design → Engineering), producing structured artifacts (markdown + json) in ./artifacts/<project_id>/. Each stage calls an LLM to generate content based on prior artifacts.

Calling AgenticPod Functions

Because this is an ESM project with top-level await, use script files in ./scripts/ rather than npx tsx -e. Write a short .ts script, run it with npx tsx scripts/your-script.ts, then delete it after.

Example pattern:

// scripts/run-stage.ts
import { runStage } from '../src/index.js';
const r = await runStage('snackquest', 'pm');
console.log(JSON.stringify(r, null, 2));
npx tsx scripts/run-stage.ts

Commands (what users will ask you to do)

Start a new project

When the user describes a product idea, init a project:

import { initProject } from '../src/index.js';
const r = await initProject('THE IDEA TEXT', 'optional-project-id');
console.log(JSON.stringify(r, null, 2));

Then show the user the generated idea.json and ask if they want to refine it before proceeding.

Run the next stage

Run one stage at a time. After each stage, present:

  1. What artifacts were created/updated
  2. A brief summary of the content
  3. Any quality issues
  4. Ask: "Ready to proceed to [next stage]?"
import { runStage } from '../src/index.js';
const r = await runStage('PROJECT_ID', 'STAGE');
console.log(JSON.stringify(r, null, 2));

Stages in order: pm → research → design → engineering

Check project status

import { getProjectStatus } from '../src/index.js';
console.log(JSON.stringify(getProjectStatus('PROJECT_ID'), null, 2));

Run quality checks

import { runQualityChecks } from '../src/index.js';
console.log(JSON.stringify(runQualityChecks('PROJECT_ID'), null, 2));

Read the full file on GitHub · 173 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. 2mo ago First seen · 173 lines · 2,057 tokens per session scan A 318fa3b03d05

Subscribe to this mod's changes

AgenticProductTeam CLAUDE.md is an instructions file published in the GitHub repository Jason-Cyr/AgenticProductTeam (20 stars, last pushed 8mo ago), licensed MIT. It adds 2,057 tokens to every session, about $0.0082 per session on Opus 5.5. 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.

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