vid-batch

A workflow for turning recorded footage into finished vertical videos for Instagram Reels, with captions, opening hooks, and post text. It also supports creating new hook versions of published videos and sending items for review.

In plain words
What is it for?
Use it to process queued footage, review generated video versions, request redrafts, register approved videos, and send approved posts to Buffer, a social-media publishing service.
Why use it?
It organizes cutting, review, and publishing preparation without having the main workflow edit the footage itself. Separating the reviewer from the editing agents helps catch problems in the results.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/jared833/claude-code-hooks/vid-batch
Any agent
npx skills add jared833/claude-code-hooks --skill vid-batch
Clone the repo
git clone --depth 1 https://github.com/jared833/claude-code-hooks

Made for: Claude Code, Codex.

Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 20,929 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00082 $0.20929
Opus 5 $0.00041 $0.10465
Sonnet 5 $0.00016 $0.04186
Haiku 4.5 $0.00008 $0.02093

Measured yesterday against content hash 36a6eb5d066e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

vid-batch 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 yesterday.

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/vid-batch/SKILL.md · 1,314 lines

How it starts

The opening of the file, as written. The whole thing — 1,314 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cut a shoot into posts

Turn banked footage into finished, captioned, vertical videos waiting on one screen for approval. Runs four ways, same instructions every time: the nightly sweep, the "Draft everything in the queue" button on /intake, saving a decision on /video that approves or redrafts something, or Jared typing /vid-batch. Uploading footage does NOT start a pass. Only the nightly sweep runs step 0: it passes reconcile, every other trigger passes skip-reconcile, and one of the two words is always there.

Working directory is <HOME>\Desktop\Social Content. Read its CLAUDE.md first. It is the contract for this folder and everything below assumes it.

You are the ORCHESTRATOR and the REVIEWER. You do not cut.

Jared's call, 2026-07-29, and it is permanent: "You're new role forever and always will be orchestrator. You will shift from video editor to video reviewer."

The session that runs this skill dispatches the cutting to agents and then judges what comes back. It never writes an EDL itself. That is not a style preference, it is the only way the quality check exists at all: a session that made the cut cannot catch what is wrong with the cut, for the same reason the independent-review rule exists for code. Doing both is what produced the batch he rejected on 2026-07-29, where composeo-tool was ONE EDL row across 73 seconds and ponytail-tokens was one row across 42. One row means nothing was removed from the middle. Every false start, every stall and every silence he left in was still there, and it shipped to his review page as a finished draft.

His words: "I'm noticing a lot of dead space and you're not cutting out portions where I start and abruptly stop, or unnatural periods when I'm silent. We're not reviewing these for quality like we should and that's partly because you're doing all of the work."

How a pass runs now:

  1. You do the accounting, the reconciling, the pushing and the filing. Steps 0, 1, 8 and everything after.
  2. For each shoot to cut, dispatch a CUTTING AGENT. One agent per shoot, in parallel when there are several. Its whole job is steps 2 to 6: read the clips, group them, measure the boundaries, write post.json, render, and hand back the slug plus what it dropped.
  3. You then REVIEW the render against the bar below. Watch the output, do not read the JSON and call it reviewed. A cut that fails goes BACK to the agent with the specific defect named. It does not go to Jared.
  4. Only what passes your review gets registered at step 7.

Read the full file on GitHub · 1,314 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. yesterday First seen · 1,314 lines · 82 tokens per session scan A 36a6eb5d066e

Subscribe to this mod's changes

vid-batch is a skill published in the GitHub repository jared833/claude-code-hooks (2 stars, last pushed 13d ago), licensed MIT. It adds 82 tokens to every session and 20,929 once invoked, about $0.0004 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.

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