poppify-schedule-optimizer

poppify-schedule-optimizer is a skill for Claude Code from Poppify/poppify-claude-plugin. It costs 144 tokens per session (1,056 once invoked), scanned A, original, MIT.

A Poppify publishing skill that chooses when to post or schedule a rendered reel using an account's past engagement. Engagement measures how much people interact with published content.

In plain words
What is it for?
Use it when posting, scheduling, or choosing the best time for a reel to improve likely reach or engagement.
Why use it?
It replaces blind scheduling with recommendations based on the account's own day-and-time performance history, including sample size and timezone.

Skill for Claude Code

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

Part of the poppify plugin — 7 skills, 3 commands, 1 MCP server shipped together

Good fit Use it when posting, scheduling, or choosing the best time for a reel to improve likely reach or engagement.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/poppify/poppify-claude-plugin/poppify-schedule-optimizer
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 Poppify/poppify-claude-plugin --skill poppify-schedule-optimizer
Clone the repo
git clone --depth 1 https://github.com/Poppify/poppify-claude-plugin

Made for: Claude Code.

Or install poppify, the plugin that ships this one along with the rest of its 7 skills, 3 commands, 1 MCP server.

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 poppify-schedule-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/poppify/poppify-claude-plugin/poppify-schedule-optimizer.svg)](https://agentmods.dev/skills/poppify/poppify-claude-plugin/poppify-schedule-optimizer)
Your own site
<a href="https://agentmods.dev/skills/poppify/poppify-claude-plugin/poppify-schedule-optimizer"><img src="https://agentmods.dev/badge/skills/poppify/poppify-claude-plugin/poppify-schedule-optimizer.svg" alt="Measured on agentmods" height="20"></a>
Per session 144 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,056 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 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 $0.00144 $0.01056
Opus 5 $0.00072 $0.00528
Sonnet 5 $0.00029 $0.00211
Haiku 4.5 $0.00014 $0.00106

Measured 7d ago against content hash 4d90b6da2aea, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

poppify-schedule-optimizer 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.

skills/poppify-schedule-optimizer/SKILL.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scheduling for engagement & reach — the optimizer flow

Poppify tracks engagement per {day-of-week, hour} bucket from the account's REAL published posts (the same data behind the mobile calendar heatmap). publish_post exposes it — your job is to use it instead of scheduling blind.

Where the data comes from (so you can explain it honestly)

  • aggregates/time_slots per user: avg engagement rate, reach, likes/comments/saves/shares, post count, bestPostType, and a performance tier per day×hour bucket — recomputed from published-post metrics at every sync.
  • Ranking is confidence-weighted (ER × log2(samples+1) × reach factor), so one lucky post doesn't crown a slot. Two posts at 142% ER can still rank above ten posts at 2% — but say the sample size out loud when it's thin (postCount ≤ 3).
  • All hours are UTC. Convert to the user's timezone when presenting, and say which timezone you're showing.

The flow

  1. After get_result returns complete, call publish_post({apiKey, postId}) with no channelIds. The response carries BOTH availableChannels and recommendedSlots.
  2. Check recommendedSlots.basis:
    • "engagement_history" → these are the account's own numbers. Present the top 2–3 like: "Your audience engages best Fridays around 19:00 UTC (avg ER 4.2% across 11 posts). Next window: Fri Jul 10."
    • "platform_norms" → no history yet. Say so: "No engagement history on this account yet, so these are general platform norms (Tue–Thu midday). They'll become YOUR numbers as posts accrue metrics." Don't oversell norms as personalized.
  3. Let the user pick channels (never auto-select) and a time:
    • User says "best time" → scheduledAt: "best" (auto-picks the top upcoming slot; response's pickedFrom tells you which basis it used), or pass a specific slot's nextOccurrence verbatim.
    • User picks their own time → schedule it, then read slotAssessment in the response. If the verdict says historically weak, surface it with the better alternative — advisory, one sentence, never re-schedule without being asked.
  4. "Post now" (omit scheduledAt) is always legitimate for time-sensitive content. For evergreen content, gently note when a top slot is < ~48h away: waiting usually beats posting into a dead zone.

Read the full file on GitHub · 49 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. 7d ago First seen · 49 lines · 144 tokens per session scan A 4d90b6da2aea

Subscribe to this mod's changes

poppify-schedule-optimizer is a skill published in the GitHub repository Poppify/poppify-claude-plugin (0 stars, last pushed 1mo ago), licensed MIT. It adds 144 tokens to every session and 1,056 once invoked, about $0.0007 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.

Related

Other skills, from other repositories

lanshu-create-ai-presenter-video

Turn a topic or finished script plus an authorized adult presenter image into a complete, publish-ready AI presenter video. Use for new presenter videos and for continuing, revising, captioning, lip-sync repairing, or re-exporting an existing presenter-video job. Keep model and provider selection capability-based and…

cclank/lanshu-create-ai-presenter-video · 75 tokens

video-podcast-maker

Use when the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video…

Agents365-ai/video-podcast-maker · 214 tokens

video-podcast-maker-lite

Minimal personal narrated-video pipeline — a topic becomes a talking-head-free explainer MP4 (1080p or 4K) via script → Azure TTS (SSML) → Remotion. Use when the user wants a quick narrated video from a topic without the full video-podcast-maker machinery (no extra skills, no thumbnails/shorts/publish matrix). Do NOT…

Agents365-ai/video-podcast-maker · 96 tokens

video-podcast-maker-nano

Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints…

Agents365-ai/video-podcast-maker · 163 tokens

video-podcast-maker-factory

Use when the user wants to create their own custom narrated-video pipeline skill — a personal video-podcast-maker-xxx variant with their own TTS backend, visual style, and review gates. Forks the lite reference implementation. Do NOT trigger for making an actual video (use the user's existing variant) or for full…

Agents365-ai/video-podcast-maker · 79 tokens

travel-skill

A production guide for realistic travel-promotion videos that combine authorised real footage with AI-generated shots.

kangarooking/director-skills · 142 tokens