participation-warmup-planner

participation-warmup-planner is a skill for Claude Code from iamwaqargulzar/Marketing-Agent-OS. It costs 167 tokens per session (869 once invoked), scanned A, a copy of impeccable, Apache-2.0.

A planning skill for preparing accounts and communities before promoting something in them. It covers participation history, entry incentives, and member stages for communities such as Discord.

In plain words
What is it for?
Use it to set account-age or participation expectations, plan a community entry path, and design incentives for moving members through different stages.
Why use it?
It helps avoid promoting too early or appearing unfamiliar and unwelcome in a community. It also separates estimates from measured or user-provided evidence.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the marketing-agent-os plugin — 173 skills shipped together

Good fit Use it to set account-age or participation expectations, plan a community entry path, and design incentives for moving members through different stages.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner
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 iamwaqargulzar/Marketing-Agent-OS --skill participation-warmup-planner
Clone the repo
git clone --depth 1 https://github.com/iamwaqargulzar/Marketing-Agent-OS

Made for: Claude Code.

Or install marketing-agent-os, the plugin that ships this one along with the rest of its 173 skills.

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 participation-warmup-planner

README.md
[![agentmods](https://agentmods.dev/badge/skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner/github.svg)](https://agentmods.dev/skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner)
Your own site
<a href="https://agentmods.dev/skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner"><img src="https://agentmods.dev/badge/skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner/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.

agentmods 80×15 button for participation-warmup-planner

Your own site · 80×15
<a href="https://agentmods.dev/skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner"><img src="https://agentmods.dev/badge/skills/iamwaqargulzar/marketing-agent-os/participation-warmup-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 167 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 869 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.
Origin 61% copy Near-identical to another mod 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 $0.00167 $0.00869
Opus 5 $0.00084 $0.00434
Sonnet 5 $0.00033 $0.00174
Haiku 4.5 $0.00017 $0.00087

Measured 7d ago against content hash 865946a38967, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

participation-warmup-planner 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 7d 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.

Origin

This is a copy

61% identical to impeccable — 317 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/participation-warmup-planner/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Participation Warmup Planner

Quick Start

Use this skill for participation warmup planner. Start from the user’s concrete objective and available evidence; do not substitute generic marketing advice for task-specific analysis.

Skill Contract

  • Reads: user-provided context; relevant project files; .agents/product-marketing.md when present; approved public or connected data sources.
  • Writes: recommendations and artifacts in the response by default. Persistent file/account changes require explicit request or authorization.
  • Evidence: label consequential claims as measured, user-provided, calculated, estimated, or proxy. Never upgrade uncertainty silently.
  • Side effects: do not publish, send, spend, delete, mutate accounts, or persist registry truth without user authorization.
  • Freshness: verify current platform rules, search eligibility, ad policies, model/tool capabilities, laws, pricing, and other time-sensitive claims before acting.

Instructions

  1. Define the exact participation warmup planner objective, audience/scope, constraints and success metric before recommending action.
  2. Load shared product-marketing context when it materially changes the answer; ask only for missing facts that block a decision.
  3. Collect the minimum evidence needed for participation warmup planner. Distinguish direct observations from assumptions and proxies.
  4. Execute the participation warmup planner analysis or artifact using the domain checklist below; prefer specific outputs over generic best-practice lists.
  5. Prioritize actions by impact, confidence, effort and dependency. Identify what would falsify important assumptions.
  6. For external side effects, publishing, sending, spend changes, account changes or persistent writes, obtain authorization first.
  7. Finish with decision-ready output, evidence labels, open loops, and no more than three next-best skills.

Domain Checklist

  • Channel role
  • Audience behavior
  • Native format norms
  • Content/participation mix
  • Community response rules
  • Distribution loop
  • Measurement
  • Brand/safety escalation

Read the full file on GitHub · 75 lines

Files

What ships with it

4 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.

  1. 7d ago First seen · 75 lines · 167 tokens per session scan A 865946a38967

Subscribe to this mod's changes

participation-warmup-planner is a skill published in the GitHub repository iamwaqargulzar/Marketing-Agent-OS (5 stars, last pushed 8d ago), licensed Apache-2.0. It adds 167 tokens to every session and 869 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 61% identical to impeccable, differing in 317 lines, and is treated as a copy.

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