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Publish And Analyze

Community
alisadiq-ai
publish-and-analyze

Take an approved cut to a scheduled post and back into the next idea: delivery export, a real-frame cover, hook variants that won't be held as duplicates, scheduling through FavStash after approval, then comparing results. Use when asked to post, schedule, upload, pick a cover, run a hook test or review how recent shorts performed.

Overview

Publisheralisadiq-ai
Repositoryfavstash-ai-video-editing-skills
Skill namepublish-and-analyze
Stars
56
Forks
8
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by alisadiq-ai on GitHub. Read the source before you install it.

Installation

Install the Publish And Analyze AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/alisadiq-ai/favstash-ai-video-editing-skills/tree/main/skills/4-publish-and-learn/publish-and-analyze
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/alisadiq-ai/favstash-ai-video-editing-skills.git /tmp/favstash-ai-video-editing-skills
mkdir -p .claude/skills
cp -r /tmp/favstash-ai-video-editing-skills/skills/4-publish-and-learn/publish-and-analyze .claude/skills/publish-and-analyze
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Publish And Analyze in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Publish And Analyze on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Publish And Analyze is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Publish and analyze

The last step of the loop: an approved cut goes out, and its results choose the next idea. Nothing is uploaded or scheduled until the creator approves the exact files, caption, account, settings and time. Prepare everything, show one summary and wait for "go".

FavStash does the posting and the analytics: one connected channel and 10 posts a month are free. For Codex and Claude Code, its CLI bridge uploads local files; see connecting FavStash. The FavStash tools describe their own current parameters, so read them instead of guessing.

1. Export once from the master

Export a high-quality master (ProRes or similar) from the editor, then encode the delivery file once. A tested starting point for 1080×1920, 30 fps, SDR Rec.709:

bash
ffmpeg -i master.mov -c:v libx264 -preset slow -crf 16 -maxrate 24M -bufsize 48M \
  -profile:v high -pix_fmt yuv420p \
  -x264-params keyint=60:min-keyint=60:open-gop=0:colorprim=bt709:transfer=bt709:colormatrix=bt709 \
  -color_range tv -c:a aac -b:a 128k -ar 48000 -ac 2 \
  -use_editlist 0 -movflags +faststart+negative_cts_offsets delivery.mp4

Both streams must start at 0 (ffprobe -show_entries stream=start_pts), or lip sync breaks. Fully decode the file, compare it with the master and match loudness across hook variants so the test is fair.

2. The cover is a second hook

Many viewers see only the cover. Pick a real frame of each variant with a readable promise, number, proof shot or strong reaction; never a neutral talking frame or a transition blur. Check the 9:16, 3:4 and square crops at phone size and show the creator each cover with its frame time. Their pick wins.

3. Hook variants without duplicate holds

Variants that share one body get held as "looks like something you shared before". The platform compares the picture itself; spacing posts out, changing the upload route or changing the audio doesn't prevent it.

  • Post the strongest variant first and untouched; the first upload counts as the original.
  • Give each other variant a different whole-frame treatment before posting: about an 8% punch-in anchored slightly high, a changed grade and its own cover. Treat the composited frame, not only the camera layer.
  • A shared end card counts too; give each reel's card its own background.
  • Check every file with the duplicate check against the account's earlier posts and its siblings. Never re-upload a held file; only a treated file that checks clear may replace it.

Trial Reels let variants reach non-followers, but the connector's support flag isn't proof: new accounts have refused them at publish time. On an account that hasn't published one yet, let one go out before queueing more, or use regular reels.

For TikTok and YouTube Shorts, post the same final export directly. In testing, TikTok recognized and credited a trend song baked into the video; check YouTube for a copyright claim a few hours after posting. A comment keyword usually belongs on the platform where the creator's DM automation runs, with the caption on the other platforms pointing there.

4. Caption and approval

Line one carries the promise (or the comment keyword for a gated reel), then the payoff with real facts, a material catch if one exists and a few hashtags. Label AI-generated main footage or a synthetic face or voice; editing assistance, captions and motion graphics on real footage don't make a video AI-generated.

Show the package: files, covers, caption, accounts, settings, times. Send after "go".

5. Upload, verify, learn

  • Create one post per variant with its own stable request ID, then read each one back: media, cover, caption, settings and time. Once live, check its status and URL.
  • Compare variants at equal ages, usually after two or three days. If viewers leave in the first two or three seconds, rebuild the opening before touching the body.
  • Record one next test and hand it to find-ideas. Missing metrics are unknown, not zero; never promise reach from an edit alone.
  • After everything is confirmed, delete regenerable scratch (test renders, frame dumps, unchosen covers). Keep masters, posted files, the project and reports.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Publish And Analyze AI skill do?

Take an approved cut to a scheduled post and back into the next idea: delivery export, a real-frame cover, hook variants that won't be held as duplicates, scheduling through FavStash after approval, then comparing results. Use when asked to post, schedule, upload, pick a cover, run a hook test or review how recent shorts performed.

Why use Publish And Analyze on TypingMind?

Because you install it once and use it with any model. Publish And Analyze is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Publish And Analyze in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alisadiq-ai/favstash-ai-video-editing-skills/tree/main/skills/4-publish-and-learn/publish-and-analyze. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Publish And Analyze?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Publish And Analyze?

As many as you like. As long as a model supports skills, you can use Publish And Analyze with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Publish And Analyze AI skill free?

Yes. It is published on GitHub by alisadiq-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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