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Naming Convention

Community
mrpaidsocial
naming-convention

Name a batch of video deliverables so each filename says what the file actually is — subject, variant axis, and treatment — instead of `render-final-v3.mp4`. Use whenever more than two files are being handed over, and whenever someone says "rename these", "give these descriptive names", "I can't tell these apart", "name them so I know which is which", "put the product in the filename", "which one is the captioned version?", or is about to deliver a folder of variants, renders, exports, or cutdowns. Also use when naming a NEW batch before it ships, not just fixing an existing one — deciding the pattern up front is the cheap moment. Covers choosing the axes that vary, verifying the facts in a name before baking them into 40 files, keeping the subject map as data, and renaming idempotently without breaking a review canvas that already points at the old names.

Overview

Publishermrpaidsocial
Repositorymrpaidsocial-video-editor-agent
Skill namenaming-convention
Stars
114
Forks
34
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by mrpaidsocial on GitHub. Read the source before you install it.

Installation

Install the Naming Convention 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/mrpaidsocial/mrpaidsocial-video-editor-agent/tree/main/.claude/skills/naming-convention
  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/mrpaidsocial/mrpaidsocial-video-editor-agent.git /tmp/mrpaidsocial-video-editor-agent
mkdir -p .claude/skills
cp -r /tmp/mrpaidsocial-video-editor-agent/.claude/skills/naming-convention .claude/skills/naming-convention
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Naming Convention 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 Naming Convention 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 Naming Convention 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.

naming-convention — filenames that carry the work

A filename is the only metadata that survives every tool: Finder, an upload dialog, a Slack drop, a folder someone opens in six months. When a batch ships as ad-v5-hook-01.mp4 … ad-v5-hook-21.mp4, the person receiving it has to open all 21 to do anything, and the numbers mean nothing a week later.

The job of this skill is that someone can pick the right file without opening it.

The rule

The filename carries every axis that varies across the batch, plus a stable sort key.

Anything constant across the batch belongs in the folder name, not in 40 filenames. Anything that varies and is not in the name is a thing the user has to open files to discover.

<index>-<subject>-<descriptor>-<treatment>.<ext>
  • index — zero-padded, first, so disk order matches whatever ordering the review used (canvas cards, a shot list, the order they were cut). This is worth a lot: "card 7" on the review page and the 7th file in the folder should be the same thing.
  • subject — what the thing is about. The product, the client, the topic.
  • descriptor — the axis that distinguishes near-duplicates: style, format, angle, take.
  • treatment — what was done to it: body-edited / body-unedited, captioned / clean, 9x16 / 1x1, draft / approved.

Worked example, from a hook A/B batch:

hook-07-lookalike-cinematic-body-unedited.mp4
hook-07-lookalike-cinematic-body-edited.mp4
     │  │          │         └ treatment: which body cut (captioned vs clean)
     │  │          └ descriptor: the ad style
     │  └ subject: the product being advertised
     └ index: matches card 7 on the review canvas

Two files, same hook, and you can tell them apart in a Finder list without a preview.

Spell the axis out when a bare word is ambiguous. …-unedited.mp4 reads as though the hook is unedited; …-body-unedited.mp4 cannot be misread. Length is cheap; a wrong reading is not.

Verify the facts before you bake them into 40 files

This is the part that actually costs time when skipped.

Labels inherited from an upstream step are evidence, not truth. A cut list, a shot list, a client spreadsheet, an earlier session's JSON — each was written by someone solving a different problem, and mislabels survive because nothing downstream ever checks them.

Two real examples from one batch of 21, both wrong in the upstream cut list:

  • A hook titled "sparkling water" was actually an ad for a cap. The generation had two references: a reference image (the cap — the product) and a reference video (a sparkling-water ad — the style). The upstream title had named the style reference.
  • A hook titled "Gut Check" was actually the Thumb-Stopper protein bar. Gut Check was a different hook's product, one slot away.

Both would have shipped into four filenames and a review canvas.

How to check cheaply, in order:

  1. Find where the source states it. In generated-ad work the tool's own prompt panel shows the prompt and a legible reference-image thumbnail a few seconds into most clips — that settles it outright. The equivalent elsewhere: a slate, a title card, a project file, an order form.
  2. Look at the artifact itself. Pull frames from where the subject is on screen. Be aware this is the harder path for close-up product work: extreme close-ups crop wordmarks, and picture-in-picture bubbles cover them. Four escalating crops of one bar wrapper still only read "THUMB…"; the prompt panel gave the full name in one frame.
  3. Cross-check the transcript. Someone usually says the product name out loud.

Spend the ten minutes. Renaming is cheap; a wrong product name on a client deliverable is not, and neither is the credibility hit when they spot it.

When you do correct an inherited label, say so explicitly in the handover — the user may have that wrong label in their own notes, and a silent fix reads as an inconsistency later.

Slug rules

Keep names shell-safe, URL-safe, and sortable:

  • lowercase, hyphen-separated, ASCII; no spaces, apostrophes, &, or parentheses
  • zero-pad indexes (07, not 7) so lexical sort matches numeric sort
  • prefer the name a human would say ("hyperfocus", "gut-check") over an internal code
  • keep the subject slug stable across batches — same product, same slug, so a later ls *lookalike* finds every one
  • resist stuffing: if the name needs four descriptors you probably have two batches, and the extra axis belongs in the folder

Keep the map as data

Put the per-item facts in a small JSON file next to the batch, not inside a rename script:

json
{"_note": "read off the tool's prompt panel, NOT the upstream cut list — two of those were wrong",
 "items": {
   "1": {"subject": "cap",        "style": "cinematic"},
   "5": {"subject": "thumb-stopper", "style": "asmr"}
 }}

Batches get relabelled. When the mapping is data, a correction is a one-line edit and a re-run; baked into a script it is a rewrite; left in a chat transcript it is gone. Include a note about where the facts came from — the next person will wonder whether to trust it.

Rename safely

bash
python3 scripts/rename_batch.py --dir variants/ --map subjects.json \
    --pattern "hook-{index:02d}-{subject}-{style}-body-{treatment}" \
    --set treatment=unedited --manifest variants.json          # dry run
python3 scripts/rename_batch.py ... --apply                    # commit

The script refuses to run rather than half-finish: it checks every source exists, that no target collides with a different file, and that the pattern actually produces unique names. It is idempotent — a second run reports "already named" instead of double-renaming — and it updates the build manifest so later steps still find the files.

Four things to get right around a rename:

  • Dry run first, always. The output is the review; read it before --apply.
  • Update the manifest (variants.json or equivalent), or the next tool in the chain looks for files that no longer exist.
  • Republish the review canvas if the batch is already on one, and print the filename on each card so canvas ↔ disk is a direct match. A reviewer saying "number 7 is the one" and a folder where 7 is something else is exactly the confusion this skill exists to remove.
  • Do not rename a file someone already has. If it has been delivered and downloaded, ship the new name as a new copy or tell them plainly that the names changed. The repo rule that delivered files are never overwritten applies to their names too.

House patterns

BatchPattern
hook variants (one body × N hooks)hook-NN-<product>-<style>-body-<edited|unedited>
ratio cutdowns of one edit<project>-<ratio> e.g. acme-9x16
revision roundskeep the name, bump a version segment; never overwrite a delivered file
stills / frame grabs for review<project>-<timecode> e.g. arcads-0m17s2

If a new batch does not fit these, choose the axes first and write them down in the handover before renaming 40 files — the pattern is much cheaper to change before it exists.

Files

PathWhat it is
scripts/rename_batch.pypattern + map → renames; dry run by default, idempotent, refuses to clobber or collide
assets/subjects.example.jsona real 21-item map, including the note about where the facts came from

Related skills: hook-variations (produces the batch this most often names), video-review-canvas (where the names need to match the cards).

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 Naming Convention AI skill do?

Name a batch of video deliverables so each filename says what the file actually is — subject, variant axis, and treatment — instead of `render-final-v3.mp4`. Use whenever more than two files are being handed over, and whenever someone says "rename these", "give these descriptive names", "I can't tell these apart", "name them so I know which is which", "put the product in the filename", "which one is the captioned version?", or is about to deliver a folder of variants, renders, exports, or cutdowns. Also use when naming a NEW batch before it ships, not just fixing an existing one — deciding...

Why use Naming Convention on TypingMind?

Because you install it once and use it with any model. Naming Convention 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 Naming Convention in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mrpaidsocial/mrpaidsocial-video-editor-agent/tree/main/.claude/skills/naming-convention. 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 Naming Convention?

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 Naming Convention?

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

Is the Naming Convention AI skill free?

It is published on GitHub by mrpaidsocial. Check the repository for licensing terms. 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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