What is sequential-presets?

A guide and implementation aid for creating ordered marketing filter presets in DataSift, a real-estate lead-management system.

It helps design, discover, customize, and update presets for first-to-market or bulk-data campaigns, including adding exclusions such as properties already sold.

It helps organize leads through stages such as skip tracing, texting, calling, mailing, and deeper prospecting instead of managing each filter workflow separately.

sequential-presets — Skill by DataSift-Ty-Personal/SiftStack

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.

Any agent

npx skills add DataSift-Ty-Personal/SiftStack --skill sequential-presets

Clone the repo

git clone --depth 1 https://github.com/DataSift-Ty-Personal/SiftStack

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin sequential-presets — /plugin install sequential-presets after adding the marketplace above.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/sequential-presets/github.svg)](https://agentmods.dev/skills/datasift-ty-personal/siftstack/sequential-presets)

Your own site

<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/sequential-presets"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/sequential-presets/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/datasift-ty-personal/siftstack/sequential-presets"><img src="https://agentmods.dev/badge/skills/datasift-ty-personal/siftstack/sequential-presets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,108 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00095	$0.03108
Opus 5.5	$0.00038	$0.01243
Sonnet 5.5	$0.00019	$0.00622
Haiku 4.5	$0.00010	$0.00311

Measured 2mo ago against content hash 4bcac1d29570, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sequential-presets 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/datasift_core.py, scripts/manage_presets.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/sequential-presets/SKILL.md · 255 lines

How it starts

The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sequential Presets Skill

This skill makes you a world-class preset consultant AND implementor. You guide users through a consultative process to design optimized sequential marketing filter presets in DataSift, then help them build each preset step-by-step in the DataSift UI.

Execution Mode Detection

Before starting, detect the execution environment:

Check 1: Does scripts/manage_presets.py exist in this skill's directory? Check 2: Is Playwright available? Run: python -c "from playwright.sync_api import sync_playwright; print('OK')" Check 3: Are credentials set? Check for DATASIFT_EMAIL and DATASIFT_PASSWORD in .env or environment.

Automated Mode (Claude Code CLI — all 3 checks pass)

Run preset management directly:

# Discover all existing presets
python scripts/manage_presets.py --discover

# Add Sold exclusion to all 21 presets
python scripts/manage_presets.py --add-sold-exclusion

# Full workflow: discover + update all
python scripts/manage_presets.py --all

The script handles login, filter panel navigation, preset discovery, and Sold exclusion updates automatically using Playwright browser automation. After running, proceed to the consultative design section below.

Manual Mode (Co-Work or no Playwright — any check fails)

Follow the step-by-step UI walkthrough instructions below. Claude will guide you through each click in the DataSift interface.

Core Concepts

Niche vs. Bulk Sequential Marketing

The primary distinction is the data source and marketing approach. Determine which strategy the user is employing first -- this dictates the entire preset structure.

Aspect	Niche Sequential	Bulk Sequential
Data Type	First-to-market/Tier 1 (courthouse data)	Tier 2/3 (stacked lists, AI-enriched)
Calling Method	Manual click-to-dial	Multi-line power dialer
Urgency	High	Low to Medium
Primary Tag	`Courthouse Data`	`dataflik`
Preset Count	12 presets (00-11)	9 presets (00-08)
Folder Name	`00 Niche Sequential Marketing`	`01. Bulk Sequential Marketing`

Read the full file on GitHub · 255 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

2mo ago First seen · 255 lines · 95 tokens per session scan A 4bcac1d29570

Subscribe to this mod's changes

sequential-presets is a skill published in the GitHub repository DataSift-Ty-Personal/SiftStack (23 stars, last pushed 23d ago), licensed MIT. It adds 95 tokens to every session and 3,108 once invoked, about $0.0004 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.

Marketing and SEO

sequential-presets next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
sequential-presetsDataSift-Ty-Personal/SiftStack	Skill	23	23d ago	MIT	A	95	Claude Code, Codex
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code