macro-pickle CLAUDE.md

macro-pickle CLAUDE.md is an instructions file for coding agents from MrJPTech/macro-pickle. It costs 2,817 tokens per session, scanned A, original, MIT.

A set of project instructions for macro-pickle, a local toolkit that creates image and video prompts and saves its results as files.

In plain words
What is it for?
Use it when working on macro-pickle's TypeScript scripts, desktop server, prompt engine, brand profiles, image or video generation, and optional text recognition.
Why use it?
It gives coding agents the project's assumptions, tools, file locations, and normal commands so they can make compatible changes.

Instructions file

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/mrjptech/macro-pickle/claude-md
Clone the repo
git clone --depth 1 https://github.com/MrJPTech/macro-pickle

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 macro-pickle CLAUDE.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/mrjptech/macro-pickle/claude-md.svg)](https://agentmods.dev/instructions/mrjptech/macro-pickle/claude-md)
Your own site
<a href="https://agentmods.dev/instructions/mrjptech/macro-pickle/claude-md"><img src="https://agentmods.dev/badge/instructions/mrjptech/macro-pickle/claude-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 2,817 This file is loaded in full into every session.
When invoked 2,817 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 $0.02817 $0.02817
Opus 5 $0.01409 $0.01409
Sonnet 5 $0.00563 $0.00563
Haiku 4.5 $0.00282 $0.00282

Measured 5d ago against content hash ec6cb1b45ab3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

macro-pickle 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 5d 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 · 169 lines

How it starts

The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.

macro-pickle — AI Creative Tooling

Project Overview

Local, database-free AI creative toolkit — image & video generation plus a typed, brand-aware Prompt Engine, driven through Claude (CLI scripts, a desktop MCP server, and Claude Code skills/commands). There is no database and no web app: every rail writes files to disk, and finalized prompts are exported as copy-paste markdown into a notes vault of your choosing.

Brand profiles and campaign prompt packs are intentionally NOT in this repo — it ships one worked example (quiet-desk) and stays brand-agnostic. Keep your own brands in a private directory and point MACRO_PICKLE_BRANDS_DIR at it.

Technology Stack

Layer Technology
Language / runtime TypeScript (strict, ESM) on Node via tsx
Image / video @google/genai ≥2.x (Gemini vision · Imagen · Veo · Nano Banana · Omni Flash video editing) · @fal-ai/client (FLUX et al. · Kling)
MCP @modelcontextprotocol/sdk (desktop macro-pickle-images server)
Validation zod
Optional OCR sidecar Python + PaddleOCR (scripts/py/, opt-in)
Package Manager pnpm

Common Commands

pnpm install
cp .env.example .env.local      # add GOOGLE_API_KEY + FAL_KEY
pnpm lint                       # tsc --noEmit (type-check the whole toolkit)

# Prompts
pnpm prompt --brands            # list brand profiles
pnpm prompt --image --brand quiet-desk --json '{"subject":"lamp hero shot"}' --save

# Image generation
pnpm img "neon pickle mascot"   # Imagen 4.0 → MACRO_PICKLE_EXPORT_DIR
pnpm fal --model flux-pro "…"   # fal.ai (FLUX / Recraft / Ideogram / SD3.5)
pnpm nano                       # Nano Banana face-lock from reference photos

# Video generation
pnpm veo                        # Google Veo (t2v / i2v / --refs ASSET lock)
pnpm kling                      # Kling via fal.ai (i2v / t2v / start+end)

# Video EDITING — Gemini Omni Flash (PAID, ~$0.10/s output; no free tier)
pnpm omni --video ref.mp4 --prompt "…"    # manipulate a reference video in plain language
pnpm omni --continue <id> --prompt "…"    # chain conversational refinements (multi-turn)
pnpm omni --prompt "…" --dry-run          # prompt + cost estimate, no API call

# Media pipeline (two-stage: idea → reference frames → curate → video)
pnpm scene:new <slug>           # scaffold a scene.json
pnpm scene:refs <slug>          # Stage 1: generate reference frames
pnpm scene:select <slug> <ids>  # curate the keepers
pnpm scene:video <slug>         # Stage 2: selected frames → video (scene.json "model": "omni-flash" routes via Omni, paid)

# Store batch clips (gen5 product refs → clips), spend-gated + resume-safe
pnpm rank-skus --store <dir> --top 8 --ids   # rank a store's SKUs by sales → top-N ids
pnpm gen-clips --store <dir> --dry-run       # generic batch (Veo free + Kling paid); dry-run first
pnpm gen-clips --store <dir> --yes           # live paid run (ENFORCED: --yes + --max-spend cap; existing clips skipped)
#   per-store tuning lives in content/clip-scenes/<store>.json (see example-store.json)

# Vision / UGC
pnpm describe                   # Gemini: product OCR + in-use scene rec (--paddle for sidecar)
pnpm analyze-video              # describe a reference clip
pnpm mcp:image                  # run the desktop MCP server

Read the full file on GitHub · 169 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. 5d ago First seen · 169 lines · 2,817 tokens per session scan A ec6cb1b45ab3

Subscribe to this mod's changes

macro-pickle CLAUDE.md is an instructions file published in the GitHub repository MrJPTech/macro-pickle (2 stars, last pushed 1mo ago), licensed MIT. It adds 2,817 tokens to every session, about $0.0141 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 2026-08-31.

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